



from math import ceil






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	int				)       ->							Tuple:

							'''simple docstring'''






							A      						=    list(range(0			,	lowerCAmelCase__				)				)

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

							# Duplicate check
							A      						=    []
							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
							A      						=    [i for i in blocks if i not in device_map_blocks]
							A      						=    [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  lowerCamelCase_	(      lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	str				)       ->							Any:

							'''simple docstring'''






							A      						=    list(range(lowerCAmelCase__				)				)
							A      						=    int(ceil(n_layers / len(lowerCAmelCase__				)				)				)
							A      						=    [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
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
	from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       Any:
								snake_case_           =				self.config_class(**self.inputs_dict   )
								self.parent.assertTrue(hasattr(a			,     'embed_dim'   )   )
								self.parent.assertTrue(hasattr(a			,     'num_heads'   )   )






class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a			,     a=13			,     a=64			,     a=3			,     a=[16, 48, 96]			,     a=[1, 3, 6]			,     a=[1, 2, 10]			,     a=[7, 3, 3]			,     a=[4, 2, 2]			,     a=[2, 1, 1]			,     a=[2, 2, 2]			,     a=[False, False, True]			,     a=[0.0, 0.0, 0.0]			,     a=0.02			,     a=1E-12			,     a=True			,     a=True			,     a=2			,     )			->       List[Any]:
								snake_case_           =				parent
								snake_case_           =				batch_size
								snake_case_           =				image_size
								snake_case_           =				patch_sizes
								snake_case_           =				patch_stride
								snake_case_           =				patch_padding
								snake_case_           =				is_training
								snake_case_           =				use_labels
								snake_case_           =				num_labels
								snake_case_           =				num_channels
								snake_case_           =				embed_dim
								snake_case_           =				num_heads
								snake_case_           =				stride_kv
								snake_case_           =				depth
								snake_case_           =				cls_token
								snake_case_           =				attention_drop_rate
								snake_case_           =				initializer_range
								snake_case_           =				layer_norm_eps




	def 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

								snake_case_           =				None
								if self.use_labels:
															# create a random int32 tensor of given shape
															snake_case_           =				ids_tensor([self.batch_size]			,     self.num_labels   )

								snake_case_           =				self.get_config()
								return config, pixel_values, labels




	def 				_UpperCamelCase     (			self   )			->       Dict:
								return CvtConfig(
								    image_size=self.image_size			,     num_labels=self.num_labels			,     num_channels=self.num_channels			,     embed_dim=self.embed_dim			,     num_heads=self.num_heads			,     patch_sizes=self.patch_sizes			,     patch_padding=self.patch_padding			,     patch_stride=self.patch_stride			,     stride_kv=self.stride_kv			,     depth=self.depth			,     cls_token=self.cls_token			,     attention_drop_rate=self.attention_drop_rate			,     initializer_range=self.initializer_range			,     )




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a   )			->       int:
								snake_case_           =				TFCvtModel(config=a   )
								snake_case_           =				model(a			,     training=a   )
								snake_case_           =				(self.image_size, self.image_size)
								snake_case_     ,						snake_case_           =				image_size[0], image_size[1]
								for i in range(len(self.depth   )   ):
															snake_case_           =				floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1   )
															snake_case_           =				floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1   )
								self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.embed_dim[-1], height, width)   )




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a   )			->       Dict:
								snake_case_           =				self.num_labels
								snake_case_           =				TFCvtForImageClassification(a   )
								snake_case_           =				model(a			,     labels=a			,     training=a   )
								self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)   )






	def 				_UpperCamelCase     (			self   )			->       Tuple:
								snake_case_           =				self.prepare_config_and_inputs()
								snake_case_     ,						snake_case_     ,						snake_case_           =				config_and_inputs
								snake_case_           =				{'pixel_values': pixel_values}
								return config, inputs_dict






@require_tf
class 					UpperCamelCase_ (     snake_case_			,   snake_case_			,   unittest.TestCase							):



	'''simple docstring'''




	lowerCAmelCase						=					(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
	lowerCAmelCase						=					(
	    {'''feature-extraction''': TFCvtModel, '''image-classification''': TFCvtForImageClassification}
	    if is_tf_available()
	    else {}
	)
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_           =				TFCvtModelTester(self   )
								snake_case_           =				TFCvtConfigTester(self			,     config_class=a			,     has_text_modality=a			,     hidden_size=37   )




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								self.config_tester.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()




	@unittest.skip(reason='Cvt does not output attentions'   )
	def 				_UpperCamelCase     (			self   )			->       Dict:
								pass




	@unittest.skip(reason='Cvt does not use inputs_embeds'   )
	def 				_UpperCamelCase     (			self   )			->       List[str]:
								pass




	@unittest.skip(reason='Cvt does not support input and output embeddings'   )
	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								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.'			,     )
	def 				_UpperCamelCase     (			self   )			->       Dict:
								super().test_dataset_conversion()




	@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 				_UpperCamelCase     (			self   )			->       Dict:
								super().test_keras_fit()




	@unittest.skip(reason='Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8'   )
	def 				_UpperCamelCase     (			self   )			->       List[str]:
								snake_case_           =				tf.keras.mixed_precision.Policy('mixed_float16'   )
								tf.keras.mixed_precision.set_global_policy(a   )
								super().test_keras_fit()
								tf.keras.mixed_precision.set_global_policy('float32'   )




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_     ,						snake_case_           =				self.model_tester.prepare_config_and_inputs_for_common()

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

															snake_case_           =				['pixel_values']
															self.assertListEqual(arg_names[:1]			,     a   )




	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								def check_hidden_states_output(a			,     a			,     a   ):
															snake_case_           =				model_class(a   )

															snake_case_           =				model(**self._prepare_for_class(a			,     a   )   )
															snake_case_           =				outputs.hidden_states

															snake_case_           =				len(self.model_tester.depth   )
															self.assertEqual(len(a   )			,     a   )

															# verify the first hidden states (first block)
															self.assertListEqual(
															    list(hidden_states[0].shape[-3:]   )			,     [
															        self.model_tester.embed_dim[0],
															        self.model_tester.image_size // 4,
															        self.model_tester.image_size // 4,
															    ]			,     )

								snake_case_     ,						snake_case_           =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															snake_case_           =				True
															check_hidden_states_output(a			,     a			,     a   )

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

															check_hidden_states_output(a			,     a			,     a   )




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								snake_case_           =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a   )




	def 				_UpperCamelCase     (			self   )			->       List[str]:
								snake_case_           =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a   )






	@slow
	def 				_UpperCamelCase     (			self   )			->       int:
								for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_           =				TFCvtModel.from_pretrained(a   )
															self.assertIsNotNone(a   )






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




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



	'''simple docstring'''




	@cached_property
	def 				_UpperCamelCase     (			self   )			->       Any:
								return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )






	@slow
	def 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

								snake_case_           =				self.default_image_processor
								snake_case_           =				prepare_img()
								snake_case_           =				image_processor(images=a			,     return_tensors='tf'   )

								# forward pass
								snake_case_           =				model(**a   )

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

								snake_case_           =				tf.constant([0.9_285, 0.9_015, -0.3_150]   )
								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()			,     a			,     atol=1E-4   )   )





