





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


A_        =  logging.get_logger(__name__)

A_        =  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"),
    ]
)

A_        =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def  __UpperCamelCase   (     a)			->Union[str, Any]:
					for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
										if class_name in processors:
															lowerCamelCase__      =							model_type_to_module_name(a)

															lowerCamelCase__      =							importlib.import_module(f".{module_name}",  "transformers.models")
															try:
																				return getattr(a,  a)
															except AttributeError:
																				continue

					for processor in PROCESSOR_MAPPING._extra_content.values():
										if getattr(a,  "__name__",  a) == 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(a,  a):
										return getattr(a,  a)

					return None







class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					def __init__(			self						):
										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(_lowerCAmelCase						)
					def        __magic_name__			(			cls						,	_lowerCAmelCase						,	**_lowerCAmelCase						):
										lowerCamelCase__      =							kwargs.pop("config"						,	_lowerCAmelCase						)
										lowerCamelCase__      =							kwargs.pop("trust_remote_code"						,	_lowerCAmelCase						)
										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(_lowerCAmelCase						).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(
										    _lowerCAmelCase						,	_lowerCAmelCase						,	**_lowerCAmelCase						)
										if preprocessor_config_file is not None:
															lowerCamelCase__     ,		lowerCamelCase__      =							ImageProcessingMixin.get_image_processor_dict(_lowerCAmelCase						,	**_lowerCAmelCase						)
															lowerCamelCase__      =							config_dict.get("processor_class"						,	_lowerCAmelCase						)
															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(_lowerCAmelCase						,	**_lowerCAmelCase						)
															lowerCamelCase__      =							config_dict.get("processor_class"						,	_lowerCAmelCase						)
															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(
															    _lowerCAmelCase						,	_lowerCAmelCase						,	**_lowerCAmelCase						)
															if tokenizer_config_file is not None:
																				with open(_lowerCAmelCase						,	encoding="utf-8"						) as reader:
																									lowerCamelCase__      =							json.load(_lowerCAmelCase						)

																				lowerCamelCase__      =							config_dict.get("processor_class"						,	_lowerCAmelCase						)
																				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(_lowerCAmelCase						,	_lowerCAmelCase						):
																				lowerCamelCase__      =							AutoConfig.from_pretrained(
																				    _lowerCAmelCase						,	trust_remote_code=_lowerCAmelCase						,	**_lowerCAmelCase						)

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

										if processor_class is not None:
															lowerCamelCase__      =							processor_class_from_name(_lowerCAmelCase						)

										lowerCamelCase__      =							processor_auto_map is not None
										lowerCamelCase__      =							processor_class is not None or type(_lowerCAmelCase						) in PROCESSOR_MAPPING
										lowerCamelCase__      =							resolve_trust_remote_code(
										    _lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

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

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

															try:
																				return AutoFeatureExtractor.from_pretrained(
																				    _lowerCAmelCase						,	trust_remote_code=_lowerCAmelCase						,	**_lowerCAmelCase						)
															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        __magic_name__			(			_lowerCAmelCase						,	_lowerCAmelCase						):
										PROCESSOR_MAPPING.register(_lowerCAmelCase						,	_lowerCAmelCase						)








from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
						from PIL import Image

						from transformers import ConvNextImageProcessor

class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=13						,	_lowerCAmelCase=32						,	_lowerCAmelCase=3						,	_lowerCAmelCase=4						,	_lowerCAmelCase=[10, 20, 30, 40]						,	_lowerCAmelCase=[2, 2, 3, 2]						,	_lowerCAmelCase=True						,	_lowerCAmelCase=True						,	_lowerCAmelCase=37						,	_lowerCAmelCase="gelu"						,	_lowerCAmelCase=10						,	_lowerCAmelCase=0.02						,	_lowerCAmelCase=3						,	_lowerCAmelCase=None						,	):
										lowerCamelCase__      =							parent
										lowerCamelCase__      =							batch_size
										lowerCamelCase__      =							image_size
										lowerCamelCase__      =							num_channels
										lowerCamelCase__      =							num_stages
										lowerCamelCase__      =							hidden_sizes
										lowerCamelCase__      =							depths
										lowerCamelCase__      =							is_training
										lowerCamelCase__      =							use_labels
										lowerCamelCase__      =							intermediate_size
										lowerCamelCase__      =							hidden_act
										lowerCamelCase__      =							type_sequence_label_size
										lowerCamelCase__      =							initializer_range
										lowerCamelCase__      =							scope





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

										lowerCamelCase__      =							None
										if self.use_labels:
															lowerCamelCase__      =							ids_tensor([self.batch_size]						,	self.type_sequence_label_size						)

										lowerCamelCase__      =							self.get_config()

										return config, pixel_values, labels





					def        __magic_name__			(			self						):
										return ConvNextConfig(
										    num_channels=self.num_channels						,	hidden_sizes=self.hidden_sizes						,	depths=self.depths						,	num_stages=self.num_stages						,	hidden_act=self.hidden_act						,	is_decoder=_lowerCAmelCase						,	initializer_range=self.initializer_range						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							TFConvNextModel(config=_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	training=_lowerCAmelCase						)
										# expected last hidden states: B, C, H // 32, W // 32
										self.parent.assertEqual(
										    result.last_hidden_state.shape						,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							self.type_sequence_label_size
										lowerCamelCase__      =							TFConvNextForImageClassification(_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	labels=_lowerCAmelCase						,	training=_lowerCAmelCase						)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)						)






					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.prepare_config_and_inputs()
										lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__      =							config_and_inputs
										lowerCamelCase__      =							{"pixel_values": pixel_values}
										return config, inputs_dict

@require_tf
class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_				,							unittest.TestCase ):





					"""simple docstring"""


					A__								=     (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
					A__								=     (
					    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
					    if is_tf_available()
					    else {}
					)

					A__								=     False
					A__								=     False
					A__								=     False
					A__								=     False
					A__								=     False





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextModelTester(self						)
										lowerCamelCase__      =							ConfigTester(
										    self						,	config_class=_lowerCAmelCase						,	has_text_modality=_lowerCAmelCase						,	hidden_size=37						,	)





					@unittest.skip(reason="ConvNext does not use inputs_embeds"						)
					def        __magic_name__			(			self						):
										pass





					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0						,	reason="TF does not support backprop for grouped convolutions on CPU."						,	)
					@slow
					def        __magic_name__			(			self						):
										super().test_keras_fit()





					@unittest.skip(reason="ConvNext does not support input and output embeddings"						)
					def        __magic_name__			(			self						):
										pass





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)
															lowerCamelCase__      =							inspect.signature(model.call						)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															lowerCamelCase__      =							[*signature.parameters.keys()]

															lowerCamelCase__      =							["pixel_values"]
															self.assertListEqual(arg_names[:1]						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCAmelCase						)





					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0						,	reason="TF does not support backprop for grouped convolutions on CPU."						,	)
					def        __magic_name__			(			self						):
										super().test_dataset_conversion()





					def        __magic_name__			(			self						):
										def check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)

															lowerCamelCase__      =							model(**self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)						)
															lowerCamelCase__      =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															lowerCamelCase__      =							self.model_tester.num_stages
															self.assertEqual(len(_lowerCAmelCase						)						,	expected_num_stages + 1						)

															# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]						)						,	[self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,	)

										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							True
															check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															lowerCamelCase__      =							True

															check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										def check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase={}						):
															lowerCamelCase__      =							model(_lowerCAmelCase						,	return_dict=_lowerCAmelCase						,	**_lowerCAmelCase						)
															lowerCamelCase__      =							model(_lowerCAmelCase						,	return_dict=_lowerCAmelCase						,	**_lowerCAmelCase						).to_tuple()

															def recursive_check(_lowerCAmelCase						,	_lowerCAmelCase						):
																				if isinstance(_lowerCAmelCase						,	(List, Tuple)						):
																									for tuple_iterable_value, dict_iterable_value in zip(_lowerCAmelCase						,	_lowerCAmelCase						):
																														recursive_check(_lowerCAmelCase						,	_lowerCAmelCase						)
																				elif tuple_object is None:
																									return
																				else:
																									self.assertTrue(
																									    all(tf.equal(_lowerCAmelCase						,	_lowerCAmelCase						)						)						,	msg=(
																									        "Tuple and dict output are not equal. Difference:"
																									        F" {tf.math.reduce_max(tf.abs(tuple_object - dict_object						)						)}"
																									    )						,	)

																				recursive_check(_lowerCAmelCase						,	_lowerCAmelCase						)

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	{"output_hidden_states": True}						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	{"output_hidden_states": True}						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)






					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"						)
										self.assertIsNotNone(_lowerCAmelCase						)





def  __UpperCamelCase   (     )			->List[str]:
					lowerCamelCase__      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
					return image







@require_tf
@require_vision
class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					@cached_property
					def        __magic_name__			(			self						):
										return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"						) if is_vision_available() else None






					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"						)

										lowerCamelCase__      =							self.default_image_processor
										lowerCamelCase__      =							prepare_img()
										lowerCamelCase__      =							image_processor(images=_lowerCAmelCase						,	return_tensors="tf"						)

										# forward pass
										lowerCamelCase__      =							model(**_lowerCAmelCase						)

										# verify the logits
										lowerCamelCase__      =							tf.TensorShape((1, 1000)						)
										self.assertEqual(outputs.logits.shape						,	_lowerCAmelCase						)

										lowerCamelCase__      =							tf.constant([-0.0_260, -0.4_739, 0.1_911]						)

										tf.debugging.assert_near(outputs.logits[0, :3]						,	_lowerCAmelCase						,	atol=1E-4						)

