




import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa

class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= ["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]




					def __init__(   self:		List[Any],     _lowercase:		Any):


									'''simple docstring'''


									assert Path(hparams.data_dir).exists()
									__lowerCAmelCase					=  Path(hparams.output_dir)
									self.output_dir.mkdir(exist_ok=_lowercase)

									__lowerCAmelCase					=  self.output_dir.joinpath("""student""")

									__lowerCAmelCase					=  str(_lowercase)  # Tell lightning we are training the student
									__lowerCAmelCase					=  AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher).eval()
									use_task_specific_params(_lowercase,     hparams.task)  # We copy good generation parameters to student by default
									if hparams.student is not None:
													__lowerCAmelCase					=  AutoModelForSeqaSeqLM.from_pretrained(hparams.student)
													use_task_specific_params(_lowercase,     hparams.task)
													__lowerCAmelCase	,				__lowerCAmelCase					=  None, None
									else:
													__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  create_student_by_copying_alternating_layers(
													    _lowercase,     e=hparams.student_encoder_layers,     d=hparams.student_decoder_layers,     save_path=_lowercase)

									if hparams.length_penalty != -1:
													__lowerCAmelCase					=  hparams.length_penalty
									__lowerCAmelCase					=  hparams.teacher  # Use teacher's tokenizer
									super().__init__(_lowercase,     model=_lowercase,     config=student.config)
									assert student.config.model_type == teacher.config.model_type, (
									    f'''teacher, student model types should be the same, got {student.config.model_type} !='''
									    f''' {teacher.config.model_type}'''
									)

									if student.config.model_type == "t5":
													__lowerCAmelCase					=  len(student.get_encoder().block)
													__lowerCAmelCase					=  len(student.get_decoder().block)
													__lowerCAmelCase					=  len(teacher.get_encoder().block)
													__lowerCAmelCase					=  len(teacher.get_decoder().block)
									else:
													__lowerCAmelCase					=  student.config.encoder_layers
													__lowerCAmelCase					=  student.config.decoder_layers
													__lowerCAmelCase					=  teacher.config.encoder_layers
													__lowerCAmelCase					=  teacher.config.decoder_layers

									__lowerCAmelCase					=  not (hparams.student is None or hparams.teacher == hparams.student)
									__lowerCAmelCase					=  (not self.different_base_models) and hparams.alpha_hid > 0
									__lowerCAmelCase					=  self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
									# self.different_encoder determines whether we need to run the teacher encoder
									__lowerCAmelCase					=  teacher
									freeze_params(self.teacher)

									if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
													try:
																	del self.teacher.model.encoder
													except AttributeError:  # T5
																	del self.teacher.encoder

									if e_layer_ids is None:
													__lowerCAmelCase					=  list(range(_lowercase))
									if d_layer_ids is None:
													__lowerCAmelCase					=  list(range(_lowercase))

									__lowerCAmelCase	,				__lowerCAmelCase					=  e_layer_ids, d_layer_ids  # type: List[int], List[int]

									if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
													if hparams.supervise_forward:
																	__lowerCAmelCase					=  get_layers_to_supervise(
																	    n_student=len(self.e_layer_ids),     n_teacher=_lowercase)
																	__lowerCAmelCase					=  get_layers_to_supervise(
																	    n_student=len(self.d_layer_ids),     n_teacher=_lowercase)
													else:  # student layer should emulate hidden states of the teacher layer it was copied from
																	__lowerCAmelCase					=  self.e_layer_ids
																	__lowerCAmelCase					=  self.d_layer_ids
									else:
													__lowerCAmelCase					=  None
													__lowerCAmelCase					=  None

									__lowerCAmelCase					=  nn.KLDivLoss(reduction="""batchmean""")
									__lowerCAmelCase					=  2.0
									__lowerCAmelCase					=  hparams.alpha_mlm
									__lowerCAmelCase					=  hparams.alpha_ce
									__lowerCAmelCase					=  hparams.alpha_hid
									gc.collect()
									torch.cuda.empty_cache()




					def     _lowercase						(   self:		List[Any],     _lowercase:		Optional[Any],     _lowercase:		List[str],     _lowercase:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  mask[:, :, None].expand_as(_lowercase)
									__lowerCAmelCase					=  s_logits.size(-1)
									__lowerCAmelCase					=  torch.masked_select(_lowercase,     _lowercase)  # (bs * seq_length * voc_size) modulo the 1s in mask
									__lowerCAmelCase					=  torch.masked_select(_lowercase,     _lowercase)  # (bs * seq_length * voc_size) modulo the 1s in mask
									__lowerCAmelCase					=  s_logits_slct.view(-1,     _lowercase)  # (bs * seq_length, voc_size) modulo the 1s in mask
									__lowerCAmelCase					=  t_logits_slct.view(-1,     _lowercase)  # (bs * seq_length, voc_size) modulo the 1s in mask
									assert t_logits_slct.size() == s_logits_slct.size()
									__lowerCAmelCase					=  (
									    self.ce_loss_fct(
									        nn.functional.log_softmax(s_logits_slct / self.temperature,     dim=-1),     nn.functional.softmax(t_logits_slct / self.temperature,     dim=-1),     )
									    * (self.temperature) ** 2
									)
									return loss_ce




					@staticmethod
					def     _lowercase						(   _lowercase:		int,     _lowercase:		Optional[int]):


									'''simple docstring'''


									SummarizationModule.add_model_specific_args(_lowercase,     _lowercase)
									add_distill_args(_lowercase)
									return parser




					def     _lowercase						(   self:		List[str],     _lowercase:		dict):


									'''simple docstring'''


									__lowerCAmelCase					=  self.tokenizer.pad_token_id
									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
									if isinstance(self.model,     _lowercase):
													__lowerCAmelCase					=  self.model._shift_right(_lowercase)
									else:
													__lowerCAmelCase					=  shift_tokens_right(_lowercase,     _lowercase)

									# noinspection PyCallingNonCallable
									__lowerCAmelCase					=  self(
									    _lowercase,     attention_mask=_lowercase,     decoder_input_ids=_lowercase,     output_hidden_states=self.do_calc_hidden_loss,     output_attentions=_lowercase,     use_cache=_lowercase,     )
									__lowerCAmelCase					=  student_outputs["""logits"""]

									# Same cross entropy vs. label smoothing logic as finetune.py
									assert lm_logits.shape[-1] == self.model.config.vocab_size
									if self.hparams.label_smoothing == 0:
													# Same behavior as modeling_bart.py, besides ignoring pad_token_id
													__lowerCAmelCase					=  nn.CrossEntropyLoss(ignore_index=_lowercase)
													__lowerCAmelCase					=  loss_fct(lm_logits.view(-1,     lm_logits.shape[-1]),     labels.view(-1))
									else:
													__lowerCAmelCase					=  nn.functional.log_softmax(_lowercase,     dim=-1)
													__lowerCAmelCase	,				__lowerCAmelCase					=  label_smoothed_nll_loss(
													    _lowercase,     _lowercase,     self.hparams.label_smoothing,     ignore_index=_lowercase)

									def zero_tensor():
													return torch.tensor(0.0).type_as(_lowercase)

									__lowerCAmelCase					=  student_outputs[
									    """encoder_last_hidden_state"""
									]  # use this unless self.different_base_models
									__lowerCAmelCase	,				__lowerCAmelCase					=  zero_tensor(), zero_tensor()
									if self.different_encoder:  # compute encoder hidden state loss
													__lowerCAmelCase					=  self.teacher.get_encoder()(
													    _lowercase,     attention_mask=_lowercase,     output_hidden_states=self.do_calc_hidden_loss,     )
													if self.different_base_models:
																	__lowerCAmelCase					=  all_teacher_encoder_outputs["""last_hidden_state"""]
													elif self.do_calc_hidden_loss:
																	__lowerCAmelCase					=  self.calc_hidden_loss(
																	    _lowercase,     student_outputs["""encoder_hidden_states"""],     all_teacher_encoder_outputs["""hidden_states"""],     self.e_matches,     normalize_hidden=self.hparams.normalize_hidden,     )

									__lowerCAmelCase					=  self.teacher(
									    _lowercase,     attention_mask=_lowercase,     encoder_outputs=(teacher_enc_outputs,),     decoder_input_ids=_lowercase,     output_hidden_states=self.do_calc_hidden_loss,     use_cache=_lowercase,     )
									__lowerCAmelCase					=  decoder_input_ids.ne(_lowercase)
									__lowerCAmelCase					=  self.calc_ce_loss(_lowercase,     _lowercase,     teacher_outputs["""logits"""])
									if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
													__lowerCAmelCase					=  self.calc_hidden_loss(
													    _lowercase,     student_outputs["""decoder_hidden_states"""],     teacher_outputs["""decoder_hidden_states"""],     self.d_matches,     normalize_hidden=self.hparams.normalize_hidden,     )

									__lowerCAmelCase					=  (
									    self.alpha_ce * loss_ce
									    + self.alpha_mlm * student_lm_loss
									    + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
									)
									return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec







					@staticmethod
					def     _lowercase						(   _lowercase:		str,     _lowercase:		int,     _lowercase:		Tuple,     _lowercase:		int,     _lowercase:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  """expected list or tuple for hidden_states, got tensor of shape: """
									assert not isinstance(_lowercase,     torch.Tensor), f'''{msg}{hidden_states.shape}'''
									assert not isinstance(_lowercase,     torch.Tensor), f'''{msg}{hidden_states_T.shape}'''
									__lowerCAmelCase					=  attention_mask.to(hidden_states[0])
									__lowerCAmelCase					=  mask.sum() * hidden_states[0].size(-1)
									__lowerCAmelCase					=  torch.stack([hidden_states[i] for i in range(len(_lowercase))])
									__lowerCAmelCase					=  torch.stack([hidden_states_T[j] for j in matches])
									assert student_states.shape == teacher_states.shape, f'''{student_states.shape} != {teacher_states.shape}'''
									if normalize_hidden:
													__lowerCAmelCase					=  nn.functional.layer_norm(_lowercase,     student_states.shape[1:])
													__lowerCAmelCase					=  nn.functional.layer_norm(_lowercase,     teacher_states.shape[1:])
									__lowerCAmelCase					=  nn.functional.mse_loss(_lowercase,     _lowercase,     reduction="""none""")
									__lowerCAmelCase					=  (mse * mask.unsqueeze(0).unsqueeze(-1)).sum() / valid_count
									return masked_mse





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				parser.add_argument("""--teacher"""						,			type=UpperCamelCase__   )
				parser.add_argument("""--alpha_ce"""						,			default=0.8						,			type=UpperCamelCase__   )
				parser.add_argument("""--alpha_mlm"""						,			default=0.2						,			type=UpperCamelCase__   )
				parser.add_argument("""--alpha_hid"""						,			default=0.0						,			type=UpperCamelCase__						,			required=UpperCamelCase__   )
				parser.add_argument("""--student"""						,			type=UpperCamelCase__						,			required=UpperCamelCase__   )
				parser.add_argument("""--student_decoder_layers"""						,			default=12						,			type=UpperCamelCase__						,			required=UpperCamelCase__   )
				parser.add_argument("""--student_encoder_layers"""						,			default=12						,			type=UpperCamelCase__						,			required=UpperCamelCase__   )
				parser.add_argument("""--no_teacher"""						,			action="""store_true"""						,			default=UpperCamelCase__   )
				parser.add_argument("""--length_penalty"""						,			type=UpperCamelCase__						,			default=-1   )
				parser.add_argument("""--supervise_forward"""						,			action="""store_true"""						,			default=UpperCamelCase__   )
				parser.add_argument("""--normalize_hidden"""						,			action="""store_true"""						,			default=UpperCamelCase__   )

class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= "translation"
					__UpperCamelCase     	= ["bleu"]
					__UpperCamelCase     	= "bleu"




					def __init__(   self:		Optional[int],     _lowercase:		List[str],     **_lowercase:		Dict):


									'''simple docstring'''


									super().__init__(_lowercase,     **_lowercase)
									assert hparams.src_lang is not None
									assert hparams.tgt_lang is not None
									__lowerCAmelCase					=  hparams.src_lang
									__lowerCAmelCase					=  hparams.tgt_lang
									if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer,     _lowercase):
													__lowerCAmelCase					=  self.tokenizer.lang_code_to_id[hparams.tgt_lang]




					def     _lowercase						(   self:		List[str],     _lowercase:		Tuple,     _lowercase:		str):


									'''simple docstring'''


									return calculate_bleu(_lowercase,     _lowercase)







					@staticmethod
					def     _lowercase						(   _lowercase:		str,     _lowercase:		str):


									'''simple docstring'''


									TranslationModule.add_model_specific_args(_lowercase,     _lowercase)
									add_distill_args(_lowercase)
									return parser





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


				if args.no_teacher:
								__lowerCAmelCase					=  TranslationModule if """translation""" in args.task else SummarizationModule
				else:  # DISTILL WITH TEACHER
								__lowerCAmelCase					=  TranslationDistiller if """translation""" in args.task else SummarizationDistiller
				__lowerCAmelCase					=  module_cls.__name__
				print(F'''using module {args.setup_cls}'''   )
				__lowerCAmelCase					=  module_cls(UpperCamelCase__   )
				return model





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     List[str]:







				'''simple docstring'''


				Path(args.output_dir   ).mkdir(exist_ok=UpperCamelCase__   )
				check_output_dir(UpperCamelCase__						,			expected_items=3   )

				__lowerCAmelCase					=  create_module(UpperCamelCase__   )
				return ft_main(UpperCamelCase__						,			model=UpperCamelCase__   )


if __name__ == "__main__":
	__A						:		List[Any]          =						argparse.ArgumentParser()
	__A						:		List[Any]          =						pl.Trainer.add_argparse_args(parser)
	__A						:		Optional[Any]          =						SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
	__A						:		Optional[Any]          =						parser.parse_args()

	distill_main(args)











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 		lowercase_       :




					def __init__(   self:		Any,     _lowercase:		Tuple,     _lowercase:		int=13,     _lowercase:		Dict=32,     _lowercase:		Any=3,     _lowercase:		int=4,     _lowercase:		Optional[Any]=[10, 20, 30, 40],     _lowercase:		Any=[2, 2, 3, 2],     _lowercase:		Optional[int]=True,     _lowercase:		List[Any]=True,     _lowercase:		List[Any]=37,     _lowercase:		List[str]="gelu",     _lowercase:		List[str]=10,     _lowercase:		Any=0.02,     _lowercase:		int=3,     _lowercase:		List[str]=None,     ):


									'''simple docstring'''


									__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     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__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     _lowercase						(   self:		int):


									'''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=_lowercase,     initializer_range=self.initializer_range,     )




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		List[Any],     _lowercase:		Optional[Any],     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModel(config=_lowercase)
									__lowerCAmelCase					=  model(_lowercase,     training=_lowercase)
									# 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     _lowercase						(   self:		Optional[Any],     _lowercase:		int,     _lowercase:		int,     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  self.type_sequence_label_size
									__lowerCAmelCase					=  TFConvNextForImageClassification(_lowercase)
									__lowerCAmelCase					=  model(_lowercase,     labels=_lowercase,     training=_lowercase)
									self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.type_sequence_label_size))







					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


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





@require_tf
class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__						,							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     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModelTester(self)
									__lowerCAmelCase					=  ConfigTester(
									    self,     config_class=_lowercase,     has_text_modality=_lowercase,     hidden_size=37,     )




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


									'''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     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									super().test_keras_fit()




					@unittest.skip(reason="""ConvNext does not support input and output embeddings""")
					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  model_class(_lowercase)
													__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],     _lowercase)




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase)




					@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     _lowercase						(   self:		int):


									'''simple docstring'''


									super().test_dataset_conversion()




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									def check_hidden_states_output(_lowercase:		Any,     _lowercase:		Dict,     _lowercase:		Any):
													__lowerCAmelCase					=  model_class(_lowercase)

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

													__lowerCAmelCase					=  self.model_tester.num_stages
													self.assertEqual(len(_lowercase),     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(_lowercase,     _lowercase,     _lowercase)

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

													check_hidden_states_output(_lowercase,     _lowercase,     _lowercase)




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									def check_equivalence(_lowercase:		Tuple,     _lowercase:		Tuple,     _lowercase:		str,     _lowercase:		Dict={}):
													__lowerCAmelCase					=  model(_lowercase,     return_dict=_lowercase,     **_lowercase)
													__lowerCAmelCase					=  model(_lowercase,     return_dict=_lowercase,     **_lowercase).to_tuple()

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

																	recursive_check(_lowercase,     _lowercase)

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  model_class(_lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase,     {"""output_hidden_states""": True})

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase,     {"""output_hidden_states""": True})




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_lowercase)







					@slow
					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModel.from_pretrained("""facebook/convnext-tiny-224""")
									self.assertIsNotNone(_lowercase)





def      UpperCAmelCase  (						)     ->     Any:







				'''simple docstring'''


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

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




					@cached_property
					def     _lowercase						(   self:		int):


									'''simple docstring'''


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







					@slow
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224""")

									__lowerCAmelCase					=  self.default_image_processor
									__lowerCAmelCase					=  prepare_img()
									__lowerCAmelCase					=  image_processor(images=_lowercase,     return_tensors="""tf""")

									# forward pass
									__lowerCAmelCase					=  model(**_lowercase)

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

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

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





