
"""simple docstring"""





import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
_a									=				logging.get_logger(__name__)

_a									=				{
    'post_extract_proj': 'feature_projection.projection',
    'models.0.layer_norm': 'feature_projection.layer_norm',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
_a									=				[
    'lm_head',
]







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   for attribute in key.split("."    ):
      UpperCAmelCase_   :   Any     	=       getattr(__lowerCamelCase,						__lowerCamelCase    )

   if weight_type is not None:
      UpperCAmelCase_   :   int     	=       getattr(__lowerCamelCase,						__lowerCamelCase    ).shape
   else:
      UpperCAmelCase_   :   Union[str, Any]     	=       hf_pointer.shape

   if hf_shape != value.shape:
      raise ValueError(
          f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
          f""" {value.shape} for {full_name}"""    )

   if weight_type == "weight":
      UpperCAmelCase_   :   Tuple     	=       value
   elif weight_type == "weight_g":
      UpperCAmelCase_   :   Optional[int]     	=       value
   elif weight_type == "weight_v":
      UpperCAmelCase_   :   str     	=       value
   elif weight_type == "bias":
      UpperCAmelCase_   :   Any     	=       value
   else:
      UpperCAmelCase_   :   List[str]     	=       value

   logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   List[Any]     	=       []
   UpperCAmelCase_   :   Any     	=       fairseq_model.state_dict()

   if not is_headless:
      UpperCAmelCase_   :   Any     	=       hf_model.dataavec_audio.feature_extractor
      UpperCAmelCase_   :   int     	=       hf_model.dataavec_audio.encoder.pos_conv_embed

   else:
      UpperCAmelCase_   :   Optional[Any]     	=       hf_model.feature_extractor
      UpperCAmelCase_   :   List[Any]     	=       hf_model.encoder.pos_conv_embed

   for name, value in fairseq_dict.items():
      UpperCAmelCase_   :   Any     	=       False
      if "conv_layers" in name:
         load_conv_layer(
             __lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						)
         UpperCAmelCase_   :   Tuple     	=       True
      elif "pos_conv" in name:
         load_pos_conv_layer(
             __lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						)
         UpperCAmelCase_   :   Dict     	=       True
      else:
         for key, mapped_key in MAPPING.items():
            if not is_headless:
               UpperCAmelCase_   :   List[Any]     	=       "data2vec_audio." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
            if key in name or key.split("w2v_model."    )[-1] == name.split("."    )[0]:
               UpperCAmelCase_   :   Optional[Any]     	=       True
               if "*" in mapped_key:
                  UpperCAmelCase_   :   Any     	=       name.split(__lowerCamelCase    )[0].split("."    )[-2]
                  UpperCAmelCase_   :   Tuple     	=       mapped_key.replace("*",						__lowerCamelCase    )
               if "weight_g" in name:
                  UpperCAmelCase_   :   Optional[int]     	=       "weight_g"
               elif "weight_v" in name:
                  UpperCAmelCase_   :   str     	=       "weight_v"
               elif "bias" in name:
                  UpperCAmelCase_   :   Optional[int]     	=       "bias"
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  UpperCAmelCase_   :   Any     	=       "weight"
               else:
                  UpperCAmelCase_   :   Union[str, Any]     	=       None
               set_recursively(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
            continue
      if not is_used:
         unused_weights.append(__lowerCamelCase    )

   logger.warning(f"""Unused weights: {unused_weights}"""    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       path.split("."    )
   return reduce(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Any     	=       access_by_string(__lowerCamelCase,						__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       hf_weight.data

   if fsq_value.shape != hf_value.shape:
      raise ValueError(f"""{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."""    )
   UpperCAmelCase_   :   str     	=       fsq_value
   logger.info(f"""{full_name} was correctly initialized from {hf_weight_path}."""    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   str     	=       full_name.split("conv_layers."    )[-1]
   UpperCAmelCase_   :   Union[str, Any]     	=       name.split("."    )
   UpperCAmelCase_   :   Tuple     	=       int(items[0]    )
   UpperCAmelCase_   :   Union[str, Any]     	=       int(items[1]    )

   UpperCAmelCase_   :   Optional[Any]     	=       name.split("."    )[-1]
   if type_id == 0:
      UpperCAmelCase_   :   List[Any]     	=       "conv"
   elif type_id == 2:
      UpperCAmelCase_   :   str     	=       "layer_norm"
   else:
      unused_weights.append(__lowerCamelCase    )
      return

   set_weights(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						f"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   str     	=       full_name.split("pos_conv."    )[-1]
   UpperCAmelCase_   :   Any     	=       name.split("."    )
   UpperCAmelCase_   :   List[Any]     	=       int(items[0]    )
   UpperCAmelCase_   :   str     	=       int(items[1]    )

   UpperCAmelCase_   :   Tuple     	=       name.split("."    )[-1]
   if type_id != 0:
      unused_weights.append(__lowerCamelCase    )
      return
   else:
      UpperCAmelCase_   :   Tuple     	=       "conv"

   set_weights(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						f"""layers.{layer_id}.{layer_type}.{weight_type}"""    )







@torch.no_grad()
def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase=None,						__lowerCamelCase=None,						__lowerCamelCase=True    ):
   if config_path is not None:
      UpperCAmelCase_   :   Dict     	=       DataaVecAudioConfig.from_pretrained(__lowerCamelCase    )
   else:
      UpperCAmelCase_   :   Dict     	=       DataaVecAudioConfig()

   if not is_finetuned:
      # Modify final_proj layer name
      UpperCAmelCase_   :   int     	=       DataaVecAudioModel(__lowerCamelCase    )
      UpperCAmelCase_   :   List[str]     	=       os.path.dirname(__lowerCamelCase    )

      UpperCAmelCase_   :   Optional[Any]     	=       torch.load(__lowerCamelCase    )
      UpperCAmelCase_   :   List[str]     	=       state_dict["model"].pop("final_proj.0.weight"    )
      UpperCAmelCase_   :   List[str]     	=       state_dict["model"].pop("final_proj.0.bias"    )
      UpperCAmelCase_   :   List[str]     	=       os.path.join(__lowerCamelCase,						"converted.pt"    )
      torch.save(__lowerCamelCase,						__lowerCamelCase    )
   else:
      UpperCAmelCase_   :   str     	=       DataaVecAudioForCTC(__lowerCamelCase    )
      UpperCAmelCase_   :   str     	=       checkpoint_path

   def load_dataavec(__lowerCamelCase    ):
      UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       fairseq.checkpoint_utils.load_model_ensemble_and_task([path]    )
      return model[0].eval()

   UpperCAmelCase_   :   Union[str, Any]     	=       load_dataavec(__lowerCamelCase    )

   recursively_load_weights(__lowerCamelCase,						__lowerCamelCase,						not is_finetuned    )

   UpperCAmelCase_   :   int     	=       WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"    )

   UpperCAmelCase_   :   Any     	=       load_dataset("patrickvonplaten/librispeech_asr_dummy",						"clean",						split="validation"    )
   UpperCAmelCase_   :   Optional[Any]     	=       [x["array"] for x in ds[:4]["audio"]]

   UpperCAmelCase_   :   str     	=       processor(__lowerCamelCase,						return_tensors="pt",						padding=__lowerCamelCase    )

   UpperCAmelCase_   :   List[str]     	=       inputs.input_values
   UpperCAmelCase_   :   Union[str, Any]     	=       inputs.attention_mask
   #    input_values = inputs.input_values[:, :-1]
   #    attention_mask = inputs.attention_mask[:, :-1]

   hf_wavavec.eval()
   model.eval()
   if is_finetuned:
      UpperCAmelCase_   :   List[str]     	=       model(source=__lowerCamelCase,						padding_mask=(1 - attention_mask),						mask=__lowerCamelCase,						features_only=__lowerCamelCase    )[
          "encoder_out"
      ].transpose(0,						1    )
      UpperCAmelCase_   :   Dict     	=       hf_wavavec(__lowerCamelCase,						attention_mask=__lowerCamelCase    )["logits"]

      UpperCAmelCase_   :   int     	=       torch.argmax(__lowerCamelCase,						dim=-1    )
      UpperCAmelCase_   :   Any     	=       processor.batch_decode(__lowerCamelCase    )

      print(f"""Expected Output: {ds[:4]["text"]}, Pred: {output_string}"""    )
   else:
      UpperCAmelCase_   :   Tuple     	=       model(source=__lowerCamelCase,						padding_mask=(1 - attention_mask),						mask=__lowerCamelCase,						features_only=__lowerCamelCase    )[
          "layer_results"
      ][-1][0].transpose(0,						1    )
      UpperCAmelCase_   :   Union[str, Any]     	=       hf_wavavec(__lowerCamelCase,						attention_mask=__lowerCamelCase    )["last_hidden_state"]

   print(our_output.shape,						their_output.shape    )
   UpperCAmelCase_   :   Tuple     	=       torch.max(torch.abs(our_output - their_output    )    ).item()
   print(f"""max_absolute_diff = {max_absolute_diff}"""    )  # ~ 1e-7
   UpperCAmelCase_   :   Optional[Any]     	=       torch.allclose(__lowerCamelCase,						__lowerCamelCase,						atol=1E-3    )
   print("Do both models output the same tensors?",						"🔥" if success else "💩"    )
   if not success:
      raise Exception("Something went wRoNg"    )

   hf_wavavec.save_pretrained(__lowerCamelCase    )

   if is_finetuned:
      processor.save_pretrained(__lowerCamelCase    )
   else:
      processor.feature_extractor.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
    _a									=				argparse.ArgumentParser()
    parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
    parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
    parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
    parser.add_argument(
        '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
    )
    _a									=				parser.parse_args()
    convert_wavaveca_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )










"""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				:    Optional[int]    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    int=1_3    ,     __lowerCamelCase				:    Any=3_2    ,     __lowerCamelCase				:    Dict=3    ,     __lowerCamelCase				:    List[Any]=4    ,     __lowerCamelCase				:    Union[str, Any]=[1_0, 2_0, 3_0, 4_0]    ,     __lowerCamelCase				:    int=[2, 2, 3, 2]    ,     __lowerCamelCase				:    str=True    ,     __lowerCamelCase				:    List[str]=True    ,     __lowerCamelCase				:    List[Any]=3_7    ,     __lowerCamelCase				:    Optional[Any]="gelu"    ,     __lowerCamelCase				:    str=1_0    ,     __lowerCamelCase				:    int=0.02    ,     __lowerCamelCase				:    Optional[int]=3    ,     __lowerCamelCase				:    Any=None    ,     )					->       List[Any]:
										__magic_name__      =  parent
										__magic_name__      =  batch_size
										__magic_name__      =  image_size
										__magic_name__      =  num_channels
										__magic_name__      =  num_stages
										__magic_name__      =  hidden_sizes
										__magic_name__      =  depths
										__magic_name__      =  is_training
										__magic_name__      =  use_labels
										__magic_name__      =  intermediate_size
										__magic_name__      =  hidden_act
										__magic_name__      =  type_sequence_label_size
										__magic_name__      =  initializer_range
										__magic_name__      =  scope







				def      _snake_case       (				self				:    Optional[Any]						)					->       int:
										__magic_name__      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

										__magic_name__      =  self.get_config()

										return config, pixel_values, labels







				def      _snake_case       (				self				:    Optional[int]						)					->       Union[str, Any]:
										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      _snake_case       (				self				:    List[Any]    ,     __lowerCamelCase				:    List[str]    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Optional[Any]						)					->       List[str]:
										__magic_name__      =  TFConvNextModel(config=__lowerCamelCase						)
										__magic_name__      =  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 // 3_2, self.image_size // 3_2)    ,     )







				def      _snake_case       (				self				:    List[Any]    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    List[str]						)					->       int:
										__magic_name__      =  self.type_sequence_label_size
										__magic_name__      =  TFConvNextForImageClassification(__lowerCamelCase						)
										__magic_name__      =  model(__lowerCamelCase    ,     labels=__lowerCamelCase    ,     training=__lowerCamelCase						)
										self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.type_sequence_label_size)						)







				def      _snake_case       (				self				:    List[str]						)					->       str:
										__magic_name__      =  self.prepare_config_and_inputs()
										__magic_name__   ,     __magic_name__   ,     __magic_name__      =  config_and_inputs
										__magic_name__      =  {"pixel_values": pixel_values}
										return config, inputs_dict





@require_tf
class     A_				(   snake_case_       ,				snake_case_       ,				unittest.TestCase	):
				UpperCAmelCase__										=					(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
				UpperCAmelCase__										=					(
				    {'''feature-extraction''': TFConvNextModel, '''image-classification''': TFConvNextForImageClassification}
				    if is_tf_available()
				    else {}
				)

				UpperCAmelCase__										=					False
				UpperCAmelCase__										=					False
				UpperCAmelCase__										=					False
				UpperCAmelCase__										=					False
				UpperCAmelCase__										=					False







				def      _snake_case       (				self				:    str						)					->       str:
										__magic_name__      =  TFConvNextModelTester(self						)
										__magic_name__      =  ConfigTester(
										    self    ,     config_class=__lowerCamelCase    ,     has_text_modality=__lowerCamelCase    ,     hidden_size=3_7    ,     )







				@unittest.skip(reason="ConvNext does not use inputs_embeds"						)
				def      _snake_case       (				self				:    Dict						)					->       Dict:
										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      _snake_case       (				self				:    List[str]						)					->       Tuple:
										super().test_keras_fit()







				@unittest.skip(reason="ConvNext does not support input and output embeddings"						)
				def      _snake_case       (				self				:    str						)					->       Tuple:
										pass







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Optional[int]:
										__magic_name__   ,     __magic_name__      =  self.model_tester.prepare_config_and_inputs_for_common()

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

																__magic_name__      =  ["pixel_values"]
																self.assertListEqual(arg_names[:1]    ,     __lowerCamelCase						)







				def      _snake_case       (				self				:    Dict						)					->       Optional[int]:
										__magic_name__      =  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      _snake_case       (				self				:    Optional[int]						)					->       Tuple:
										super().test_dataset_conversion()







				def      _snake_case       (				self				:    int						)					->       List[str]:
										def check_hidden_states_output(__lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    List[str]						):
																__magic_name__      =  model_class(__lowerCamelCase						)

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

																__magic_name__      =  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]    ,     )

										__magic_name__   ,     __magic_name__      =  self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																__magic_name__      =  True
																check_hidden_states_output(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)

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

																check_hidden_states_output(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)







				def      _snake_case       (				self				:    List[str]						)					->       Dict:
										__magic_name__   ,     __magic_name__      =  self.model_tester.prepare_config_and_inputs_for_common()

										def check_equivalence(__lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Any={}						):
																__magic_name__      =  model(__lowerCamelCase    ,     return_dict=__lowerCamelCase    ,     **__lowerCamelCase						)
																__magic_name__      =  model(__lowerCamelCase    ,     return_dict=__lowerCamelCase    ,     **__lowerCamelCase						).to_tuple()

																def recursive_check(__lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Union[str, Any]						):
																						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:
																__magic_name__      =  model_class(__lowerCamelCase						)

																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase						)
																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase						)
																check_equivalence(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)

																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase    ,     return_labels=__lowerCamelCase						)
																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase    ,     return_labels=__lowerCamelCase						)
																check_equivalence(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)

																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase						)
																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase						)
																check_equivalence(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     {"output_hidden_states": True}						)

																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase    ,     return_labels=__lowerCamelCase						)
																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase    ,     return_labels=__lowerCamelCase						)
																check_equivalence(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     {"output_hidden_states": True}						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Dict:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase						)







				@slow
				def      _snake_case       (				self				:    List[str]						)					->       Tuple:
										__magic_name__      =  TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"						)
										self.assertIsNotNone(__lowerCamelCase						)






def _lowerCAmelCase						(	):

						'''simple docstring'''






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


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







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







				@slow
				def      _snake_case       (				self				:    Any						)					->       str:
										__magic_name__      =  TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"						)

										__magic_name__      =  self.default_image_processor
										__magic_name__      =  prepare_img()
										__magic_name__      =  image_processor(images=__lowerCamelCase    ,     return_tensors="tf"						)

										# forward pass
										__magic_name__      =  model(**__lowerCamelCase						)

										# verify the logits
										__magic_name__      =  tf.TensorShape((1, 1_0_0_0)						)
										self.assertEqual(outputs.logits.shape    ,     __lowerCamelCase						)

										__magic_name__      =  tf.constant([-0.0260, -0.4739, 0.1911]						)

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



