




from math import ceil


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	Tuple:
		SCREAMING_SNAKE_CASE_					      =							list(range(0    ,						lowerCAmelCase ) )

		SCREAMING_SNAKE_CASE_					      =							[item for sublist in list(device_map.values() ) for item in sublist]

		# Duplicate check
		SCREAMING_SNAKE_CASE_					      =							[]
		for i in device_map_blocks:
				if device_map_blocks.count(lowerCAmelCase ) > 1 and i not in duplicate_blocks:
						duplicate_blocks.append(lowerCAmelCase )
    # Missing blocks
		SCREAMING_SNAKE_CASE_					      =							[i for i in blocks if i not in device_map_blocks]
		SCREAMING_SNAKE_CASE_					      =							[i for i in device_map_blocks if i not in blocks]

		if len(lowerCAmelCase ) != 0:
				raise ValueError(
				    'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
				    ' These attention blocks were specified more than once: ' + str(lowerCAmelCase ) )
		if len(lowerCAmelCase ) != 0:
				raise ValueError(
				    'There are attention blocks for this model that are not specified in the device_map. Add these attention '
				    'blocks to a device on the device_map: ' + str(lowerCAmelCase ) )
		if len(lowerCAmelCase ) != 0:
				raise ValueError(
				    'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
				    + str(lowerCAmelCase ) )




def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	Optional[int]:
		SCREAMING_SNAKE_CASE_					      =							list(range(lowerCAmelCase ) )
		SCREAMING_SNAKE_CASE_					      =							int(ceil(n_layers / len(lowerCAmelCase ) ) )
		SCREAMING_SNAKE_CASE_					      =							[layers[i : i + n_blocks] for i in range(0    ,						lowerCAmelCase    ,						lowerCAmelCase )]

		return dict(zip(lowerCAmelCase    ,						lowerCAmelCase ) )





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    lowercase_		:



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   List[Any]					,							snake_case__	:   Tuple=3					,							snake_case__	:   Union[str, Any]=32					,							snake_case__	:   Union[str, Any]=3					,							snake_case__	:   str=10					,							snake_case__	:   List[Any]=[10, 20, 30, 40]					,							snake_case__	:   Optional[Any]=[1, 1, 2, 1]					,							snake_case__	:   str=True					,							snake_case__	:   str=True					,							snake_case__	:   List[str]="relu"					,							snake_case__	:   Tuple=3					,							snake_case__	:   str=None					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							parent
								SCREAMING_SNAKE_CASE_					      =							batch_size
								SCREAMING_SNAKE_CASE_					      =							image_size
								SCREAMING_SNAKE_CASE_					      =							num_channels
								SCREAMING_SNAKE_CASE_					      =							embeddings_size
								SCREAMING_SNAKE_CASE_					      =							hidden_sizes
								SCREAMING_SNAKE_CASE_					      =							depths
								SCREAMING_SNAKE_CASE_					      =							is_training
								SCREAMING_SNAKE_CASE_					      =							use_labels
								SCREAMING_SNAKE_CASE_					      =							hidden_act
								SCREAMING_SNAKE_CASE_					      =							num_labels
								SCREAMING_SNAKE_CASE_					      =							scope
								SCREAMING_SNAKE_CASE_					      =							len(snake_case__  )



						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								SCREAMING_SNAKE_CASE_					      =							None
								if self.use_labels:
										SCREAMING_SNAKE_CASE_					      =							ids_tensor([self.batch_size]					,							self.num_labels  )

								SCREAMING_SNAKE_CASE_					      =							self.get_config()

								return config, pixel_values, labels



						def 	__a					(			self	:   int  ):

								"""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 	__a					(			self	:   str					,							snake_case__	:   str					,							snake_case__	:   Tuple					,							snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFResNetModel(config=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							model(snake_case__  )
								# 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 	__a					(			self	:   int					,							snake_case__	:   Optional[Any]					,							snake_case__	:   int					,							snake_case__	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.num_labels
								SCREAMING_SNAKE_CASE_					      =							TFResNetForImageClassification(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							labels=snake_case__  )
								self.parent.assertEqual(result.logits.shape					,							(self.batch_size, self.num_labels)  )






						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

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



@require_tf
class    lowercase_		(SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						lowerCAmelCase__			      =(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
						lowerCAmelCase__			      =(
						    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFResNetModelTester(self  )
								SCREAMING_SNAKE_CASE_					      =							ConfigTester(self					,							config_class=snake_case__					,							has_text_modality=snake_case__  )



						def 	__a					(			self	:   Optional[Any]  ):

								"""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 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								return



						@unittest.skip(reason='ResNet does not use inputs_embeds'  )
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								pass



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

								"""simple docstring"""

								pass



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs_for_common()

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

										SCREAMING_SNAKE_CASE_					      =							['pixel_values']
										self.assertListEqual(arg_names[:1]					,							snake_case__  )



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

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case__  )



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								def check_hidden_states_output(snake_case__	:   Optional[int]					,							snake_case__	:   int					,							snake_case__	:   Dict  ):
										SCREAMING_SNAKE_CASE_					      =							model_class(snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							model(**self._prepare_for_class(snake_case__					,							snake_case__  )  )

										SCREAMING_SNAKE_CASE_					      =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										SCREAMING_SNAKE_CASE_					      =							self.model_tester.num_stages
										self.assertEqual(len(snake_case__  )					,							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]					,							)

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs_for_common()
								SCREAMING_SNAKE_CASE_					      =							['basic', 'bottleneck']
								for model_class in self.all_model_classes:
										for layer_type in layers_type:
												SCREAMING_SNAKE_CASE_					      =							layer_type
												SCREAMING_SNAKE_CASE_					      =							True
												check_hidden_states_output(snake_case__					,							snake_case__					,							snake_case__  )

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

												check_hidden_states_output(snake_case__					,							snake_case__					,							snake_case__  )



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*snake_case__  )






						@slow
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_					      =							TFResNetModel.from_pretrained(snake_case__  )
										self.assertIsNotNone(snake_case__  )


def 					_a							(					)->	Tuple:
		SCREAMING_SNAKE_CASE_					      =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
		return image




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



						@cached_property
						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

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






						@slow
						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )

								SCREAMING_SNAKE_CASE_					      =							self.default_image_processor
								SCREAMING_SNAKE_CASE_					      =							prepare_img()
								SCREAMING_SNAKE_CASE_					      =							image_processor(images=snake_case__					,							return_tensors='tf'  )

								# forward pass
								SCREAMING_SNAKE_CASE_					      =							model(**snake_case__  )

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

								SCREAMING_SNAKE_CASE_					      =							tf.constant([-11.10_69, -9.78_77, -8.37_77]  )

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