





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    _UpperCAmelCase							:
						def __init__(		self      ,       a__      ,       a__=3      ,       a__=32      ,       a__=3      ,       a__=10      ,       a__=[10, 20, 30, 40]      ,       a__=[1, 1, 2, 1]      ,       a__=True      ,       a__=True      ,       a__="relu"      ,       a__=3      ,       a__=None      ,       ):
									A_     :				Optional[int]			      =	parent
									A_     :				Tuple			      =	batch_size
									A_     :				Optional[int]			      =	image_size
									A_     :				Optional[Any]			      =	num_channels
									A_     :				Union[str, Any]			      =	embeddings_size
									A_     :				List[Any]			      =	hidden_sizes
									A_     :				List[Any]			      =	depths
									A_     :				Tuple			      =	is_training
									A_     :				Tuple			      =	use_labels
									A_     :				Tuple			      =	hidden_act
									A_     :				str			      =	num_labels
									A_     :				List[Any]			      =	scope
									A_     :				Any			      =	len(a__       )
						def 		_lowerCamelCase      (		self       ):
									A_     :				Optional[Any]			      =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

									A_     :				List[str]			      =	None
									if self.use_labels:
												A_     :				Union[str, Any]			      =	ids_tensor([self.batch_size]      ,       self.num_labels       )

									A_     :				Dict			      =	self.get_config()

									return config, pixel_values, labels
						def 		_lowerCamelCase      (		self       ):
									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 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__       ):
									A_     :				Optional[int]			      =	TFResNetModel(config=a__       )
									A_     :				Union[str, Any]			      =	model(a__       )
									# 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 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__       ):
									A_     :				Optional[Any]			      =	self.num_labels
									A_     :				str			      =	TFResNetForImageClassification(a__       )
									A_     :				Tuple			      =	model(a__      ,       labels=a__       )
									self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)       )






						def 		_lowerCamelCase      (		self       ):
									A_     :				int			      =	self.prepare_config_and_inputs()
									A_     :				Tuple			      =	config_and_inputs
									A_     :				Optional[Any]			      =	{"""pixel_values""": pixel_values}
									return config, inputs_dict






@require_tf
class    _UpperCAmelCase							(		_lowerCamelCase			,       _lowerCamelCase			,       unittest.TestCase     ):
						a						       =							(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
						a						       =							(
						    {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						a						       =							False
						a						       =							False
						a						       =							False
						a						       =							False
						a						       =							False
						def 		_lowerCamelCase      (		self       ):
									A_     :				Optional[Any]			      =	TFResNetModelTester(self       )
									A_     :				str			      =	ConfigTester(self      ,       config_class=a__      ,       has_text_modality=a__       )
						def 		_lowerCamelCase      (		self       ):
									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 		_lowerCamelCase      (		self       ):
									return
						@unittest.skip(reason="""ResNet does not use inputs_embeds"""       )
						def 		_lowerCamelCase      (		self       ):
									pass
						@unittest.skip(reason="""ResNet does not support input and output embeddings"""       )
						def 		_lowerCamelCase      (		self       ):
									pass
						def 		_lowerCamelCase      (		self       ):
									A_     :				Dict			      =	self.model_tester.prepare_config_and_inputs_for_common()

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

												A_     :				Optional[Any]			      =	["""pixel_values"""]
												self.assertListEqual(arg_names[:1]      ,       a__       )
						def 		_lowerCamelCase      (		self       ):
									A_     :				Tuple			      =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*a__       )
						def 		_lowerCamelCase      (		self       ):
									def check_hidden_states_output(a__      ,       a__      ,       a__       ):
												A_     :				Optional[Any]			      =	model_class(a__       )
												A_     :				Optional[Any]			      =	model(**self._prepare_for_class(a__      ,       a__       )       )

												A_     :				str			      =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												A_     :				Dict			      =	self.model_tester.num_stages
												self.assertEqual(len(a__       )      ,       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]      ,       )

									A_     :				Tuple			      =	self.model_tester.prepare_config_and_inputs_for_common()
									A_     :				Optional[int]			      =	["""basic""", """bottleneck"""]
									for model_class in self.all_model_classes:
												for layer_type in layers_type:
															A_     :				List[Any]			      =	layer_type
															A_     :				List[str]			      =	True
															check_hidden_states_output(a__      ,       a__      ,       a__       )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															A_     :				Dict			      =	True

															check_hidden_states_output(a__      ,       a__      ,       a__       )
						def 		_lowerCamelCase      (		self       ):
									A_     :				Optional[int]			      =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*a__       )






						@slow
						def 		_lowerCamelCase      (		self       ):
									for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A_     :				int			      =	TFResNetModel.from_pretrained(a__       )
												self.assertIsNotNone(a__       )






def     _lowerCAmelCase		(  ):
			'''simple docstring'''


			A_     :				Union[str, Any]			      =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
			return image







@require_tf
@require_vision
class    _UpperCAmelCase							(		unittest.TestCase     ):
						@cached_property
						def 		_lowerCamelCase      (		self       ):
									return (
									    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
									    if is_vision_available()
									    else None
									)






						@slow
						def 		_lowerCamelCase      (		self       ):
									A_     :				Union[str, Any]			      =	TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

									A_     :				str			      =	self.default_image_processor
									A_     :				List[Any]			      =	prepare_img()
									A_     :				Union[str, Any]			      =	image_processor(images=a__      ,       return_tensors="""tf"""       )

									# forward pass
									A_     :				Union[str, Any]			      =	model(**a__       )

									# verify the logits
									A_     :				int			      =	tf.TensorShape((1, 1000)       )
									self.assertEqual(outputs.logits.shape      ,       a__       )

									A_     :				List[Any]			      =	tf.constant([-11.1069, -9.7877, -8.3777]       )

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













from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






