



"""simple docstring"""


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				snake_case_								=			"""platform"""

				import jax
				import jax.numpy as jnp

				from transformers import MarianTokenizer
				from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_=None						,      lowercase_=None						,      lowercase_=None						,      lowercase_=None						,      lowercase_=None						,      lowercase_=None						,      ):
						if attention_mask is None:
												UpperCAmelCase			 =					np.where(input_ids != config.pad_token_id						,      1						,      0		)
						if decoder_attention_mask is None:
												UpperCAmelCase			 =					np.where(decoder_input_ids != config.pad_token_id						,      1						,      0		)
						if head_mask is None:
												UpperCAmelCase			 =					np.ones((config.encoder_layers, config.encoder_attention_heads)		)
						if decoder_head_mask is None:
												UpperCAmelCase			 =					np.ones((config.decoder_layers, config.decoder_attention_heads)		)
						if cross_attn_head_mask is None:
												UpperCAmelCase			 =					np.ones((config.decoder_layers, config.decoder_attention_heads)		)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": attention_mask,
						}







class  A_     :



						"""simple docstring"""






						def __init__(   self					:Any  ,    lowercase_					:Tuple  ,    lowercase_					:List[str]=13  ,    lowercase_					:Optional[int]=7  ,    lowercase_					:List[Any]=True  ,    lowercase_					:Any=False  ,    lowercase_					:Optional[Any]=99  ,    lowercase_					:Optional[int]=16  ,    lowercase_					:List[Any]=2  ,    lowercase_					:Tuple=4  ,    lowercase_					:Optional[int]=4  ,    lowercase_					:Union[str, Any]="gelu"  ,    lowercase_					:List[Any]=0.1  ,    lowercase_					:int=0.1  ,    lowercase_					:List[str]=32  ,    lowercase_					:Dict=2  ,    lowercase_					:Optional[Any]=1  ,    lowercase_					:int=0  ,    lowercase_					:Union[str, Any]=0.02  ,    )							->     Any:
												UpperCAmelCase			 =					parent
												UpperCAmelCase			 =					batch_size
												UpperCAmelCase			 =					seq_length
												UpperCAmelCase			 =					is_training
												UpperCAmelCase			 =					use_labels
												UpperCAmelCase			 =					vocab_size
												UpperCAmelCase			 =					hidden_size
												UpperCAmelCase			 =					num_hidden_layers
												UpperCAmelCase			 =					num_attention_heads
												UpperCAmelCase			 =					intermediate_size
												UpperCAmelCase			 =					hidden_act
												UpperCAmelCase			 =					hidden_dropout_prob
												UpperCAmelCase			 =					attention_probs_dropout_prob
												UpperCAmelCase			 =					max_position_embeddings
												UpperCAmelCase			 =					eos_token_id
												UpperCAmelCase			 =					pad_token_id
												UpperCAmelCase			 =					bos_token_id
												UpperCAmelCase			 =					initializer_range






						def       UpperCAmelCase__	(   self					:List[str]   )							->     Tuple:
												UpperCAmelCase			 =					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,    self.vocab_size   )  ,    3  ,    self.vocab_size   )
												UpperCAmelCase			 =					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,    dtype=np.intaa   ))  ,    -1   )

												UpperCAmelCase			 =					shift_tokens_right(lowercase_  ,    1  ,    2   )

												UpperCAmelCase			 =					MarianConfig(
												    vocab_size=self.vocab_size  ,    d_model=self.hidden_size  ,    encoder_layers=self.num_hidden_layers  ,    decoder_layers=self.num_hidden_layers  ,    encoder_attention_heads=self.num_attention_heads  ,    decoder_attention_heads=self.num_attention_heads  ,    encoder_ffn_dim=self.intermediate_size  ,    decoder_ffn_dim=self.intermediate_size  ,    dropout=self.hidden_dropout_prob  ,    attention_dropout=self.attention_probs_dropout_prob  ,    max_position_embeddings=self.max_position_embeddings  ,    eos_token_id=self.eos_token_id  ,    bos_token_id=self.bos_token_id  ,    pad_token_id=self.pad_token_id  ,    initializer_range=self.initializer_range  ,    use_cache=lowercase_  ,    )
												UpperCAmelCase			 =					prepare_marian_inputs_dict(lowercase_  ,    lowercase_  ,    lowercase_   )
												return config, inputs_dict






						def       UpperCAmelCase__	(   self					:Tuple   )							->     Any:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.prepare_config_and_inputs()
												return config, inputs_dict






						def       UpperCAmelCase__	(   self					:Tuple  ,    lowercase_					:List[Any]  ,    lowercase_					:Any  ,    lowercase_					:str   )							->     Tuple:
												UpperCAmelCase			 =					20
												UpperCAmelCase			 =					model_class_name(lowercase_   )

												UpperCAmelCase			 =					model.encode(inputs_dict['input_ids']   )

												UpperCAmelCase ,       UpperCAmelCase			 =					(
												    inputs_dict['decoder_input_ids'],
												    inputs_dict['decoder_attention_mask'],
												)

												UpperCAmelCase			 =					model.init_cache(decoder_input_ids.shape[0]  ,    lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,    dtype='i4'   )

												UpperCAmelCase			 =					jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]  ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,    )
												UpperCAmelCase			 =					model.decode(
												    decoder_input_ids[:, :-1]  ,    lowercase_  ,    decoder_attention_mask=lowercase_  ,    past_key_values=lowercase_  ,    decoder_position_ids=lowercase_  ,    )

												UpperCAmelCase			 =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,    dtype='i4'   )
												UpperCAmelCase			 =					model.decode(
												    decoder_input_ids[:, -1:]  ,    lowercase_  ,    decoder_attention_mask=lowercase_  ,    past_key_values=outputs_cache.past_key_values  ,    decoder_position_ids=lowercase_  ,    )

												UpperCAmelCase			 =					model.decode(lowercase_  ,    lowercase_   )

												UpperCAmelCase			 =					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       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:List[Any]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Any   )							->     Optional[Any]:
												UpperCAmelCase			 =					20
												UpperCAmelCase			 =					model_class_name(lowercase_   )

												UpperCAmelCase			 =					model.encode(inputs_dict['input_ids']   )

												UpperCAmelCase ,       UpperCAmelCase			 =					(
												    inputs_dict['decoder_input_ids'],
												    inputs_dict['decoder_attention_mask'],
												)

												UpperCAmelCase			 =					jnp.concatenate(
												    [
												        decoder_attention_mask,
												        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
												    ]  ,    axis=-1  ,    )

												UpperCAmelCase			 =					model.init_cache(decoder_input_ids.shape[0]  ,    lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]  ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,    )

												UpperCAmelCase			 =					model.decode(
												    decoder_input_ids[:, :-1]  ,    lowercase_  ,    decoder_attention_mask=lowercase_  ,    past_key_values=lowercase_  ,    decoder_position_ids=lowercase_  ,    )
												UpperCAmelCase			 =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,    dtype='i4'   )
												UpperCAmelCase			 =					model.decode(
												    decoder_input_ids[:, -1:]  ,    lowercase_  ,    past_key_values=outputs_cache.past_key_values  ,    decoder_attention_mask=lowercase_  ,    decoder_position_ids=lowercase_  ,    )

												UpperCAmelCase			 =					model.decode(lowercase_  ,    lowercase_  ,    decoder_attention_mask=lowercase_   )

												UpperCAmelCase			 =					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  A_     (	SCREAMING_SNAKE_CASE_      ,  unittest.TestCase      ,  SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				True
						__UpperCamelCase													=				(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
						__UpperCamelCase													=				(FlaxMarianMTModel,) if is_flax_available() else ()






						def       UpperCAmelCase__	(   self					:List[str]   )							->     Tuple:
												UpperCAmelCase			 =					FlaxMarianModelTester(self   )






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     Optional[Any]:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(lowercase_  ,    lowercase_  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:List[str]   )							->     str:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_  ,    lowercase_  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:Dict   )							->     Tuple:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__   ):
																								UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_   )
																								UpperCAmelCase			 =					model_class(lowercase_   )

																								@jax.jit
																								def encode_jitted(lowercase_					:List[Any]  ,    lowercase_					:int=None  ,    **lowercase_					:List[str]   ):
																														return model.encode(input_ids=lowercase_  ,    attention_mask=lowercase_   )

																								with self.subTest('JIT Enabled'   ):
																														UpperCAmelCase			 =					encode_jitted(**lowercase_   ).to_tuple()

																								with self.subTest('JIT Disabled'   ):
																														with jax.disable_jit():
																																				UpperCAmelCase			 =					encode_jitted(**lowercase_   ).to_tuple()

																								self.assertEqual(len(lowercase_   )  ,    len(lowercase_   )   )
																								for jitted_output, output in zip(lowercase_  ,    lowercase_   ):
																														self.assertEqual(jitted_output.shape  ,    output.shape   )






						def       UpperCAmelCase__	(   self					:List[str]   )							->     List[str]:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__   ):
																								UpperCAmelCase			 =					model_class(lowercase_   )
																								UpperCAmelCase			 =					model.encode(inputs_dict['input_ids']  ,    inputs_dict['attention_mask']   )

																								UpperCAmelCase			 =					{
																								    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																								    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																								    'encoder_outputs': encoder_outputs,
																								}

																								@jax.jit
																								def decode_jitted(lowercase_					:List[str]  ,    lowercase_					:Any  ,    lowercase_					:int   ):
																														return model.decode(
																														    decoder_input_ids=lowercase_  ,    decoder_attention_mask=lowercase_  ,    encoder_outputs=lowercase_  ,    )

																								with self.subTest('JIT Enabled'   ):
																														UpperCAmelCase			 =					decode_jitted(**lowercase_   ).to_tuple()

																								with self.subTest('JIT Disabled'   ):
																														with jax.disable_jit():
																																				UpperCAmelCase			 =					decode_jitted(**lowercase_   ).to_tuple()

																								self.assertEqual(len(lowercase_   )  ,    len(lowercase_   )   )
																								for jitted_output, output in zip(lowercase_  ,    lowercase_   ):
																														self.assertEqual(jitted_output.shape  ,    output.shape   )






						@slow
						def       UpperCAmelCase__	(   self					:str   )							->     Optional[Any]:
												for model_class_name in self.all_model_classes:
																		UpperCAmelCase			 =					model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'   )
																		# FlaxMarianForSequenceClassification expects eos token in input_ids
																		UpperCAmelCase			 =					np.ones((1, 1)   ) * model.config.eos_token_id
																		UpperCAmelCase			 =					model(lowercase_   )
																		self.assertIsNotNone(lowercase_   )







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	unittest.TestCase							):



						"""simple docstring"""
						__UpperCamelCase													=				None
						__UpperCamelCase													=				None






						@classmethod
						def       UpperCAmelCase__	(   cls					:Any   )							->     None:
												UpperCAmelCase			 =					f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
												return cls






						@cached_property
						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     Tuple:
												return MarianTokenizer.from_pretrained(self.model_name   )






						@property
						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     int:
												return self.tokenizer.eos_token_id






						@cached_property
						def       UpperCAmelCase__	(   self					:Dict   )							->     Union[str, Any]:
												UpperCAmelCase			 =					FlaxMarianMTModel.from_pretrained(self.model_name   )
												self.assertEqual(model.config.decoder_start_token_id  ,    model.config.pad_token_id   )
												return model






						def       UpperCAmelCase__	(   self					:List[str]  ,    **lowercase_					:int   )							->     Union[str, Any]:
												UpperCAmelCase			 =					self.translate_src_text(**lowercase_   )
												self.assertListEqual(self.expected_text  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:Dict  ,    **lowercase_					:Tuple   )							->     List[Any]:
												UpperCAmelCase			 =					self.tokenizer(self.src_text  ,    padding=lowercase_  ,    return_tensors='np'  ,    **lowercase_   )
												UpperCAmelCase			 =					self.model.generate(
												    model_inputs.input_ids  ,    attention_mask=model_inputs.attention_mask  ,    num_beams=2  ,    max_length=1_28  ,    ).sequences
												UpperCAmelCase			 =					self.tokenizer.batch_decode(lowercase_  ,    skip_special_tokens=lowercase_   )
												return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				"""en"""
						__UpperCamelCase													=				"""fr"""
						__UpperCamelCase													=				[
						    """I am a small frog.""",
						    """Now I can forget the 100 words of german that I know.""",
						]
						__UpperCamelCase													=				[
						    """Je suis une petite grenouille.""",
						    """Maintenant, je peux oublier les 100 mots d'allemand que je connais.""",
						]






						@slow
						def       UpperCAmelCase__	(   self					:List[Any]   )							->     List[str]:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				"""fr"""
						__UpperCamelCase													=				"""en"""
						__UpperCamelCase													=				[
						    """Donnez moi le micro.""",
						    """Tom et Mary étaient assis à une table.""",  # Accents
						]
						__UpperCamelCase													=				[
						    """Give me the microphone.""",
						    """Tom and Mary were sitting at a table.""",
						]






						@slow
						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     int:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				"""mt"""
						__UpperCamelCase													=				"""en"""
						__UpperCamelCase													=				["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
						__UpperCamelCase													=				["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]






						@slow
						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Optional[Any]:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				"""en"""
						__UpperCamelCase													=				"""de"""
						__UpperCamelCase													=				[
						    """I am a small frog.""",
						    """Now I can forget the 100 words of german that I know.""",
						    """Tom asked his teacher for advice.""",
						    """That's how I would do it.""",
						    """Tom really admired Mary's courage.""",
						    """Turn around and close your eyes.""",
						]
						__UpperCamelCase													=				[
						    """Ich bin ein kleiner Frosch.""",
						    """Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.""",
						    """Tom bat seinen Lehrer um Rat.""",
						    """So würde ich das machen.""",
						    """Tom bewunderte Marias Mut wirklich.""",
						    """Drehen Sie sich um und schließen Sie die Augen.""",
						]






						@slow
						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     Union[str, Any]:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				"""en"""
						__UpperCamelCase													=				"""zh"""
						__UpperCamelCase													=				["""My name is Wolfgang and I live in Berlin"""]
						__UpperCamelCase													=				["""我叫沃尔夫冈 我住在柏林"""]






						@slow
						def       UpperCAmelCase__	(   self					:Any   )							->     Union[str, Any]:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				"""ru"""
						__UpperCamelCase													=				"""fr"""
						__UpperCamelCase													=				["""Он показал мне рукопись своей новой пьесы."""]
						__UpperCamelCase													=				["""Il m'a montré le manuscrit de sa nouvelle pièce."""]






						@slow
						def       UpperCAmelCase__	(   self					:str   )							->     Any:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				"""en"""
						__UpperCamelCase													=				"""ROMANCE"""
						__UpperCamelCase													=				[
						    """>>fr<< Don't spend so much time watching TV.""",
						    """>>pt<< Your message has been sent.""",
						    """>>es<< He's two years older than me.""",
						]
						__UpperCamelCase													=				[
						    """Ne passez pas autant de temps à regarder la télé.""",
						    """A sua mensagem foi enviada.""",
						    """Es dos años más viejo que yo.""",
						]






						@slow
						def       UpperCAmelCase__	(   self					:List[str]   )							->     Any:
												self._assert_generated_batch_equal_expected()










"""simple docstring"""


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 					_lowerCAmelCase (  lowercase_		):
						for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
												if class_name in processors:
																		UpperCAmelCase			 =					model_type_to_module_name(lowercase_		)

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

						for processor in PROCESSOR_MAPPING._extra_content.values():
												if getattr(lowercase_						,      '__name__'						,      lowercase_		) == 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.
						UpperCAmelCase			 =					importlib.import_module('transformers'		)
						if hasattr(lowercase_						,      lowercase_		):
												return getattr(lowercase_						,      lowercase_		)

						return None







class  A_     :



						"""simple docstring"""






						def __init__(   self					:Optional[Any]   )							->     str:
												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(lowercase_   )
						def       UpperCAmelCase__	(   cls					:Optional[int]  ,    lowercase_					:Tuple  ,    **lowercase_					:int   )							->     Optional[int]:
												UpperCAmelCase			 =					kwargs.pop('config'  ,    lowercase_   )
												UpperCAmelCase			 =					kwargs.pop('trust_remote_code'  ,    lowercase_   )
												UpperCAmelCase			 =					True

												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None

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

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

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

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

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

												if processor_class is not None:
																		UpperCAmelCase			 =					processor_class_from_name(lowercase_   )

												UpperCAmelCase			 =					processor_auto_map is not None
												UpperCAmelCase			 =					processor_class is not None or type(lowercase_   ) in PROCESSOR_MAPPING
												UpperCAmelCase			 =					resolve_trust_remote_code(
												    lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_   )

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

												# 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(
																		    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
												except Exception:
																		try:
																								return AutoImageProcessor.from_pretrained(
																								    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
																		except Exception:
																								pass

																		try:
																								return AutoFeatureExtractor.from_pretrained(
																								    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
																		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__	(   lowercase_					:int  ,    lowercase_					:Union[str, Any]   )							->     Tuple:
												PROCESSOR_MAPPING.register(lowercase_  ,    lowercase_   )





