



import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
      import torch







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     int  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     Tuple=14  ,lowerCamelCase_:     List[str]=7  ,lowerCamelCase_:     Optional[Any]=True  ,lowerCamelCase_:     Optional[Any]=True  ,lowerCamelCase_:     str=False  ,lowerCamelCase_:     str=True  ,lowerCamelCase_:     List[Any]=99  ,lowerCamelCase_:     List[Any]=32  ,lowerCamelCase_:     Optional[int]=5  ,lowerCamelCase_:     Optional[Any]=4  ,lowerCamelCase_:     List[Any]=37  ,lowerCamelCase_:     Optional[Any]="gelu"  ,lowerCamelCase_:     List[str]=0.1  ,lowerCamelCase_:     str=0.1  ,lowerCamelCase_:     str=512  ,lowerCamelCase_:     Any=0.0_2  ,)	->      Any:
       UpperCAmelCase_     :       List[Any]  		=					parent
       UpperCAmelCase_     :       str  		=					batch_size
       UpperCAmelCase_     :       Union[str, Any]  		=					seq_length
       UpperCAmelCase_     :       Tuple  		=					is_training
       UpperCAmelCase_     :       Tuple  		=					use_input_mask
       UpperCAmelCase_     :       Any  		=					use_token_type_ids
       UpperCAmelCase_     :       Optional[Any]  		=					use_labels
       UpperCAmelCase_     :       List[str]  		=					vocab_size
       UpperCAmelCase_     :       Optional[int]  		=					hidden_size
       UpperCAmelCase_     :       Optional[Any]  		=					num_hidden_layers
       UpperCAmelCase_     :       Dict  		=					num_attention_heads
       UpperCAmelCase_     :       int  		=					intermediate_size
       UpperCAmelCase_     :       List[str]  		=					hidden_act
       UpperCAmelCase_     :       Tuple  		=					hidden_dropout_prob
       UpperCAmelCase_     :       str  		=					attention_probs_dropout_prob
       UpperCAmelCase_     :       Tuple  		=					max_position_embeddings
       UpperCAmelCase_     :       str  		=					initializer_range
       UpperCAmelCase_     :       Optional[int]  		=					None
       UpperCAmelCase_     :       str  		=					vocab_size - 1
       UpperCAmelCase_     :       Any  		=					vocab_size - 1
       UpperCAmelCase_     :       Dict  		=					vocab_size - 1


   def       A__    (    self:     str							)	->      Any:
       UpperCAmelCase_     :       str  		=					ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size							)

       UpperCAmelCase_     :       List[str]  		=					None
       if self.use_input_mask:
           UpperCAmelCase_     :       Any  		=					random_attention_mask([self.batch_size, self.seq_length]							)

       UpperCAmelCase_     :       Tuple  		=					GPTaConfig(
           vocab_size=self.vocab_size  ,n_embd=self.hidden_size  ,n_layer=self.num_hidden_layers  ,n_head=self.num_attention_heads  ,n_positions=self.max_position_embeddings  ,use_cache=lowerCamelCase_  ,bos_token_id=self.bos_token_id  ,eos_token_id=self.eos_token_id  ,pad_token_id=self.pad_token_id  ,)

       return (config, input_ids, input_mask)


   def       A__    (    self:     Any							)	->      Tuple:
       UpperCAmelCase_     :       Any  		=					self.prepare_config_and_inputs()
       UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       Dict  		=					config_and_inputs
       UpperCAmelCase_     :       Union[str, Any]  		=					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
       return config, inputs_dict


   def       A__    (    self:     Tuple							)	->      Any:
       UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       Optional[Any]  		=					self.prepare_config_and_inputs()

       UpperCAmelCase_     :       List[Any]  		=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
       UpperCAmelCase_     :       Optional[Any]  		=					ids_tensor([self.batch_size, self.seq_length]  ,vocab_size=2							)

       return (
           config,
           input_ids,
           attention_mask,
           encoder_hidden_states,
           encoder_attention_mask,
       )


   def       A__    (    self:     Tuple  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     List[str]							)	->      Tuple:
       UpperCAmelCase_     :       List[Any]  		=					20
       UpperCAmelCase_     :       Union[str, Any]  		=					model_class_name(lowerCamelCase_							)

       UpperCAmelCase_     :       Optional[int]  		=					model.init_cache(input_ids.shape[0]  ,lowerCamelCase_							)
       UpperCAmelCase_     :       Tuple  		=					jnp.ones((input_ids.shape[0], max_decoder_length)  ,dtype="""i4"""							)

       UpperCAmelCase_     :       Tuple  		=					jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1							)[None, :]  ,(input_ids.shape[0], input_ids.shape[-1] - 1)							)
       UpperCAmelCase_     :       List[str]  		=					model(
           input_ids[:, :-1]  ,attention_mask=lowerCamelCase_  ,past_key_values=lowerCamelCase_  ,position_ids=lowerCamelCase_  ,)

       UpperCAmelCase_     :       Union[str, Any]  		=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,dtype="""i4"""							)
       UpperCAmelCase_     :       Tuple  		=					model(
           input_ids[:, -1:]  ,attention_mask=lowerCamelCase_  ,past_key_values=outputs_cache.past_key_values  ,position_ids=lowerCamelCase_  ,)

       UpperCAmelCase_     :       Tuple  		=					model(lowerCamelCase_							)

       UpperCAmelCase_     :       List[str]  		=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
       self.parent.assertTrue(diff < 1e-3  ,msg=F'''Max diff is {diff}'''							)






   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     List[Any]  ,lowerCamelCase_:     List[Any]							)	->      Union[str, Any]:
       UpperCAmelCase_     :       List[Any]  		=					20
       UpperCAmelCase_     :       Union[str, Any]  		=					model_class_name(lowerCamelCase_							)

       UpperCAmelCase_     :       int  		=					jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])							)]  ,axis=-1  ,)

       UpperCAmelCase_     :       Optional[int]  		=					model.init_cache(input_ids.shape[0]  ,lowerCamelCase_							)
       UpperCAmelCase_     :       List[Any]  		=					jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1							)[None, :]  ,(input_ids.shape[0], input_ids.shape[-1] - 1)							)

       UpperCAmelCase_     :       Union[str, Any]  		=					model(
           input_ids[:, :-1]  ,attention_mask=lowerCamelCase_  ,past_key_values=lowerCamelCase_  ,position_ids=lowerCamelCase_  ,)
       UpperCAmelCase_     :       Optional[int]  		=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,dtype="""i4"""							)
       UpperCAmelCase_     :       Optional[Any]  		=					model(
           input_ids[:, -1:]  ,past_key_values=outputs_cache.past_key_values  ,attention_mask=lowerCamelCase_  ,position_ids=lowerCamelCase_  ,)

       UpperCAmelCase_     :       Any  		=					model(lowerCamelCase_  ,attention_mask=lowerCamelCase_							)

       UpperCAmelCase_     :       str  		=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
       self.parent.assertTrue(diff < 1e-3  ,msg=F'''Max diff is {diff}'''							)







@require_flax
class       _snake_case				(     __snake_case ,							__snake_case ,							unittest.TestCase ):






   '''simple docstring'''






   A__      : Tuple       =						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
   A__      : Optional[Any]       =						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()


   def       A__    (    self:     str							)	->      List[Any]:
       UpperCAmelCase_     :       List[Any]  		=					FlaxGPTaModelTester(self							)


   def       A__    (    self:     List[Any]							)	->      Union[str, Any]:
       for model_class_name in self.all_model_classes:
           UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       Optional[Any]  		=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)


   def       A__    (    self:     List[str]							)	->      List[Any]:
       for model_class_name in self.all_model_classes:
           UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       Tuple  		=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)


   @slow
   def       A__    (    self:     List[Any]							)	->      Optional[Any]:
       UpperCAmelCase_     :       List[str]  		=					GPTaTokenizer.from_pretrained("""gpt2"""  ,pad_token="""</s>"""  ,padding_side="""left"""							)
       UpperCAmelCase_     :       List[str]  		=					tokenizer(["""Hello this is a long string""", """Hey"""]  ,return_tensors="""np"""  ,padding=lowerCamelCase_  ,truncation=lowerCamelCase_							)

       UpperCAmelCase_     :       int  		=					FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""							)
       UpperCAmelCase_     :       List[Any]  		=					False
       UpperCAmelCase_     :       Any  		=					model.config.eos_token_id

       UpperCAmelCase_     :       Optional[int]  		=					jax.jit(model.generate							)

       UpperCAmelCase_     :       Union[str, Any]  		=					jit_generate(inputs["""input_ids"""]  ,attention_mask=inputs["""attention_mask"""]							).sequences

       UpperCAmelCase_     :       Optional[int]  		=					tokenizer.batch_decode(lowerCamelCase_  ,skip_special_tokens=lowerCamelCase_							)

       UpperCAmelCase_     :       str  		=					[
           """Hello this is a long string of words. I'm going to start with the first one.\n""",
           """Hey, I'm not sure if I'm going to be able to do""",
       ]

       self.assertListEqual(lowerCamelCase_  ,lowerCamelCase_							)


   @is_pt_flax_cross_test
   def       A__    (    self:     Optional[int]							)	->      Union[str, Any]:
       UpperCAmelCase_		, UpperCAmelCase_     :       Tuple  		=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__							):
               # prepare inputs
               UpperCAmelCase_     :       Optional[Any]  		=					self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)
               UpperCAmelCase_     :       List[str]  		=					{k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

               # load corresponding PyTorch class
               UpperCAmelCase_     :       Tuple  		=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
               UpperCAmelCase_     :       List[str]  		=					getattr(lowerCamelCase_  ,lowerCamelCase_							)

               UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					pt_inputs["""input_ids"""].shape
               UpperCAmelCase_     :       Optional[int]  		=					np.random.randint(0  ,seq_length - 1  ,size=(batch_size,)							)
               for batch_idx, start_index in enumerate(lowerCamelCase_							):
                   UpperCAmelCase_     :       int  		=					0
                   UpperCAmelCase_     :       Any  		=					1
                   UpperCAmelCase_     :       List[str]  		=					0
                   UpperCAmelCase_     :       List[str]  		=					1
               UpperCAmelCase_     :       Union[str, Any]  		=					pt_model_class(lowerCamelCase_							).eval()
               UpperCAmelCase_     :       str  		=					model_class(lowerCamelCase_  ,dtype=jnp.floataa							)

               UpperCAmelCase_     :       Union[str, Any]  		=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,lowerCamelCase_							)
               UpperCAmelCase_     :       Any  		=					fx_state

               with torch.no_grad():
                   UpperCAmelCase_     :       Optional[int]  		=					pt_model(**lowerCamelCase_							).to_tuple()

               UpperCAmelCase_     :       List[str]  		=					fx_model(**lowerCamelCase_							).to_tuple()
               self.assertEqual(len(lowerCamelCase_							)  ,len(lowerCamelCase_							)  ,"""Output lengths differ between Flax and PyTorch"""							)
               for fx_output, pt_output in zip(lowerCamelCase_  ,lowerCamelCase_							):
                   self.assert_almost_equals(fx_output[:, -1]  ,pt_output[:, -1].numpy()  ,4e-2							)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   pt_model.save_pretrained(lowerCamelCase_							)
                   UpperCAmelCase_     :       Union[str, Any]  		=					model_class.from_pretrained(lowerCamelCase_  ,from_pt=lowerCamelCase_							)

               UpperCAmelCase_     :       Any  		=					fx_model_loaded(**lowerCamelCase_							).to_tuple()
               self.assertEqual(
                   len(lowerCamelCase_							)  ,len(lowerCamelCase_							)  ,"""Output lengths differ between Flax and PyTorch"""							)
               for fx_output_loaded, pt_output in zip(lowerCamelCase_  ,lowerCamelCase_							):
                   self.assert_almost_equals(fx_output_loaded[:, -1]  ,pt_output[:, -1].numpy()  ,4e-2							)


   @is_pt_flax_cross_test
   def       A__    (    self:     Optional[int]							)	->      Any:
       UpperCAmelCase_		, UpperCAmelCase_     :       Union[str, Any]  		=					self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__							):
               # prepare inputs
               UpperCAmelCase_     :       Any  		=					self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)
               UpperCAmelCase_     :       Dict  		=					{k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

               # load corresponding PyTorch class
               UpperCAmelCase_     :       Dict  		=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
               UpperCAmelCase_     :       Dict  		=					getattr(lowerCamelCase_  ,lowerCamelCase_							)

               UpperCAmelCase_     :       Union[str, Any]  		=					pt_model_class(lowerCamelCase_							).eval()
               UpperCAmelCase_     :       Dict  		=					model_class(lowerCamelCase_  ,dtype=jnp.floataa							)

               UpperCAmelCase_     :       List[Any]  		=					load_flax_weights_in_pytorch_model(lowerCamelCase_  ,fx_model.params							)
               UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					pt_inputs["""input_ids"""].shape
               UpperCAmelCase_     :       int  		=					np.random.randint(0  ,seq_length - 1  ,size=(batch_size,)							)
               for batch_idx, start_index in enumerate(lowerCamelCase_							):
                   UpperCAmelCase_     :       Tuple  		=					0
                   UpperCAmelCase_     :       Optional[Any]  		=					1
                   UpperCAmelCase_     :       Dict  		=					0
                   UpperCAmelCase_     :       Any  		=					1

               # make sure weights are tied in PyTorch
               pt_model.tie_weights()

               with torch.no_grad():
                   UpperCAmelCase_     :       str  		=					pt_model(**lowerCamelCase_							).to_tuple()

               UpperCAmelCase_     :       Optional[int]  		=					fx_model(**lowerCamelCase_							).to_tuple()
               self.assertEqual(len(lowerCamelCase_							)  ,len(lowerCamelCase_							)  ,"""Output lengths differ between Flax and PyTorch"""							)
               for fx_output, pt_output in zip(lowerCamelCase_  ,lowerCamelCase_							):
                   self.assert_almost_equals(fx_output[:, -1]  ,pt_output[:, -1].numpy()  ,4e-2							)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   fx_model.save_pretrained(lowerCamelCase_							)
                   UpperCAmelCase_     :       Optional[Any]  		=					pt_model_class.from_pretrained(lowerCamelCase_  ,from_flax=lowerCamelCase_							)

               with torch.no_grad():
                   UpperCAmelCase_     :       Any  		=					pt_model_loaded(**lowerCamelCase_							).to_tuple()

               self.assertEqual(
                   len(lowerCamelCase_							)  ,len(lowerCamelCase_							)  ,"""Output lengths differ between Flax and PyTorch"""							)
               for fx_output, pt_output in zip(lowerCamelCase_  ,lowerCamelCase_							):
                   self.assert_almost_equals(fx_output[:, -1]  ,pt_output[:, -1].numpy()  ,4e-2							)






   @slow
   def       A__    (    self:     Optional[Any]							)	->      List[Any]:
       for model_class_name in self.all_model_classes:
           UpperCAmelCase_     :       int  		=					model_class_name.from_pretrained("""gpt2"""  ,from_pt=lowerCamelCase_							)
           UpperCAmelCase_     :       Dict  		=					model(np.ones((1, 1)							)							)
           self.assertIsNotNone(lowerCamelCase_							)

import importlib
import inspect
import json
import os
from collections import OrderedDict

# 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 ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import 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,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


snake_case__						      =			logging.get_logger(__name__)

snake_case__						      =			OrderedDict(
    [
        ('align', 'AlignProcessor'),
        ('altclip', 'AltCLIPProcessor'),
        ('bark', 'BarkProcessor'),
        ('blip', 'BlipProcessor'),
        ('blip-2', 'Blip2Processor'),
        ('bridgetower', 'BridgeTowerProcessor'),
        ('chinese_clip', 'ChineseCLIPProcessor'),
        ('clap', 'ClapProcessor'),
        ('clip', 'CLIPProcessor'),
        ('clipseg', 'CLIPSegProcessor'),
        ('flava', 'FlavaProcessor'),
        ('git', 'GitProcessor'),
        ('groupvit', 'CLIPProcessor'),
        ('hubert', 'Wav2Vec2Processor'),
        ('instructblip', 'InstructBlipProcessor'),
        ('layoutlmv2', 'LayoutLMv2Processor'),
        ('layoutlmv3', 'LayoutLMv3Processor'),
        ('markuplm', 'MarkupLMProcessor'),
        ('mctct', 'MCTCTProcessor'),
        ('mgp-str', 'MgpstrProcessor'),
        ('oneformer', 'OneFormerProcessor'),
        ('owlvit', 'OwlViTProcessor'),
        ('pix2struct', 'Pix2StructProcessor'),
        ('sam', 'SamProcessor'),
        ('sew', 'Wav2Vec2Processor'),
        ('sew-d', 'Wav2Vec2Processor'),
        ('speech_to_text', 'Speech2TextProcessor'),
        ('speech_to_text_2', 'Speech2Text2Processor'),
        ('speecht5', 'SpeechT5Processor'),
        ('trocr', 'TrOCRProcessor'),
        ('tvlt', 'TvltProcessor'),
        ('unispeech', 'Wav2Vec2Processor'),
        ('unispeech-sat', 'Wav2Vec2Processor'),
        ('vilt', 'ViltProcessor'),
        ('vision-text-dual-encoder', 'VisionTextDualEncoderProcessor'),
        ('wav2vec2', 'Wav2Vec2Processor'),
        ('wav2vec2-conformer', 'Wav2Vec2Processor'),
        ('wavlm', 'Wav2Vec2Processor'),
        ('whisper', 'WhisperProcessor'),
        ('xclip', 'XCLIPProcessor'),
    ]
)

snake_case__						      =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
            if class_name in processors:
                  _lowerCamelCase           =					model_type_to_module_name(__UpperCAmelCase		)

                  _lowerCamelCase           =					importlib.import_module(F'.{module_name}'			,    '''transformers.models'''		)
                  try:
                        return getattr(__UpperCAmelCase			,    __UpperCAmelCase		)
                  except AttributeError:
                        continue

      for processor in PROCESSOR_MAPPING._extra_content.values():
            if getattr(__UpperCAmelCase			,    '''__name__'''			,    __UpperCAmelCase		) == class_name:
                  return processor

    # 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.
      _lowerCamelCase           =					importlib.import_module('''transformers'''		)
      if hasattr(__UpperCAmelCase			,    __UpperCAmelCase		):
            return getattr(__UpperCAmelCase			,    __UpperCAmelCase		)

      return None




class 			UpperCamelCase					:

     '''simple docstring'''

     def __init__(       self    )			->					List[str]:






           """simple docstring"""
           raise EnvironmentError(
               '''AutoProcessor is designed to be instantiated '''
               '''using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''    )



     @classmethod
     @replace_list_option_in_docstrings(A_    )
     def 		UpperCamelCase_    (       cls					,					A_					,					**A_    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					kwargs.pop('''config'''					,					A_    )
           _lowerCamelCase           =					kwargs.pop('''trust_remote_code'''					,					A_    )
           _lowerCamelCase           =					True

           _lowerCamelCase           =					None
           _lowerCamelCase           =					None

           # First, let's see if we have a preprocessor config.
           # Filter the kwargs for `get_file_from_repo`.
           _lowerCamelCase           =					{
               key: kwargs[key] for key in inspect.signature(A_    ).parameters.keys() if key in kwargs
           }
           # Let's start by checking whether the processor class is saved in an image processor
           _lowerCamelCase           =					get_file_from_repo(
               A_					,					A_					,					**A_    )
           if preprocessor_config_file is not None:
                 _lowerCamelCase     ,       _lowerCamelCase           =					ImageProcessingMixin.get_image_processor_dict(A_					,					**A_    )
                 _lowerCamelCase           =					config_dict.get('''processor_class'''					,					A_    )
                 if "AutoProcessor" in config_dict.get('''auto_map'''					,					{}    ):
                       _lowerCamelCase           =					config_dict['''auto_map''']['''AutoProcessor''']

        # If not found, let's check whether the processor class is saved in a feature extractor config
           if preprocessor_config_file is not None and processor_class is None:
                 _lowerCamelCase     ,       _lowerCamelCase           =					FeatureExtractionMixin.get_feature_extractor_dict(A_					,					**A_    )
                 _lowerCamelCase           =					config_dict.get('''processor_class'''					,					A_    )
                 if "AutoProcessor" in config_dict.get('''auto_map'''					,					{}    ):
                       _lowerCamelCase           =					config_dict['''auto_map''']['''AutoProcessor''']

           if processor_class is None:
                 # Next, let's check whether the processor class is saved in a tokenizer
                 _lowerCamelCase           =					get_file_from_repo(
                     A_					,					A_					,					**A_    )
                 if tokenizer_config_file is not None:
                       with open(A_					,					encoding='''utf-8'''    ) as reader:
                             _lowerCamelCase           =					json.load(A_    )

                       _lowerCamelCase           =					config_dict.get('''processor_class'''					,					A_    )
                       if "AutoProcessor" in config_dict.get('''auto_map'''					,					{}    ):
                             _lowerCamelCase           =					config_dict['''auto_map''']['''AutoProcessor''']

           if processor_class is None:
                 # Otherwise, load config, if it can be loaded.
                 if not isinstance(A_					,					A_    ):
                       _lowerCamelCase           =					AutoConfig.from_pretrained(
                           A_					,					trust_remote_code=A_					,					**A_    )

                 # And check if the config contains the processor class.
                 _lowerCamelCase           =					getattr(A_					,					'''processor_class'''					,					A_    )
                 if hasattr(A_					,					'''auto_map'''    ) and "AutoProcessor" in config.auto_map:
                       _lowerCamelCase           =					config.auto_map['''AutoProcessor''']

           if processor_class is not None:
                 _lowerCamelCase           =					processor_class_from_name(A_    )

           _lowerCamelCase           =					processor_auto_map is not None
           _lowerCamelCase           =					processor_class is not None or type(A_    ) in PROCESSOR_MAPPING
           _lowerCamelCase           =					resolve_trust_remote_code(
               A_					,					A_					,					A_					,					A_    )

           if has_remote_code and trust_remote_code:
                 _lowerCamelCase           =					get_class_from_dynamic_module(
                     A_					,					A_					,					**A_    )
                 _lowerCamelCase           =					kwargs.pop('''code_revision'''					,					A_    )
                 if os.path.isdir(A_    ):
                       processor_class.register_for_auto_class()
                 return processor_class.from_pretrained(
                     A_					,					trust_remote_code=A_					,					**A_    )
           elif processor_class is not None:
                 return processor_class.from_pretrained(
                     A_					,					trust_remote_code=A_					,					**A_    )
           # Last try: we use the PROCESSOR_MAPPING.
           elif type(A_    ) in PROCESSOR_MAPPING:
                 return PROCESSOR_MAPPING[type(A_    )].from_pretrained(A_					,					**A_    )

           # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
           # tokenizer.
           try:
                 return AutoTokenizer.from_pretrained(
                     A_					,					trust_remote_code=A_					,					**A_    )
           except Exception:
                 try:
                       return AutoImageProcessor.from_pretrained(
                           A_					,					trust_remote_code=A_					,					**A_    )
                 except Exception:
                       pass

                 try:
                       return AutoFeatureExtractor.from_pretrained(
                           A_					,					trust_remote_code=A_					,					**A_    )
                 except Exception:
                       pass

           raise ValueError(
               F'Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '
               '''tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'''
               '''the files of at least one of those processing classes.'''    )





     @staticmethod
     def 		UpperCamelCase_    (       A_					,					A_    )			->					List[Any]:






           """simple docstring"""
           PROCESSOR_MAPPING.register(A_					,					A_    )