




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 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			Optional[int]					    =["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]



				def __init__(			self							,			UpperCAmelCase_    ):
											assert Path(hparams.data_dir    ).exists()
											lowerCAmelCase =		Path(hparams.output_dir    )
											self.output_dir.mkdir(exist_ok=UpperCAmelCase_    )

											lowerCAmelCase =		self.output_dir.joinpath('''student'''    )

											lowerCAmelCase =		str(UpperCAmelCase_    )  # Tell lightning we are training the student
											lowerCAmelCase =		AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher    ).eval()
											use_task_specific_params(UpperCAmelCase_							,			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(UpperCAmelCase_							,			hparams.task    )
																		lowerCAmelCase ,  lowerCAmelCase =		None, None
											else:
																		lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		create_student_by_copying_alternating_layers(
																		    UpperCAmelCase_							,			e=hparams.student_encoder_layers							,			d=hparams.student_decoder_layers							,			save_path=UpperCAmelCase_    )

											if hparams.length_penalty != -1:
																		lowerCAmelCase =		hparams.length_penalty
											lowerCAmelCase =		hparams.teacher  # Use teacher's tokenizer
											super().__init__(UpperCAmelCase_							,			model=UpperCAmelCase_							,			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(UpperCAmelCase_    )    )
											if d_layer_ids is None:
																		lowerCAmelCase =		list(range(UpperCAmelCase_    )    )

											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=UpperCAmelCase_    )
																									lowerCAmelCase =		get_layers_to_supervise(
																									    n_student=len(self.d_layer_ids    )							,			n_teacher=UpperCAmelCase_    )
																		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    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		mask[:, :, None].expand_as(UpperCAmelCase_    )
											lowerCAmelCase =		s_logits.size(-1    )
											lowerCAmelCase =		torch.masked_select(UpperCAmelCase_							,			UpperCAmelCase_    )  # (bs * seq_length * voc_size) modulo the 1s in mask
											lowerCAmelCase =		torch.masked_select(UpperCAmelCase_							,			UpperCAmelCase_    )  # (bs * seq_length * voc_size) modulo the 1s in mask
											lowerCAmelCase =		s_logits_slct.view(-1							,			UpperCAmelCase_    )  # (bs * seq_length, voc_size) modulo the 1s in mask
											lowerCAmelCase =		t_logits_slct.view(-1							,			UpperCAmelCase_    )  # (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    __snake_case		(			UpperCAmelCase_							,			UpperCAmelCase_    ):
											SummarizationModule.add_model_specific_args(UpperCAmelCase_							,			UpperCAmelCase_    )
											add_distill_args(UpperCAmelCase_    )
											return parser



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											lowerCAmelCase =		self.tokenizer.pad_token_id
											lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
											if isinstance(self.model							,			UpperCAmelCase_    ):
																		lowerCAmelCase =		self.model._shift_right(UpperCAmelCase_    )
											else:
																		lowerCAmelCase =		shift_tokens_right(UpperCAmelCase_							,			UpperCAmelCase_    )

											# noinspection PyCallingNonCallable
											lowerCAmelCase =		self(
											    UpperCAmelCase_							,			attention_mask=UpperCAmelCase_							,			decoder_input_ids=UpperCAmelCase_							,			output_hidden_states=self.do_calc_hidden_loss							,			output_attentions=UpperCAmelCase_							,			use_cache=UpperCAmelCase_							,			)
											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=UpperCAmelCase_    )
																		lowerCAmelCase =		loss_fct(lm_logits.view(-1							,			lm_logits.shape[-1]    )							,			labels.view(-1    )    )
											else:
																		lowerCAmelCase =		nn.functional.log_softmax(UpperCAmelCase_							,			dim=-1    )
																		lowerCAmelCase ,  lowerCAmelCase =		label_smoothed_nll_loss(
																		    UpperCAmelCase_							,			UpperCAmelCase_							,			self.hparams.label_smoothing							,			ignore_index=UpperCAmelCase_    )

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

											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()(
																		    UpperCAmelCase_							,			attention_mask=UpperCAmelCase_							,			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(
																									    UpperCAmelCase_							,			student_outputs['''encoder_hidden_states''']							,			all_teacher_encoder_outputs['''hidden_states''']							,			self.e_matches							,			normalize_hidden=self.hparams.normalize_hidden							,			)

											lowerCAmelCase =		self.teacher(
											    UpperCAmelCase_							,			attention_mask=UpperCAmelCase_							,			encoder_outputs=(teacher_enc_outputs,)							,			decoder_input_ids=UpperCAmelCase_							,			output_hidden_states=self.do_calc_hidden_loss							,			use_cache=UpperCAmelCase_							,			)
											lowerCAmelCase =		decoder_input_ids.ne(UpperCAmelCase_    )
											lowerCAmelCase =		self.calc_ce_loss(UpperCAmelCase_							,			UpperCAmelCase_							,			teacher_outputs['''logits''']    )
											if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
																		lowerCAmelCase =		self.calc_hidden_loss(
																		    UpperCAmelCase_							,			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    __snake_case		(			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		'''expected list or tuple for hidden_states, got tensor of shape: '''
											assert not isinstance(UpperCAmelCase_							,			torch.Tensor    ), F"""{msg}{hidden_states.shape}"""
											assert not isinstance(UpperCAmelCase_							,			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(UpperCAmelCase_    )    )]    )
											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(UpperCAmelCase_							,			student_states.shape[1:]    )
																		lowerCAmelCase =		nn.functional.layer_norm(UpperCAmelCase_							,			teacher_states.shape[1:]    )
											lowerCAmelCase =		nn.functional.mse_loss(UpperCAmelCase_							,			UpperCAmelCase_							,			reduction='''none'''    )
											lowerCAmelCase =		(mse * mask.unsqueeze(0    ).unsqueeze(-1    )).sum() / valid_count
											return masked_mse




def    UpperCAmelCase							(  _snake_case				):
							# NOTE: if --student argument was specified and the teacher and student base models
							# are different, the models still have to have the same tokenizer, specified by
							# --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
							# from bart to t5. This s because if the tokenizers are different, the output space
							# for the two models is also different and their logits are not comparable.
							parser.add_argument('''--teacher'''    ,			type=_snake_case				)
							parser.add_argument('''--alpha_ce'''    ,			default=0.8    ,			type=_snake_case				)
							parser.add_argument('''--alpha_mlm'''    ,			default=0.2    ,			type=_snake_case				)
							parser.add_argument('''--alpha_hid'''    ,			default=0.0    ,			type=_snake_case    ,			required=_snake_case				)
							parser.add_argument('''--student'''    ,			type=_snake_case    ,			required=_snake_case				)
							parser.add_argument('''--student_decoder_layers'''    ,			default=12    ,			type=_snake_case    ,			required=_snake_case				)
							parser.add_argument('''--student_encoder_layers'''    ,			default=12    ,			type=_snake_case    ,			required=_snake_case				)
							parser.add_argument('''--no_teacher'''    ,			action='''store_true'''    ,			default=_snake_case				)
							parser.add_argument('''--length_penalty'''    ,			type=_snake_case    ,			default=-1				)
							parser.add_argument('''--supervise_forward'''    ,			action='''store_true'''    ,			default=_snake_case				)
							parser.add_argument('''--normalize_hidden'''    ,			action='''store_true'''    ,			default=_snake_case				)


class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			str					    ="""translation"""
				__a				:			Union[str, Any]					    =["""bleu"""]
				__a				:			Dict					    ="""bleu"""



				def __init__(			self							,			UpperCAmelCase_							,			**UpperCAmelCase_    ):
											super().__init__(UpperCAmelCase_							,			**UpperCAmelCase_    )
											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							,			UpperCAmelCase_    ):
																		lowerCAmelCase =		self.tokenizer.lang_code_to_id[hparams.tgt_lang]



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											return calculate_bleu(UpperCAmelCase_							,			UpperCAmelCase_    )






				@staticmethod
				def    __snake_case		(			UpperCAmelCase_							,			UpperCAmelCase_    ):
											TranslationModule.add_model_specific_args(UpperCAmelCase_							,			UpperCAmelCase_    )
											add_distill_args(UpperCAmelCase_    )
											return parser




def    UpperCAmelCase							(  _snake_case				):
							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(_snake_case				)
							return model



def    UpperCAmelCase							(  _snake_case				):
							Path(args.output_dir				).mkdir(exist_ok=_snake_case				)
							check_output_dir(_snake_case    ,			expected_items=3				)

							lowerCAmelCase =		create_module(_snake_case				)
							return ft_main(_snake_case    ,			model=_snake_case				)


if __name__ == "__main__":
		UpperCAmelCase_    =argparse.ArgumentParser()
		UpperCAmelCase_    =pl.Trainer.add_argparse_args(parser)
		UpperCAmelCase_    =SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
		UpperCAmelCase_    =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 				__UpperCamelCase      :





				'''simple docstring'''



				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							,			):
											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    __snake_case		(			self    ):
											lowerCAmelCase =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

											lowerCAmelCase =		self.get_config()

											return config, pixel_values, labels



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



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		TFConvNextModel(config=UpperCAmelCase_    )
											lowerCAmelCase =		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    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		self.type_sequence_label_size
											lowerCAmelCase =		TFConvNextForImageClassification(UpperCAmelCase_    )
											lowerCAmelCase =		model(UpperCAmelCase_							,			labels=UpperCAmelCase_							,			training=UpperCAmelCase_    )
											self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)    )






				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.prepare_config_and_inputs()
											lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		config_and_inputs
											lowerCAmelCase =		{'''pixel_values''': pixel_values}
											return config, inputs_dict




@require_tf
class 				__UpperCamelCase      (			__UpperCAmelCase  ,       __UpperCAmelCase  ,       unittest.TestCase	):





				'''simple docstring'''



				__a				:			Union[str, Any]					    =(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
				__a				:			Optional[int]					    =(
				    {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
				    if is_tf_available()
				    else {}
				)

				__a				:			Any					    =False
				__a				:			int					    =False
				__a				:			Union[str, Any]					    =False
				__a				:			Optional[Any]					    =False
				__a				:			List[Any]					    =False



				def    __snake_case		(			self    ):
											lowerCAmelCase =		TFConvNextModelTester(self    )
											lowerCAmelCase =		ConfigTester(
											    self							,			config_class=UpperCAmelCase_							,			has_text_modality=UpperCAmelCase_							,			hidden_size=37							,			)



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



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



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



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		lowerCAmelCase =		model_class(UpperCAmelCase_    )
																		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]							,			UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											lowerCAmelCase =		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    __snake_case		(			self    ):
											super().test_dataset_conversion()



				def    __snake_case		(			self    ):
											def check_hidden_states_output(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
																		lowerCAmelCase =		model_class(UpperCAmelCase_    )

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

																		lowerCAmelCase =		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]							,			)

											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(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

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

																		check_hidden_states_output(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

											def check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_={}    ):
																		lowerCAmelCase =		model(UpperCAmelCase_							,			return_dict=UpperCAmelCase_							,			**UpperCAmelCase_    )
																		lowerCAmelCase =		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:
																		lowerCAmelCase =		model_class(UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			{'''output_hidden_states''': True}    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			{'''output_hidden_states''': True}    )



				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_    )






				@slow
				def    __snake_case		(			self    ):
											lowerCAmelCase =		TFConvNextModel.from_pretrained('''facebook/convnext-tiny-224'''    )
											self.assertIsNotNone(UpperCAmelCase_    )




def    UpperCAmelCase							(  ):
							lowerCAmelCase =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
							return image


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





				'''simple docstring'''



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






				@slow
				def    __snake_case		(			self    ):
											lowerCAmelCase =		TFConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''    )

											lowerCAmelCase =		self.default_image_processor
											lowerCAmelCase =		prepare_img()
											lowerCAmelCase =		image_processor(images=UpperCAmelCase_							,			return_tensors='''tf'''    )

											# forward pass
											lowerCAmelCase =		model(**UpperCAmelCase_    )

											# verify the logits
											lowerCAmelCase =		tf.TensorShape((1, 10_00)    )
											self.assertEqual(outputs.logits.shape							,			UpperCAmelCase_    )

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

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



