




'''simple docstring'''






import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
      import tensorflow as tf

      tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset


lowercase						:   Dict      						=							5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors




def 		a_(			lowerCamelCase   )					->Any:






       '''simple docstring'''




       return PTtoTFCommand(
           args.model_name ,					args.local_dir ,					args.max_error ,					args.new_weights ,					args.no_pr ,					args.push ,					args.extra_commit_description ,					args.override_model_class ,					)





class 	SCREAMING_SNAKE_CASE_		(							__SCREAMING_SNAKE_CASE					):



 @staticmethod
 def  SCREAMING_SNAKE_CASE(	A			):






        '''simple docstring'''




        lowercase	:		Optional[int]    =   parser.add_parser(
            '''pt-to-tf''' ,		help=(
                '''CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'''
                ''' Can also be used to validate existing weights without opening PRs, with --no-pr.'''
            ) ,		)
        train_parser.add_argument(
            '''--model-name''' ,		type=__UpperCAmelCase ,		required=__UpperCAmelCase ,		help='''The model name, including owner/organization, as seen on the hub.''' ,		)
        train_parser.add_argument(
            '''--local-dir''' ,		type=__UpperCAmelCase ,		default='''''' ,		help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}''' ,		)
        train_parser.add_argument(
            '''--max-error''' ,		type=__UpperCAmelCase ,		default=__UpperCAmelCase ,		help=(
                f"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
            ) ,		)
        train_parser.add_argument(
            '''--new-weights''' ,		action='''store_true''' ,		help='''Optional flag to create new TensorFlow weights, even if they already exist.''' ,		)
        train_parser.add_argument(
            '''--no-pr''' ,		action='''store_true''' ,		help='''Optional flag to NOT open a PR with converted weights.'''			)
        train_parser.add_argument(
            '''--push''' ,		action='''store_true''' ,		help='''Optional flag to push the weights directly to `main` (requires permissions)''' ,		)
        train_parser.add_argument(
            '''--extra-commit-description''' ,		type=__UpperCAmelCase ,		default='''''' ,		help='''Optional additional commit description to use when opening a PR (e.g. to tag the owner).''' ,		)
        train_parser.add_argument(
            '''--override-model-class''' ,		type=__UpperCAmelCase ,		default=__UpperCAmelCase ,		help='''If you think you know better than the auto-detector, you can specify the model class here. '''
            '''Can be either an AutoModel class or a specific model class like BertForSequenceClassification.''' ,		)
        train_parser.set_defaults(func=__UpperCAmelCase			)



 @staticmethod
 def  SCREAMING_SNAKE_CASE(	A ,		A			):






        '''simple docstring'''




        lowercase	:		int    =   set(pt_outputs.keys()			)
        lowercase	:		str    =   set(tf_outputs.keys()			)
        if pt_out_attrs != tf_out_attrs:
               raise ValueError(
                   f"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
                   f""" {tf_out_attrs})"""			)

        # 2. For each output attribute, computes the difference
        def _find_pt_tf_differences(A ,		A ,		A ,		A=""			):
               # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
               # recursivelly, keeping the name of the attribute.
               if isinstance(__UpperCAmelCase ,		torch.Tensor			):
                      lowercase	:		int    =   np.max(np.abs(pt_out.numpy() - tf_out.numpy()			)			)
                      lowercase	:		List[str]    =   tensor_difference
               else:
                      lowercase	:		Tuple    =   attr_name
                      for i, pt_item in enumerate(__UpperCAmelCase			):
                             # If it is a named attribute, we keep the name. Otherwise, just its index.
                             if isinstance(__UpperCAmelCase ,		__UpperCAmelCase			):
                                    lowercase	:		Any    =   root_name + pt_item
                                    lowercase	:		Union[str, Any]    =   tf_out[pt_item]
                                    lowercase	:		List[Any]    =   pt_out[pt_item]
                             else:
                                    lowercase	:		str    =   root_name + f"""[{i}]"""
                                    lowercase	:		List[str]    =   tf_out[i]
                             lowercase	:		int    =   _find_pt_tf_differences(__UpperCAmelCase ,		__UpperCAmelCase ,		__UpperCAmelCase ,		__UpperCAmelCase			)

               return differences

        return _find_pt_tf_differences(__UpperCAmelCase ,		__UpperCAmelCase ,		{}			)



 def __init__(	self ,		A ,		A ,		A ,		A ,		A ,		A ,		A ,		A ,		*A ,		):






        '''simple docstring'''




        lowercase	:		Optional[int]    =   logging.get_logger('''transformers-cli/pt_to_tf'''			)
        lowercase	:		Optional[int]    =   model_name
        lowercase	:		Optional[Any]    =   local_dir if local_dir else os.path.join('''/tmp''' ,		__UpperCAmelCase			)
        lowercase	:		List[str]    =   max_error
        lowercase	:		List[str]    =   new_weights
        lowercase	:		Tuple    =   no_pr
        lowercase	:		Optional[Any]    =   push
        lowercase	:		int    =   extra_commit_description
        lowercase	:		Tuple    =   override_model_class



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        def _get_audio_input():
               lowercase	:		str    =   load_dataset('''hf-internal-testing/librispeech_asr_dummy''' ,		'''clean''' ,		split='''validation'''			)
               lowercase	:		str    =   ds.sort('''id'''			).select(range(2			)			)[:2]["""audio"""]
               lowercase	:		int    =   [x["""array"""] for x in speech_samples]
               return raw_samples

        lowercase	:		Tuple    =   type(pt_model.config			)
        if model_config_class in PROCESSOR_MAPPING:
               lowercase	:		Optional[Any]    =   AutoProcessor.from_pretrained(self._local_dir			)
               if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
                      lowercase	:		Optional[Any]    =   processor.tokenizer.eos_token
        elif model_config_class in IMAGE_PROCESSOR_MAPPING:
               lowercase	:		Tuple    =   AutoImageProcessor.from_pretrained(self._local_dir			)
        elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
               lowercase	:		int    =   AutoFeatureExtractor.from_pretrained(self._local_dir			)
        elif model_config_class in TOKENIZER_MAPPING:
               lowercase	:		Optional[int]    =   AutoTokenizer.from_pretrained(self._local_dir			)
               if processor.pad_token is None:
                      lowercase	:		Optional[Any]    =   processor.eos_token
        else:
               raise ValueError(f"""Unknown data processing type (model config type: {model_config_class})"""			)

        lowercase	:		Tuple    =   set(inspect.signature(pt_model.forward			).parameters.keys()			)
        lowercase	:		Dict    =   {}
        if "input_ids" in model_forward_signature:
               processor_inputs.update(
                   {
                       '''text''': ['''Hi there!''', '''I am a batch with more than one row and different input lengths.'''],
                       '''padding''': True,
                       '''truncation''': True,
                   }			)
        if "pixel_values" in model_forward_signature:
               lowercase	:		Optional[Any]    =   load_dataset('''cifar10''' ,		'''plain_text''' ,		split='''test'''			)[:2]["""img"""]
               processor_inputs.update({'''images''': sample_images}			)
        if "input_features" in model_forward_signature:
               lowercase	:		Optional[Any]    =   inspect.signature(processor.feature_extractor			).parameters
               # Pad to the largest input length by default but take feature extractor default
               # padding value if it exists e.g. "max_length" and is not False or None
               if "padding" in feature_extractor_signature:
                      lowercase	:		Dict    =   feature_extractor_signature["""padding"""].default
                      if default_strategy is not False and default_strategy is not None:
                             lowercase	:		Optional[int]    =   default_strategy
                      else:
                             lowercase	:		Optional[int]    =   True
               else:
                      lowercase	:		Optional[int]    =   True
               processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': padding_strategy}			)
        if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
               processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': True}			)
        lowercase	:		Dict    =   processor(**__UpperCAmelCase ,		return_tensors='''pt'''			)
        lowercase	:		Optional[Any]    =   processor(**__UpperCAmelCase ,		return_tensors='''tf'''			)

        # Extra input requirements, in addition to the input modality
        if (
            config.is_encoder_decoder
            or (hasattr(__UpperCAmelCase ,		'''encoder'''			) and hasattr(__UpperCAmelCase ,		'''decoder'''			))
            or "decoder_input_ids" in tf_dummy_inputs
        ):
               lowercase	:		Union[str, Any]    =   np.asarray([[1], [1]] ,		dtype=__UpperCAmelCase			) * (pt_model.config.decoder_start_token_id or 0)
               pt_input.update({'''decoder_input_ids''': torch.tensor(__UpperCAmelCase			)}			)
               tf_input.update({'''decoder_input_ids''': tf.convert_to_tensor(__UpperCAmelCase			)}			)

        return pt_input, tf_input





 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        if version.parse(huggingface_hub.__version__			) < version.parse('''0.9.0'''			):
               raise ImportError(
                   '''The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'''
                   ''' installation.'''			)
        else:
               from huggingface_hub import Repository, create_commit
               from huggingface_hub._commit_api import CommitOperationAdd

        # Fetch remote data
        lowercase	:		Any    =   Repository(local_dir=self._local_dir ,		clone_from=self._model_name			)

        # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
        lowercase	:		str    =   AutoConfig.from_pretrained(self._local_dir			)
        lowercase	:		Dict    =   config.architectures
        if self._override_model_class is not None:
               if self._override_model_class.startswith('''TF'''			):
                      lowercase	:		Union[str, Any]    =   [self._override_model_class[2:]]
               else:
                      lowercase	:		Tuple    =   [self._override_model_class]
               try:
                      lowercase	:		Any    =   getattr(import_module('''transformers'''			) ,		architectures[0]			)
               except AttributeError:
                      raise ValueError(f"""Model class {self._override_model_class} not found in transformers."""			)
               try:
                      lowercase	:		int    =   getattr(import_module('''transformers'''			) ,		'''TF''' + architectures[0]			)
               except AttributeError:
                      raise ValueError(f"""TF model class TF{self._override_model_class} not found in transformers."""			)
        elif architectures is None:  # No architecture defined -- use auto classes
               lowercase	:		int    =   getattr(import_module('''transformers'''			) ,		'''AutoModel'''			)
               lowercase	:		List[str]    =   getattr(import_module('''transformers'''			) ,		'''TFAutoModel'''			)
               self._logger.warning('''No detected architecture, using AutoModel/TFAutoModel'''			)
        else:  # Architecture defined -- use it
               if len(__UpperCAmelCase			) > 1:
                      raise ValueError(f"""More than one architecture was found, aborting. (architectures = {architectures})"""			)
               self._logger.warning(f"""Detected architecture: {architectures[0]}"""			)
               lowercase	:		str    =   getattr(import_module('''transformers'''			) ,		architectures[0]			)
               try:
                      lowercase	:		Optional[Any]    =   getattr(import_module('''transformers'''			) ,		'''TF''' + architectures[0]			)
               except AttributeError:
                      raise AttributeError(f"""The TensorFlow equivalent of {architectures[0]} doesn\'t exist in transformers."""			)

        # Check the TF dummy inputs to see what keys we need in the forward pass
        lowercase	:		List[Any]    =   tf_class.from_config(__UpperCAmelCase			)
        lowercase	:		int    =   tf_from_pt_model.dummy_inputs

        del tf_from_pt_model  # Try to keep only one model in memory at a time

        # Load the model and get some basic inputs
        lowercase	:		str    =   pt_class.from_pretrained(self._local_dir			)
        pt_model.eval()

        lowercase	:		Optional[Any]    =   self.get_inputs(__UpperCAmelCase ,		__UpperCAmelCase ,		__UpperCAmelCase			)

        with torch.no_grad():
               lowercase	:		Dict    =   pt_model(**__UpperCAmelCase ,		output_hidden_states=__UpperCAmelCase			)
        del pt_model  # will no longer be used, and may have a large memory footprint

        lowercase	:		List[str]    =   tf_class.from_pretrained(self._local_dir ,		from_pt=__UpperCAmelCase			)
        lowercase	:		Tuple    =   tf_from_pt_model(**__UpperCAmelCase ,		output_hidden_states=__UpperCAmelCase ,		training=__UpperCAmelCase			)

        # Confirms that cross loading PT weights into TF worked.
        lowercase	:		Tuple    =   self.find_pt_tf_differences(__UpperCAmelCase ,		__UpperCAmelCase			)
        lowercase	:		Tuple    =   {k: v for k, v in crossload_differences.items() if """hidden""" not in k}
        lowercase	:		Union[str, Any]    =   {k: v for k, v in crossload_differences.items() if """hidden""" in k}
        if len(__UpperCAmelCase			) == 0 and architectures is not None:
               raise ValueError(
                   f"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
                   ''' output was found. All outputs start with \'hidden\''''			)
        lowercase	:		Tuple    =   max(output_differences.values()			) if output_differences else 0.0
        lowercase	:		str    =   max(hidden_differences.values()			)
        if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
               raise ValueError(
                   '''The cross-loaded TensorFlow model has different outputs, something went wrong!\n'''
                   + f"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
                   + '''\n'''.join([f"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]			)
                   + f"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
                   + '''\n'''.join([f"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]			)			)

        # Save the weights in a TF format (if needed) and confirms that the results are still good
        lowercase	:		List[str]    =   os.path.join(self._local_dir ,		__UpperCAmelCase			)
        lowercase	:		Optional[Any]    =   os.path.join(self._local_dir ,		__UpperCAmelCase			)
        if (not os.path.exists(__UpperCAmelCase			) and not os.path.exists(__UpperCAmelCase			)) or self._new_weights:
               tf_from_pt_model.save_pretrained(self._local_dir			)
        del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

        lowercase	:		Union[str, Any]    =   tf_class.from_pretrained(self._local_dir			)
        lowercase	:		List[Any]    =   tf_model(**__UpperCAmelCase ,		output_hidden_states=__UpperCAmelCase			)

        lowercase	:		Tuple    =   self.find_pt_tf_differences(__UpperCAmelCase ,		__UpperCAmelCase			)
        lowercase	:		Any    =   {k: v for k, v in conversion_differences.items() if """hidden""" not in k}
        lowercase	:		Any    =   {k: v for k, v in conversion_differences.items() if """hidden""" in k}
        if len(__UpperCAmelCase			) == 0 and architectures is not None:
               raise ValueError(
                   f"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
                   ''' output was found. All outputs start with \'hidden\''''			)
        lowercase	:		List[Any]    =   max(output_differences.values()			) if output_differences else 0.0
        lowercase	:		Optional[Any]    =   max(hidden_differences.values()			)
        if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
               raise ValueError(
                   '''The converted TensorFlow model has different outputs, something went wrong!\n'''
                   + f"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
                   + '''\n'''.join([f"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]			)
                   + f"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
                   + '''\n'''.join([f"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]			)			)

        lowercase	:		int    =   """Update TF weights""" if self._new_weights else """Add TF weights"""
        if self._push:
               repo.git_add(auto_lfs_track=__UpperCAmelCase			)
               repo.git_commit(__UpperCAmelCase			)
               repo.git_push(blocking=__UpperCAmelCase			)  # this prints a progress bar with the upload
               self._logger.warning(f"""TF weights pushed into {self._model_name}"""			)
        elif not self._no_pr:
               self._logger.warning('''Uploading the weights into a new PR...'''			)
               lowercase	:		Tuple    =   (
                   """Model converted by the [`transformers`' `pt_to_tf`"""
                   """ CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). """
                   """All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"""
                   f"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
                   f"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
                   f"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
                   f"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
               )
               if self._max_error > MAX_ERROR:
                      commit_descrition += (
                          f"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
                      )
               if self._extra_commit_description:
                      commit_descrition += "\n\n" + self._extra_commit_description

               # sharded model -> adds all related files (index and .h5 shards)
               if os.path.exists(__UpperCAmelCase			):
                      lowercase	:		Dict    =   [
                          CommitOperationAdd(path_in_repo=__UpperCAmelCase ,		path_or_fileobj=__UpperCAmelCase			)
                      ]
                      for shard_path in tf.io.gfile.glob(self._local_dir + '''/tf_model-*.h5'''			):
                             operations += [
                                 CommitOperationAdd(path_in_repo=os.path.basename(__UpperCAmelCase			) ,		path_or_fileobj=__UpperCAmelCase			)
                             ]
               else:
                      lowercase	:		Optional[Any]    =   [CommitOperationAdd(path_in_repo=__UpperCAmelCase ,		path_or_fileobj=__UpperCAmelCase			)]

               lowercase	:		Optional[int]    =   create_commit(
                   repo_id=self._model_name ,		operations=__UpperCAmelCase ,		commit_message=__UpperCAmelCase ,		commit_description=__UpperCAmelCase ,		repo_type='''model''' ,		create_pr=__UpperCAmelCase ,		).pr_url
               self._logger.warning(f"""PR open in {hub_pr_url}"""			)









'''simple docstring'''
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 _A	:





		def __init__(  self			,							__UpperCAmelCase			,							__UpperCAmelCase=13			,							__UpperCAmelCase=32			,							__UpperCAmelCase=3			,							__UpperCAmelCase=4			,							__UpperCAmelCase=[10, 20, 30, 40]			,							__UpperCAmelCase=[2, 2, 3, 2]			,							__UpperCAmelCase=True			,							__UpperCAmelCase=True			,							__UpperCAmelCase=37			,							__UpperCAmelCase="gelu"			,							__UpperCAmelCase=10			,							__UpperCAmelCase=0.02			,							__UpperCAmelCase=3			,							__UpperCAmelCase=None			,							)							->  Any:

					'''simple docstring'''
					__UpperCAmelCase :   Optional[int]    					=  parent
					__UpperCAmelCase :   Optional[Any]    					=  batch_size
					__UpperCAmelCase :   List[str]    					=  image_size
					__UpperCAmelCase :   Optional[Any]    					=  num_channels
					__UpperCAmelCase :   int    					=  num_stages
					__UpperCAmelCase :   List[Any]    					=  hidden_sizes
					__UpperCAmelCase :   Optional[Any]    					=  depths
					__UpperCAmelCase :   List[str]    					=  is_training
					__UpperCAmelCase :   Tuple    					=  use_labels
					__UpperCAmelCase :   Optional[Any]    					=  intermediate_size
					__UpperCAmelCase :   Dict    					=  hidden_act
					__UpperCAmelCase :   List[str]    					=  type_sequence_label_size
					__UpperCAmelCase :   Union[str, Any]    					=  initializer_range
					__UpperCAmelCase :   Optional[Any]    					=  scope





		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   List[Any]    					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

					__UpperCAmelCase :   str    					=  None
					if self.use_labels:
								__UpperCAmelCase :   Optional[Any]    					=  ids_tensor([self.batch_size]			,							self.type_sequence_label_size					)

					__UpperCAmelCase :   Optional[int]    					=  self.get_config()

					return config, pixel_values, labels





		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					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=__UpperCAmelCase			,							initializer_range=self.initializer_range			,							)





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)							->  List[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   List[Any]    					=  TFConvNextModel(config=__UpperCAmelCase					)
					__UpperCAmelCase :   List[Any]    					=  model(__UpperCAmelCase			,							training=__UpperCAmelCase					)
					# 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 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)							->  Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase :   Optional[int]    					=  self.type_sequence_label_size
					__UpperCAmelCase :   List[Any]    					=  TFConvNextForImageClassification(__UpperCAmelCase					)
					__UpperCAmelCase :   List[str]    					=  model(__UpperCAmelCase			,							labels=__UpperCAmelCase			,							training=__UpperCAmelCase					)
					self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)					)





		def 				__A							(  self					)							->  Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase :   List[str]    					=  self.prepare_config_and_inputs()
					__UpperCAmelCase					,	__UpperCAmelCase					,	__UpperCAmelCase :   Optional[int]    					=  config_and_inputs
					__UpperCAmelCase :   Union[str, Any]    					=  {"""pixel_values""": pixel_values}
					return config, inputs_dict



@require_tf
class _A	(     __SCREAMING_SNAKE_CASE						,					__SCREAMING_SNAKE_CASE						,					unittest.TestCase     ):
		_SCREAMING_SNAKE_CASE       :     Dict       =      (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
		_SCREAMING_SNAKE_CASE       :     Optional[Any]       =      (
		    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
		    if is_tf_available()
		    else {}
		)

		_SCREAMING_SNAKE_CASE       :     Tuple       =      False
		_SCREAMING_SNAKE_CASE       :     Tuple       =      False
		_SCREAMING_SNAKE_CASE       :     Union[str, Any]       =      False
		_SCREAMING_SNAKE_CASE       :     Optional[Any]       =      False
		_SCREAMING_SNAKE_CASE       :     str       =      False





		def 				__A							(  self					)							->  List[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   Dict    					=  TFConvNextModelTester(self					)
					__UpperCAmelCase :   Optional[int]    					=  ConfigTester(
					    self			,							config_class=__UpperCAmelCase			,							has_text_modality=__UpperCAmelCase			,							hidden_size=37			,							)





		@unittest.skip(reason="""ConvNext does not use inputs_embeds"""					)
		def 				__A							(  self					)							->  str:

					'''simple docstring'''
					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 				__A							(  self					)							->  Optional[int]:

					'''simple docstring'''
					super().test_keras_fit()





		@unittest.skip(reason="""ConvNext does not support input and output embeddings"""					)
		def 				__A							(  self					)							->  Dict:

					'''simple docstring'''
					pass





		def 				__A							(  self					)							->  str:

					'''simple docstring'''
					__UpperCAmelCase					,	__UpperCAmelCase :   List[str]    					=  self.model_tester.prepare_config_and_inputs_for_common()

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

								__UpperCAmelCase :   Tuple    					=  ["""pixel_values"""]
								self.assertListEqual(arg_names[:1]			,							__UpperCAmelCase					)





		def 				__A							(  self					)							->  Dict:

					'''simple docstring'''
					__UpperCAmelCase :   Dict    					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__UpperCAmelCase					)





		@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 				__A							(  self					)							->  Union[str, Any]:

					'''simple docstring'''
					super().test_dataset_conversion()





		def 				__A							(  self					)							->  Optional[int]:

					'''simple docstring'''
					def check_hidden_states_output(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					):
								__UpperCAmelCase :   Optional[int]    					=  model_class(__UpperCAmelCase					)

								__UpperCAmelCase :   str    					=  model(**self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase					)					)
								__UpperCAmelCase :   List[str]    					=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

								__UpperCAmelCase :   Optional[int]    					=  self.model_tester.num_stages
								self.assertEqual(len(__UpperCAmelCase					)			,							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]			,							)

					__UpperCAmelCase					,	__UpperCAmelCase :   Union[str, Any]    					=  self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__UpperCAmelCase :   List[str]    					=  True
								check_hidden_states_output(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								__UpperCAmelCase :   Optional[Any]    					=  True

								check_hidden_states_output(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)





		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase					,	__UpperCAmelCase :   Any    					=  self.model_tester.prepare_config_and_inputs_for_common()

					def check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase={}					):
								__UpperCAmelCase :   Optional[Any]    					=  model(__UpperCAmelCase			,							return_dict=__UpperCAmelCase			,							**__UpperCAmelCase					)
								__UpperCAmelCase :   Optional[int]    					=  model(__UpperCAmelCase			,							return_dict=__UpperCAmelCase			,							**__UpperCAmelCase					).to_tuple()

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

											recursive_check(__UpperCAmelCase			,							__UpperCAmelCase					)

					for model_class in self.all_model_classes:
								__UpperCAmelCase :   Dict    					=  model_class(__UpperCAmelCase					)

								__UpperCAmelCase :   Optional[int]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase					)
								__UpperCAmelCase :   Tuple    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase					)
								check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)

								__UpperCAmelCase :   Union[str, Any]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase			,							return_labels=__UpperCAmelCase					)
								__UpperCAmelCase :   Union[str, Any]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase			,							return_labels=__UpperCAmelCase					)
								check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)

								__UpperCAmelCase :   Optional[Any]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase					)
								__UpperCAmelCase :   List[str]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase					)
								check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase			,							{"""output_hidden_states""": True}					)

								__UpperCAmelCase :   List[Any]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase			,							return_labels=__UpperCAmelCase					)
								__UpperCAmelCase :   Dict    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase			,							return_labels=__UpperCAmelCase					)
								check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase			,							{"""output_hidden_states""": True}					)





		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase :   int    					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase					)





		@slow
		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase :   Union[str, Any]    					=  TFConvNextModel.from_pretrained("""facebook/convnext-tiny-224"""					)
					self.assertIsNotNone(__UpperCAmelCase					)


def 	lowercase_						( ):

			"""simple docstring"""





			__UpperCAmelCase :   str    					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
			return image






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





		@cached_property
		def 				__A							(  self					)							->  Any:

					'''simple docstring'''
					return ConvNextImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""					) if is_vision_available() else None





		@slow
		def 				__A							(  self					)							->  int:

					'''simple docstring'''
					__UpperCAmelCase :   Tuple    					=  TFConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""					)

					__UpperCAmelCase :   List[Any]    					=  self.default_image_processor
					__UpperCAmelCase :   int    					=  prepare_img()
					__UpperCAmelCase :   Optional[Any]    					=  image_processor(images=__UpperCAmelCase			,							return_tensors="""tf"""					)

					# forward pass
					__UpperCAmelCase :   List[Any]    					=  model(**__UpperCAmelCase					)

					# verify the logits
					__UpperCAmelCase :   List[str]    					=  tf.TensorShape((1, 1_000)					)
					self.assertEqual(outputs.logits.shape			,							__UpperCAmelCase					)

					__UpperCAmelCase :   str    					=  tf.constant([-0.0260, -0.4739, 0.1911]					)

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




