
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class   a_    (       unittest.TestCase				):



       """simple docstring"""






       def      __lowerCAmelCase			(						self					) ->Any:
           SCREAMING_SNAKE_CASE							:							str								=      tempfile.mkdtemp()

           SCREAMING_SNAKE_CASE							:							str								=      BlipImageProcessor()
           SCREAMING_SNAKE_CASE							:							List[Any]								=      GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''					)

           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      BlipaProcessor(_a							,    _a					)

           processor.save_pretrained(self.tmpdirname					)






       def      __lowerCAmelCase			(						self							,    **_lowerCamelCase					) ->List[Any]:
           return AutoProcessor.from_pretrained(self.tmpdirname							,    **_a					).tokenizer






       def      __lowerCAmelCase			(						self							,    **_lowerCamelCase					) ->Optional[int]:
           return AutoProcessor.from_pretrained(self.tmpdirname							,    **_a					).image_processor






       def      __lowerCAmelCase			(						self					) ->Any:
           shutil.rmtree(self.tmpdirname					)






       def      __lowerCAmelCase			(						self					) ->Any:
           SCREAMING_SNAKE_CASE							:							List[str]								=      [np.random.randint(255							,    size=(3, 30, 400)							,    dtype=np.uinta					)]

           SCREAMING_SNAKE_CASE							:							List[str]								=      [Image.fromarray(np.moveaxis(_a							,    0							,    -1					)					) for x in image_inputs]

           return image_inputs






       def      __lowerCAmelCase			(						self					) ->List[Any]:
           SCREAMING_SNAKE_CASE							:							List[Any]								=      BlipaProcessor(tokenizer=self.get_tokenizer()							,    image_processor=self.get_image_processor()					)
           processor.save_pretrained(self.tmpdirname					)

           SCREAMING_SNAKE_CASE							:							Dict								=      self.get_tokenizer(bos_token='''(BOS)'''							,    eos_token='''(EOS)'''					)
           SCREAMING_SNAKE_CASE							:							Optional[int]								=      self.get_image_processor(do_normalize=_a							,    padding_value=1.0					)

           SCREAMING_SNAKE_CASE							:							List[Any]								=      BlipaProcessor.from_pretrained(
               self.tmpdirname							,    bos_token='''(BOS)'''							,    eos_token='''(EOS)'''							,    do_normalize=_a							,    padding_value=1.0					)

           self.assertEqual(processor.tokenizer.get_vocab()							,    tokenizer_add_kwargs.get_vocab()					)
           self.assertIsInstance(processor.tokenizer							,    _a					)

           self.assertEqual(processor.image_processor.to_json_string()							,    image_processor_add_kwargs.to_json_string()					)
           self.assertIsInstance(processor.image_processor							,    _a					)






       def      __lowerCAmelCase			(						self					) ->str:
           SCREAMING_SNAKE_CASE							:							Any								=      self.get_image_processor()
           SCREAMING_SNAKE_CASE							:							Optional[Any]								=      self.get_tokenizer()

           SCREAMING_SNAKE_CASE							:							List[Any]								=      BlipaProcessor(tokenizer=_a							,    image_processor=_a					)

           SCREAMING_SNAKE_CASE							:							List[str]								=      self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE							:							Optional[Any]								=      image_processor(_a							,    return_tensors='''np'''					)
           SCREAMING_SNAKE_CASE							:							Optional[int]								=      processor(images=_a							,    return_tensors='''np'''					)

           for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()							,    input_processor[key].sum()							,    delta=1e-2					)






       def      __lowerCAmelCase			(						self					) ->List[Any]:
           SCREAMING_SNAKE_CASE							:							Tuple								=      self.get_image_processor()
           SCREAMING_SNAKE_CASE							:							List[str]								=      self.get_tokenizer()

           SCREAMING_SNAKE_CASE							:							str								=      BlipaProcessor(tokenizer=_a							,    image_processor=_a					)

           SCREAMING_SNAKE_CASE							:							Optional[Any]								=      """lower newer"""

           SCREAMING_SNAKE_CASE							:							Tuple								=      processor(text=_a					)

           SCREAMING_SNAKE_CASE							:							Optional[int]								=      tokenizer(_a							,    return_token_type_ids=_a					)

           for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]							,    encoded_processor[key]					)






       def      __lowerCAmelCase			(						self					) ->Optional[Any]:
           SCREAMING_SNAKE_CASE							:							str								=      self.get_image_processor()
           SCREAMING_SNAKE_CASE							:							List[str]								=      self.get_tokenizer()

           SCREAMING_SNAKE_CASE							:							Dict								=      BlipaProcessor(tokenizer=_a							,    image_processor=_a					)

           SCREAMING_SNAKE_CASE							:							int								=      """lower newer"""
           SCREAMING_SNAKE_CASE							:							List[str]								=      self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE							:							Any								=      processor(text=_a							,    images=_a					)

           self.assertListEqual(list(inputs.keys()					)							,    ['''pixel_values''', '''input_ids''', '''attention_mask''']					)

           # test if it raises when no input is passed
           with pytest.raises(_a					):
               processor()






       def      __lowerCAmelCase			(						self					) ->Optional[Any]:
           SCREAMING_SNAKE_CASE							:							List[Any]								=      self.get_image_processor()
           SCREAMING_SNAKE_CASE							:							Optional[int]								=      self.get_tokenizer()

           SCREAMING_SNAKE_CASE							:							str								=      BlipaProcessor(tokenizer=_a							,    image_processor=_a					)

           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           SCREAMING_SNAKE_CASE							:							Tuple								=      processor.batch_decode(_a					)
           SCREAMING_SNAKE_CASE							:							Optional[Any]								=      tokenizer.batch_decode(_a					)

           self.assertListEqual(_a							,    _a					)






       def      __lowerCAmelCase			(						self					) ->Any:
           SCREAMING_SNAKE_CASE							:							List[str]								=      self.get_image_processor()
           SCREAMING_SNAKE_CASE							:							Any								=      self.get_tokenizer()

           SCREAMING_SNAKE_CASE							:							Optional[Any]								=      BlipaProcessor(tokenizer=_a							,    image_processor=_a					)

           SCREAMING_SNAKE_CASE							:							Tuple								=      """lower newer"""
           SCREAMING_SNAKE_CASE							:							str								=      self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE							:							Any								=      processor(text=_a							,    images=_a					)

           # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
           self.assertListEqual(list(inputs.keys()					)							,    ['''pixel_values''', '''input_ids''', '''attention_mask''']					)






import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       OrderedDict(
    [
        ("audio-spectrogram-transformer", "ASTFeatureExtractor"),
        ("beit", "BeitFeatureExtractor"),
        ("chinese_clip", "ChineseCLIPFeatureExtractor"),
        ("clap", "ClapFeatureExtractor"),
        ("clip", "CLIPFeatureExtractor"),
        ("clipseg", "ViTFeatureExtractor"),
        ("conditional_detr", "ConditionalDetrFeatureExtractor"),
        ("convnext", "ConvNextFeatureExtractor"),
        ("cvt", "ConvNextFeatureExtractor"),
        ("data2vec-audio", "Wav2Vec2FeatureExtractor"),
        ("data2vec-vision", "BeitFeatureExtractor"),
        ("deformable_detr", "DeformableDetrFeatureExtractor"),
        ("deit", "DeiTFeatureExtractor"),
        ("detr", "DetrFeatureExtractor"),
        ("dinat", "ViTFeatureExtractor"),
        ("donut-swin", "DonutFeatureExtractor"),
        ("dpt", "DPTFeatureExtractor"),
        ("encodec", "EncodecFeatureExtractor"),
        ("flava", "FlavaFeatureExtractor"),
        ("glpn", "GLPNFeatureExtractor"),
        ("groupvit", "CLIPFeatureExtractor"),
        ("hubert", "Wav2Vec2FeatureExtractor"),
        ("imagegpt", "ImageGPTFeatureExtractor"),
        ("layoutlmv2", "LayoutLMv2FeatureExtractor"),
        ("layoutlmv3", "LayoutLMv3FeatureExtractor"),
        ("levit", "LevitFeatureExtractor"),
        ("maskformer", "MaskFormerFeatureExtractor"),
        ("mctct", "MCTCTFeatureExtractor"),
        ("mobilenet_v1", "MobileNetV1FeatureExtractor"),
        ("mobilenet_v2", "MobileNetV2FeatureExtractor"),
        ("mobilevit", "MobileViTFeatureExtractor"),
        ("nat", "ViTFeatureExtractor"),
        ("owlvit", "OwlViTFeatureExtractor"),
        ("perceiver", "PerceiverFeatureExtractor"),
        ("poolformer", "PoolFormerFeatureExtractor"),
        ("regnet", "ConvNextFeatureExtractor"),
        ("resnet", "ConvNextFeatureExtractor"),
        ("segformer", "SegformerFeatureExtractor"),
        ("sew", "Wav2Vec2FeatureExtractor"),
        ("sew-d", "Wav2Vec2FeatureExtractor"),
        ("speech_to_text", "Speech2TextFeatureExtractor"),
        ("speecht5", "SpeechT5FeatureExtractor"),
        ("swiftformer", "ViTFeatureExtractor"),
        ("swin", "ViTFeatureExtractor"),
        ("swinv2", "ViTFeatureExtractor"),
        ("table-transformer", "DetrFeatureExtractor"),
        ("timesformer", "VideoMAEFeatureExtractor"),
        ("tvlt", "TvltFeatureExtractor"),
        ("unispeech", "Wav2Vec2FeatureExtractor"),
        ("unispeech-sat", "Wav2Vec2FeatureExtractor"),
        ("van", "ConvNextFeatureExtractor"),
        ("videomae", "VideoMAEFeatureExtractor"),
        ("vilt", "ViltFeatureExtractor"),
        ("vit", "ViTFeatureExtractor"),
        ("vit_mae", "ViTFeatureExtractor"),
        ("vit_msn", "ViTFeatureExtractor"),
        ("wav2vec2", "Wav2Vec2FeatureExtractor"),
        ("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
        ("wavlm", "Wav2Vec2FeatureExtractor"),
        ("whisper", "WhisperFeatureExtractor"),
        ("xclip", "CLIPFeatureExtractor"),
        ("yolos", "YolosFeatureExtractor"),
    ]
)

_snake_case										=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)







def 				lowerCAmelCase_	(     snake_case_     ):
      for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  _A							:     List[str]			=						model_type_to_module_name(snake_case_     )

                  _A							:     List[Any]			=						importlib.import_module(f'''.{module_name}''',"""transformers.models"""     )
                  try:
                        return getattr(snake_case_,snake_case_     )
                  except AttributeError:
                        continue

      for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
            if getattr(snake_case_,"""__name__""",snake_case_     ) == class_name:
                  return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
      _A							:     List[Any]			=						importlib.import_module("""transformers"""     )
      if hasattr(snake_case_,snake_case_     ):
            return getattr(snake_case_,snake_case_     )

      return None







def 				lowerCAmelCase_	(     snake_case_,snake_case_ = None,snake_case_ = False,snake_case_ = False,snake_case_ = None,snake_case_ = None,snake_case_ = None,snake_case_ = False,**snake_case_,):
      _A							:     Optional[int]			=						get_file_from_repo(
          snake_case_,snake_case_,cache_dir=snake_case_,force_download=snake_case_,resume_download=snake_case_,proxies=snake_case_,use_auth_token=snake_case_,revision=snake_case_,local_files_only=snake_case_,)
      if resolved_config_file is None:
            logger.info(
                """Could not locate the feature extractor configuration file, will try to use the model config instead."""     )
            return {}

      with open(snake_case_,encoding="""utf-8"""     ) as reader:
            return json.load(snake_case_     )







class 						lowercase							:

     def __init__(  self				)						->							List[Any]:
           raise EnvironmentError(
               """AutoFeatureExtractor is designed to be instantiated """
               """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."""				)

     @classmethod
     @replace_list_option_in_docstrings(_a				)
     def 					a__	(  cls		,     _a		,     **_a				)						->							Any:
           _A							:     Tuple			=						kwargs.pop("""config"""		,     _a				)
           _A							:     Tuple			=						kwargs.pop("""trust_remote_code"""		,     _a				)
           _A							:     List[Any]			=						True

           _A			,   _A							:     Tuple			=						FeatureExtractionMixin.get_feature_extractor_dict(_a		,     **_a				)
           _A							:     Tuple			=						config_dict.get("""feature_extractor_type"""		,     _a				)
           _A							:     int			=						None
           if "AutoFeatureExtractor" in config_dict.get("""auto_map"""		,     {}				):
                 _A							:     Optional[int]			=						config_dict["""auto_map"""]["""AutoFeatureExtractor"""]

           # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
           if feature_extractor_class is None and feature_extractor_auto_map is None:
                 if not isinstance(_a		,     _a				):
                       _A							:     int			=						AutoConfig.from_pretrained(_a		,     **_a				)
                 # It could be in `config.feature_extractor_type``
                 _A							:     Optional[int]			=						getattr(_a		,     """feature_extractor_type"""		,     _a				)
                 if hasattr(_a		,     """auto_map"""				) and "AutoFeatureExtractor" in config.auto_map:
                       _A							:     Tuple			=						config.auto_map["""AutoFeatureExtractor"""]

           if feature_extractor_class is not None:
                 _A							:     Optional[Any]			=						feature_extractor_class_from_name(_a				)

           _A							:     List[Any]			=						feature_extractor_auto_map is not None
           _A							:     Union[str, Any]			=						feature_extractor_class is not None or type(_a				) in FEATURE_EXTRACTOR_MAPPING
           _A							:     Optional[int]			=						resolve_trust_remote_code(
               _a		,     _a		,     _a		,     _a				)

           if has_remote_code and trust_remote_code:
                 _A							:     Dict			=						get_class_from_dynamic_module(
                     _a		,     _a		,     **_a				)
                 _A							:     str			=						kwargs.pop("""code_revision"""		,     _a				)
                 if os.path.isdir(_a				):
                       feature_extractor_class.register_for_auto_class()
                 return feature_extractor_class.from_dict(_a		,     **_a				)
           elif feature_extractor_class is not None:
                 return feature_extractor_class.from_dict(_a		,     **_a				)
           # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
           elif type(_a				) in FEATURE_EXTRACTOR_MAPPING:
                 _A							:     Dict			=						FEATURE_EXTRACTOR_MAPPING[type(_a				)]
                 return feature_extractor_class.from_dict(_a		,     **_a				)

           raise ValueError(
               F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
               F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
               F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()				)}'''				)



     @staticmethod
     def 					a__	(  _a		,     _a				)						->							Optional[int]:
           FEATURE_EXTRACTOR_MAPPING.register(_a		,     _a				)

