






import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__snake_case      :      Any             =       os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'{bindir}/../../examples/pytorch/translation'):
					from run_translation import main  # noqa


set_seed(42)
__snake_case      :      Union[str, Any]             =       """sshleifer/student_marian_en_ro_6_1"""
__snake_case      :      int             =       """sshleifer/tiny-mbart"""


@require_torch
class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase=False       ,  _UpperCamelCase=None       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  self.run_trainer(
												    eval_steps=1       ,  max_len=12       ,  model_name=_UpperCamelCase       ,  num_train_epochs=1       ,  distributed=_UpperCamelCase       ,  extra_args_str=_UpperCamelCase       ,  predict_with_generate=_UpperCamelCase       ,  do_train=_UpperCamelCase       ,  do_eval=_UpperCamelCase       ,  do_predict=_UpperCamelCase       ,  )
												lowerCAmelCase__        =  TrainerState.load_from_json(os.path.join(_UpperCamelCase       ,  'trainer_state.json'				)				).log_history

												if not do_eval:
																		return

												lowerCAmelCase__        =  [log for log in logs if 'eval_loss' in log.keys()]

												lowerCAmelCase__        =  eval_metrics[0]
												if predict_with_generate:
																		assert "eval_bleu" in first_step_stats

																		lowerCAmelCase__        =  eval_metrics[-1]
																		assert isinstance(last_step_stats['eval_bleu']       ,  _UpperCamelCase				)
																		assert not math.isnan(float(last_step_stats['eval_loss']				)				), "eval_loss must not be `nan`"






						@require_torch_non_multi_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.run_seqaseq_quick()






						@require_torch_multi_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.run_seqaseq_quick(distributed=_UpperCamelCase				)






						@require_torch_multi_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.run_seqaseq_quick(distributed=_UpperCamelCase				)






						@unittest.skip('Requires an update of the env running those tests'				)
						@require_torch_multi_gpu
						@require_fairscale
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.run_seqaseq_quick(distributed=_UpperCamelCase       ,  extra_args_str='--sharded_ddp simple'				)






						@unittest.skip('Requires an update of the env running those tests'				)
						@require_torch_multi_gpu
						@require_fairscale
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.run_seqaseq_quick(distributed=_UpperCamelCase       ,  extra_args_str='--sharded_ddp simple --fp16'				)






						@unittest.skip('Requires an update of the env running those tests'				)
						@require_torch_multi_gpu
						@require_fairscale
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.run_seqaseq_quick(distributed=_UpperCamelCase       ,  extra_args_str='--sharded_ddp zero_dp_2'       ,  predict_with_generate=_UpperCamelCase				)






						@unittest.skip('Requires an update of the env running those tests'				)
						@require_torch_multi_gpu
						@require_fairscale
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.run_seqaseq_quick(
												    distributed=_UpperCamelCase       ,  extra_args_str='--sharded_ddp zero_dp_2 --fp16'       ,  predict_with_generate=_UpperCamelCase				)






						@require_apex
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
												# program and it breaks other tests that run from the same pytest worker, therefore until this is
												# sorted out it must be run only in an external program, that is distributed=True in this
												# test and only under one or more gpus - if we want cpu will need to make a special test
												#
												# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
												# 2nd main() call it botches the future eval.
												#
												self.run_seqaseq_quick(distributed=_UpperCamelCase       ,  extra_args_str='--fp16 --fp16_backend=apex'				)
												# test 2nd time - was getting eval_loss': nan'
												# to reproduce the problem set distributed=False
												self.run_seqaseq_quick(distributed=_UpperCamelCase       ,  extra_args_str='--fp16 --fp16_backend=apex'				)






						@parameterized.expand(['base', 'low', 'high', 'mixed']				)
						@require_torch_multi_gpu
						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
												lowerCAmelCase__        =  {
												    # test with the default log_level - should be info and thus log info once
												    'base': {'extra_args_str': '', 'n_matches': 1},
												    # test with low log_level and log_level_replica - should be noisy on all processes
												    # now the info string should appear twice on 2 processes
												    'low': {'extra_args_str': '--log_level debug --log_level_replica debug', 'n_matches': 2},
												    # test with high log_level and low log_level_replica
												    # now the info string should appear once only on the replica
												    'high': {'extra_args_str': '--log_level error --log_level_replica debug', 'n_matches': 1},
												    # test with high log_level and log_level_replica - should be quiet on all processes
												    'mixed': {'extra_args_str': '--log_level error --log_level_replica error', 'n_matches': 0},
												}

												lowerCAmelCase__        =  experiments[experiment_id]
												lowerCAmelCase__        =  {'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
												lowerCAmelCase__        =  'Running training'
												with CaptureStderr() as cl:
																		self.run_seqaseq_quick(**_UpperCamelCase       ,  extra_args_str=data['extra_args_str']				)
												lowerCAmelCase__        =  len(re.findall(_UpperCamelCase       ,  cl.err				)				)
												self.assertEqual(_UpperCamelCase       ,  data['n_matches']				)






						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.run_trainer(
												    eval_steps=2       ,  max_len=1_28       ,  model_name=_UpperCamelCase       ,  learning_rate=3E-4       ,  num_train_epochs=10       ,  distributed=_UpperCamelCase       ,  )

												# Check metrics
												lowerCAmelCase__        =  TrainerState.load_from_json(os.path.join(_UpperCamelCase       ,  'trainer_state.json'				)				).log_history
												lowerCAmelCase__        =  [log for log in logs if 'eval_loss' in log.keys()]
												lowerCAmelCase__        =  eval_metrics[0]
												lowerCAmelCase__        =  eval_metrics[-1]

												assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
												assert isinstance(last_step_stats['eval_bleu']       ,  _UpperCamelCase				)

												# test if do_predict saves generations and metrics
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  {os.path.basename(_UpperCamelCase				) for p in contents}
												assert "generated_predictions.txt" in contents
												assert "predict_results.json" in contents






						@slow
						@require_bitsandbytes
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												from transformers.training_args import OptimizerNames

												def train_and_return_metrics(_UpperCamelCase				) -> Tuple[int, float]:
																		lowerCAmelCase__        =  '--skip_memory_metrics 0'

																		lowerCAmelCase__        =  self.run_trainer(
																		    max_len=1_28       ,  model_name=_UpperCamelCase       ,  learning_rate=3E-4       ,  num_train_epochs=1       ,  optim=_UpperCamelCase       ,  distributed=_UpperCamelCase       ,  extra_args_str=_UpperCamelCase       ,  do_eval=_UpperCamelCase       ,  do_predict=_UpperCamelCase       ,  n_gpus_to_use=1       ,  )

																		# Check metrics
																		lowerCAmelCase__        =  TrainerState.load_from_json(Path(_UpperCamelCase       ,  'trainer_state.json'				)				).log_history
																		lowerCAmelCase__        =  int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20				)
																		lowerCAmelCase__        =  int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20				)

																		lowerCAmelCase__        =  logs[0]['train_loss']
																		return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

												lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__        =  train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value				)
												lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__        =  train_and_return_metrics(OptimizerNames.ADAMW_BNB.value				)

												lowerCAmelCase__        =  gpu_alloc_mem_orig - gpu_alloc_mem_bnb

												lowerCAmelCase__        =  gpu_peak_mem_orig + gpu_alloc_mem_orig
												lowerCAmelCase__        =  gpu_peak_mem_bnb + gpu_alloc_mem_bnb
												lowerCAmelCase__        =  gpu_total_mem_orig - gpu_total_mem_bnb

												# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
												# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
												# in 2 bytes and the diff in optim memory usage is derived as so:
												#
												# - normal 25*8=~200MB (8 bytes per param)
												# - bnb    25*2= ~50MB (2 bytes per param)
												#
												# Thus we should expect ~150MB total memory saved.
												#
												# Peak memory should be the same - the total should be different by about that same margin
												#
												# After leaving a small margin to accommodate for differences between gpus let's check
												# that we have at least 120MB in savings
												lowerCAmelCase__        =  1_20

												# uncomment the following if this test starts failing - requires py38 for a new print feature
												# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
												# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
												# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
												# print(f"{gpu_alloc_mem_diff=}MB")
												# print(f"{gpu_peak_mem_diff=}MB")
												# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
												# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

												self.assertGreater(
												    _UpperCamelCase       ,  _UpperCamelCase       ,  'should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'
												    F" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
												    F" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"       ,  )

												self.assertGreater(
												    _UpperCamelCase       ,  _UpperCamelCase       ,  'should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'
												    F" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
												    F" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"       ,  )

												self.assertEqual(
												    _UpperCamelCase       ,  _UpperCamelCase       ,  F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = 3E-3       ,  _UpperCamelCase = "adafactor"       ,  _UpperCamelCase = False       ,  _UpperCamelCase = None       ,  _UpperCamelCase = 0       ,  _UpperCamelCase = True       ,  _UpperCamelCase = True       ,  _UpperCamelCase = True       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
												lowerCAmelCase__        =  self.get_auto_remove_tmp_dir()
												lowerCAmelCase__        =  F"\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(_UpperCamelCase				)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(_UpperCamelCase				)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        ".split()

												lowerCAmelCase__        =  F"\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(_UpperCamelCase				)}\n        ".split()

												lowerCAmelCase__        =  '\n            --do_predict\n        '.split()

												lowerCAmelCase__        =  []
												if do_train:
																		args += args_train

												if do_eval:
																		args += args_eval

												if do_predict:
																		args += args_predict

												if predict_with_generate:
																		args += "--predict_with_generate".split()

												if do_train:
																		if optim == "adafactor":
																								args += "--adafactor".split()
																		else:
																								args += F"--optim {optim}".split()

												if extra_args_str is not None:
																		args += extra_args_str.split()

												if distributed:
																		if n_gpus_to_use is None:
																								lowerCAmelCase__        =  get_gpu_count()
																		lowerCAmelCase__        =  get_torch_dist_unique_port()
																		lowerCAmelCase__        =  F"\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            ".split()
																		lowerCAmelCase__        =  [sys.executable] + distributed_args + args
																		# keep for quick debug
																		# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
																		execute_subprocess_async(_UpperCamelCase       ,  env=self.get_env()				)
												else:
																		lowerCAmelCase__        =  ['run_translation.py'] + args
																		with patch.object(_UpperCamelCase       ,  'argv'       ,  _UpperCamelCase				):
																								main()

												return output_dir













from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
					from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor


class       __SCREAMING_SNAKE_CASE   :






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase=3       ,  _UpperCamelCase=32       ,  _UpperCamelCase=3       ,  _UpperCamelCase=10       ,  _UpperCamelCase=[10, 20, 30, 40]       ,  _UpperCamelCase=[1, 1, 2, 1]       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase="relu"       ,  _UpperCamelCase=3       ,  _UpperCamelCase=None       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  parent
												lowerCAmelCase__        =  batch_size
												lowerCAmelCase__        =  image_size
												lowerCAmelCase__        =  num_channels
												lowerCAmelCase__        =  embeddings_size
												lowerCAmelCase__        =  hidden_sizes
												lowerCAmelCase__        =  depths
												lowerCAmelCase__        =  is_training
												lowerCAmelCase__        =  use_labels
												lowerCAmelCase__        =  hidden_act
												lowerCAmelCase__        =  num_labels
												lowerCAmelCase__        =  scope
												lowerCAmelCase__        =  len(_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""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.num_labels				)

												lowerCAmelCase__        =  self.get_config()

												return config, pixel_values, labels






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return ResNetConfig(
												    num_channels=self.num_channels       ,  embeddings_size=self.embeddings_size       ,  hidden_sizes=self.hidden_sizes       ,  depths=self.depths       ,  hidden_act=self.hidden_act       ,  num_labels=self.num_labels       ,  image_size=self.image_size       ,  )






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFResNetModel(config=_UpperCamelCase				)
												lowerCAmelCase__        =  model(_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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.num_labels
												lowerCAmelCase__        =  TFResNetForImageClassification(_UpperCamelCase				)
												lowerCAmelCase__        =  model(_UpperCamelCase       ,  labels=_UpperCamelCase				)
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)				)







						def  UpperCamelCase__ (		self				):






												"""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       __SCREAMING_SNAKE_CASE   (				__lowercase						,   __lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    Any						=   (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
						_SCREAMING_SNAKE_CASE		:    List[str]						=   (
						    {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFResNetModelTester(self				)
												lowerCAmelCase__        =  ConfigTester(self       ,  config_class=_UpperCamelCase       ,  has_text_modality=_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.create_and_test_config_common_properties()
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return






						@unittest.skip(reason='ResNet does not use inputs_embeds'				)
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												pass






						@unittest.skip(reason='ResNet does not support input and output embeddings'				)
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												pass






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												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				)

																		# ResNet'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()
												lowerCAmelCase__        =  ['basic', 'bottleneck']
												for model_class in self.all_model_classes:
																		for layer_type in layers_type:
																								lowerCAmelCase__        =  layer_type
																								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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase				)







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		lowerCAmelCase__        =  TFResNetModel.from_pretrained(_UpperCamelCase				)
																		self.assertIsNotNone(_UpperCamelCase				)







def 		_UpperCamelCase				(      )					->				List[str]:

						"""simple docstring"""



						lowerCAmelCase__        =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
						return image


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






						@cached_property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return (
												    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
												    if is_vision_available()
												    else None
												)







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

												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([-11.10_69, -9.78_77, -8.37_77]				)

												self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()       ,  _UpperCamelCase       ,  atol=1E-4				)				)





