[v5] Delete left traces of feature extractor

src/transformers/models/blip/processing_blip.py

@@ -60,7 +60,6 @@ class BlipProcessor(ProcessorMixin):
     def __init__(self, image_processor, tokenizer, **kwargs):
         tokenizer.return_token_type_ids = False
         super().__init__(image_processor, tokenizer)
-        self.current_processor = self.image_processor
 
     def __call__(
         self,

src/transformers/models/blip_2/processing_blip_2.py

@@ -66,7 +66,6 @@ class Blip2Processor(ProcessorMixin):
 
     def __init__(self, image_processor, tokenizer, num_query_tokens=None, **kwargs):
         tokenizer.return_token_type_ids = False
-        self.current_processor = image_processor
         if not hasattr(tokenizer, "image_token"):
             self.image_token = AddedToken("<image>", normalized=False, special=True)
             tokenizer.add_tokens([self.image_token], special_tokens=True)

src/transformers/models/chinese_clip/processing_chinese_clip.py

@@ -16,8 +16,6 @@
 Image/Text processor class for Chinese-CLIP
 """
 
-import warnings
-
 from ...processing_utils import ProcessorMixin
 
 
@@ -41,27 +39,7 @@ class ChineseCLIPProcessor(ProcessorMixin):
     tokenizer_class = ("BertTokenizer", "BertTokenizerFast")
 
     def __init__(self, image_processor=None, tokenizer=None, **kwargs):
-        feature_extractor = None
-        if "feature_extractor" in kwargs:
-            warnings.warn(
-                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
-                " instead.",
-                FutureWarning,
-            )
-            feature_extractor = kwargs.pop("feature_extractor")
-
-        image_processor = image_processor if image_processor is not None else feature_extractor
-
         super().__init__(image_processor, tokenizer)
-        self.current_processor = self.image_processor
-
-    @property
-    def feature_extractor_class(self):
-        warnings.warn(
-            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
-            FutureWarning,
-        )
-        return self.image_processor_class
 
 
 __all__ = ["ChineseCLIPProcessor"]

src/transformers/models/clip/processing_clip.py

@@ -16,8 +16,6 @@
 Image/Text processor class for CLIP
 """
 
-import warnings
-
 from ...processing_utils import ProcessorMixin
 
 
@@ -40,34 +38,7 @@ class CLIPProcessor(ProcessorMixin):
     tokenizer_class = "AutoTokenizer"
 
     def __init__(self, image_processor=None, tokenizer=None, **kwargs):
-        feature_extractor = None
-        if "feature_extractor" in kwargs:
-            warnings.warn(
-                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
-                " instead.",
-                FutureWarning,
-            )
-            feature_extractor = kwargs.pop("feature_extractor")
-
-        image_processor = image_processor if image_processor is not None else feature_extractor
-
         super().__init__(image_processor, tokenizer)
 
-    @property
-    def feature_extractor_class(self):
-        warnings.warn(
-            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
-            FutureWarning,
-        )
-        return self.image_processor_class
-
-    @property
-    def feature_extractor(self):
-        warnings.warn(
-            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
-            FutureWarning,
-        )
-        return self.image_processor
-
 
 __all__ = ["CLIPProcessor"]

src/transformers/models/clipseg/processing_clipseg.py

@@ -16,8 +16,6 @@
 Image/Text processor class for CLIPSeg
 """
 
-import warnings
-
 from ...processing_utils import ProcessorMixin
 from ...tokenization_utils_base import BatchEncoding
 
@@ -41,17 +39,6 @@ class CLIPSegProcessor(ProcessorMixin):
     tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast")
 
     def __init__(self, image_processor=None, tokenizer=None, **kwargs):
-        feature_extractor = None
-        if "feature_extractor" in kwargs:
-            warnings.warn(
-                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
-                " instead.",
-                FutureWarning,
-            )
-            feature_extractor = kwargs.pop("feature_extractor")
-
-        image_processor = image_processor if image_processor is not None else feature_extractor
-
         super().__init__(image_processor, tokenizer)
 
     def __call__(self, text=None, images=None, visual_prompt=None, return_tensors=None, **kwargs):
@@ -124,21 +111,5 @@ def __call__(self, text=None, images=None, visual_prompt=None, return_tensors=No
         else:
             return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
 
-    @property
-    def feature_extractor_class(self):
-        warnings.warn(
-            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
-            FutureWarning,
-        )
-        return self.image_processor_class
-
-    @property
-    def feature_extractor(self):
-        warnings.warn(
-            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
-            FutureWarning,
-        )
-        return self.image_processor
-
 
 __all__ = ["CLIPSegProcessor"]

src/transformers/models/donut/processing_donut.py

@@ -17,8 +17,6 @@
 """
 
 import re
-import warnings
-from contextlib import contextmanager
 from typing import Optional, Union
 
 from ...image_utils import ImageInput
@@ -55,20 +53,7 @@ class DonutProcessor(ProcessorMixin):
     tokenizer_class = "AutoTokenizer"
 
     def __init__(self, image_processor=None, tokenizer=None, **kwargs):
-        feature_extractor = None
-        if "feature_extractor" in kwargs:
-            warnings.warn(
-                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
-                " instead.",
-                FutureWarning,
-            )
-            feature_extractor = kwargs.pop("feature_extractor")
-
-        image_processor = image_processor if image_processor is not None else feature_extractor
-
         super().__init__(image_processor, tokenizer)
-        self.current_processor = self.image_processor
-        self._in_target_context_manager = False
 
     def __call__(
         self,
@@ -82,9 +67,6 @@ def __call__(
         [`~DonutProcessor.as_target_processor`] this method forwards all its arguments to DonutTokenizer's
         [`~DonutTokenizer.__call__`]. Please refer to the docstring of the above two methods for more information.
         """
-        if self._in_target_context_manager:
-            return self.current_processor(images, text, **kwargs)
-
         if images is None and text is None:
             raise ValueError("You need to specify either an `images` or `text` input to process.")
 
@@ -116,22 +98,6 @@ def model_input_names(self):
 
         return list(image_processor_input_names + ["input_ids", "labels"])
 
-    @contextmanager
-    def as_target_processor(self):
-        """
-        Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning TrOCR.
-        """
-        warnings.warn(
-            "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
-            "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
-            "your images inputs, or in a separate call."
-        )
-        self._in_target_context_manager = True
-        self.current_processor = self.tokenizer
-        yield
-        self.current_processor = self.image_processor
-        self._in_target_context_manager = False
-
     def token2json(self, tokens, is_inner_value=False, added_vocab=None):
         """
         Convert a (generated) token sequence into an ordered JSON format.
@@ -190,21 +156,5 @@ def token2json(self, tokens, is_inner_value=False, added_vocab=None):
         else:
             return [] if is_inner_value else {"text_sequence": tokens}
 
-    @property
-    def feature_extractor_class(self):
-        warnings.warn(
-            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
-            FutureWarning,
-        )
-        return self.image_processor_class
-
-    @property
-    def feature_extractor(self):
-        warnings.warn(
-            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
-            FutureWarning,
-        )
-        return self.image_processor
-
 
 __all__ = ["DonutProcessor"]

src/transformers/models/flava/processing_flava.py

@@ -16,8 +16,6 @@
 Image/Text processor class for FLAVA
 """
 
-import warnings
-
 from ...processing_utils import ProcessorMixin
 
 
@@ -38,34 +36,7 @@ class FlavaProcessor(ProcessorMixin):
     tokenizer_class = ("BertTokenizer", "BertTokenizerFast")
 
     def __init__(self, image_processor=None, tokenizer=None, **kwargs):
-        feature_extractor = None
-        if "feature_extractor" in kwargs:
-            warnings.warn(
-                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
-                " instead.",
-                FutureWarning,
-            )
-            feature_extractor = kwargs.pop("feature_extractor")
-
-        image_processor = image_processor if image_processor is not None else feature_extractor
         super().__init__(image_processor, tokenizer)
-        self.current_processor = self.image_processor
-
-    @property
-    def feature_extractor_class(self):
-        warnings.warn(
-            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
-            FutureWarning,
-        )
-        return self.image_processor_class
-
-    @property
-    def feature_extractor(self):
-        warnings.warn(
-            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
-            FutureWarning,
-        )
-        return self.image_processor
 
 
 __all__ = ["FlavaProcessor"]

src/transformers/models/fuyu/processing_fuyu.py

@@ -531,7 +531,6 @@ def __call__(
 
         if text is not None and images is None:
             logger.warning("You are processing a text with no associated image. Make sure it is intended.")
-            self.current_processor = self.tokenizer
             text_encoding = self.tokenizer(text, **output_kwargs["text_kwargs"])
             return text_encoding
 

src/transformers/models/git/processing_git.py

@@ -39,7 +39,6 @@ class GitProcessor(ProcessorMixin):
 
     def __init__(self, image_processor, tokenizer):
         super().__init__(image_processor, tokenizer)
-        self.current_processor = self.image_processor
 
 
 __all__ = ["GitProcessor"]

src/transformers/models/idefics/processing_idefics.py

@@ -159,7 +159,6 @@ class IdeficsProcessor(ProcessorMixin):
 
     def __init__(self, image_processor, tokenizer=None, image_size=224, add_end_of_utterance_token=None, **kwargs):
         super().__init__(image_processor, tokenizer)
-        self.current_processor = self.image_processor
         self.image_token_id = (
             tokenizer.image_token_id
             if hasattr(tokenizer, "image_token")

src/transformers/models/layoutlmv2/processing_layoutlmv2.py

@@ -16,7 +16,6 @@
 Processor class for LayoutLMv2.
 """
 
-import warnings
 from typing import Optional, Union
 
 from ...processing_utils import ProcessorMixin
@@ -49,17 +48,6 @@ class LayoutLMv2Processor(ProcessorMixin):
     tokenizer_class = ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")
 
     def __init__(self, image_processor=None, tokenizer=None, **kwargs):
-        feature_extractor = None
-        if "feature_extractor" in kwargs:
-            warnings.warn(
-                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
-                " instead.",
-                FutureWarning,
-            )
-            feature_extractor = kwargs.pop("feature_extractor")
-
-        image_processor = image_processor if image_processor is not None else feature_extractor
-
         super().__init__(image_processor, tokenizer)
 
     def __call__(
@@ -166,21 +154,5 @@ def get_overflowing_images(self, images, overflow_to_sample_mapping):
     def model_input_names(self):
         return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
 
-    @property
-    def feature_extractor_class(self):
-        warnings.warn(
-            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
-            FutureWarning,
-        )
-        return self.image_processor_class
-
-    @property
-    def feature_extractor(self):
-        warnings.warn(
-            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
-            FutureWarning,
-        )
-        return self.image_processor
-
 
 __all__ = ["LayoutLMv2Processor"]

src/transformers/models/layoutlmv3/processing_layoutlmv3.py

@@ -16,7 +16,6 @@
 Processor class for LayoutLMv3.
 """
 
-import warnings
 from typing import Optional, Union
 
 from ...processing_utils import ProcessorMixin
@@ -49,17 +48,6 @@ class LayoutLMv3Processor(ProcessorMixin):
     tokenizer_class = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")
 
     def __init__(self, image_processor=None, tokenizer=None, **kwargs):
-        feature_extractor = None
-        if "feature_extractor" in kwargs:
-            warnings.warn(
-                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
-                " instead.",
-                FutureWarning,
-            )
-            feature_extractor = kwargs.pop("feature_extractor")
-
-        image_processor = image_processor if image_processor is not None else feature_extractor
-
         super().__init__(image_processor, tokenizer)
 
     def __call__(
@@ -164,21 +152,5 @@ def get_overflowing_images(self, images, overflow_to_sample_mapping):
     def model_input_names(self):
         return ["input_ids", "bbox", "attention_mask", "pixel_values"]
 
-    @property
-    def feature_extractor_class(self):
-        warnings.warn(
-            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
-            FutureWarning,
-        )
-        return self.image_processor_class
-
-    @property
-    def feature_extractor(self):
-        warnings.warn(
-            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
-            FutureWarning,
-        )
-        return self.image_processor
-
 
 __all__ = ["LayoutLMv3Processor"]