




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 		lowercase							(     UpperCamelCase__	):

					def       a__			(						self       )		->							int:
											_A							:  List[Any]    =		self.config_class(**self.inputs_dict       )
											self.parent.assertTrue(hasattr(_a	,  """embed_dim"""       )       )
											self.parent.assertTrue(hasattr(_a	,  """num_heads"""       )       )







class 		lowercase							:

					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	,  )		->							Optional[int]:
											_A							:  str    =		parent
											_A							:  Union[str, Any]    =		batch_size
											_A							:  int    =		image_size
											_A							:  Optional[int]    =		patch_sizes
											_A							:  List[str]    =		patch_stride
											_A							:  Any    =		patch_padding
											_A							:  int    =		is_training
											_A							:  List[str]    =		use_labels
											_A							:  Tuple    =		num_labels
											_A							:  Tuple    =		num_channels
											_A							:  Optional[int]    =		embed_dim
											_A							:  List[str]    =		num_heads
											_A							:  Union[str, Any]    =		stride_kv
											_A							:  Union[str, Any]    =		depth
											_A							:  Dict    =		cls_token
											_A							:  Dict    =		attention_drop_rate
											_A							:  List[Any]    =		initializer_range
											_A							:  Any    =		layer_norm_eps

					def       a__			(						self       )		->							Dict:
											_A							:  Optional[Any]    =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

											_A							:  List[str]    =		self.get_config()
											return config, pixel_values, labels

					def       a__			(						self       )		->							List[Any]:
											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       a__			(						self	,  _a	,  _a	,  _a       )		->							Optional[Any]:
											_A							:  Dict    =		TFCvtModel(config=_a       )
											_A							:  int    =		model(_a	,  training=_a       )
											_A							:  Union[str, Any]    =		(self.image_size, self.image_size)
											_A							:  Union[str, Any]    =		image_size[0], image_size[1]
											for i in range(len(self.depth       )       ):
																	_A							:  Optional[int]    =		floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
																	_A							:  Dict    =		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       a__			(						self	,  _a	,  _a	,  _a       )		->							List[str]:
											_A							:  Any    =		self.num_labels
											_A							:  str    =		TFCvtForImageClassification(_a       )
											_A							:  List[Any]    =		model(_a	,  labels=_a	,  training=_a       )
											self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels)       )



					def       a__			(						self       )		->							Tuple:
											_A							:  List[Any]    =		self.prepare_config_and_inputs()
											_A							:  Tuple    =		config_and_inputs
											_A							:  Any    =		{"""pixel_values""": pixel_values}
											return config, inputs_dict







@require_tf
class 		lowercase							(     UpperCamelCase__,UpperCamelCase__,unittest.TestCase	):
					_a											=		(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
					_a											=		(
					    {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
					    if is_tf_available()
					    else {}
					)
					_a											=		False
					_a											=		False
					_a											=		False
					_a											=		False
					_a											=		False

					def       a__			(						self       )		->							List[Any]:
											_A							:  Union[str, Any]    =		TFCvtModelTester(self       )
											_A							:  str    =		TFCvtConfigTester(self	,  config_class=_a	,  has_text_modality=_a	,  hidden_size=37       )

					def       a__			(						self       )		->							str:
											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       a__			(						self       )		->							List[str]:
											pass

					@unittest.skip(reason="""Cvt does not use inputs_embeds"""       )
					def       a__			(						self       )		->							List[Any]:
											pass

					@unittest.skip(reason="""Cvt does not support input and output embeddings"""       )
					def       a__			(						self       )		->							str:
											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       a__			(						self       )		->							Union[str, Any]:
											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       a__			(						self       )		->							List[str]:
											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       a__			(						self       )		->							int:
											_A							:  Any    =		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       a__			(						self       )		->							Union[str, Any]:
											_A							:  Dict    =		self.model_tester.prepare_config_and_inputs_for_common()

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

																	_A							:  Dict    =		["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]	,  _a       )

					def       a__			(						self       )		->							Optional[Any]:
											def check_hidden_states_output(_a	,  _a	,  _a       ):
																	_A							:  Union[str, Any]    =		model_class(_a       )

																	_A							:  Union[str, Any]    =		model(**self._prepare_for_class(_a	,  _a       )       )
																	_A							:  Optional[int]    =		outputs.hidden_states

																	_A							:  Optional[Any]    =		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,
																	    ]	,  )

											_A							:  Optional[Any]    =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	_A							:  Any    =		True
																	check_hidden_states_output(_a	,  _a	,  _a       )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	_A							:  Optional[Any]    =		True

																	check_hidden_states_output(_a	,  _a	,  _a       )

					def       a__			(						self       )		->							Dict:
											_A							:  str    =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_a       )

					def       a__			(						self       )		->							Tuple:
											_A							:  str    =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_a       )



					@slow
					def       a__			(						self       )		->							Any:
											for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_A							:  Optional[Any]    =		TFCvtModel.from_pretrained(_a       )
																	self.assertIsNotNone(_a       )







def        lowerCAmelCase_			(  ):
						_A							:  str    =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
						return image







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

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



					@slow
					def       a__			(						self       )		->							Tuple:
											_A							:  Dict    =		TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

											_A							:  Optional[Any]    =		self.default_image_processor
											_A							:  Tuple    =		prepare_img()
											_A							:  Optional[Any]    =		image_processor(images=_a	,  return_tensors="""tf"""       )

											# forward pass
											_A							:  int    =		model(**_a       )

											# verify the logits
											_A							:  Dict    =		tf.TensorShape((1, 1000)       )
											self.assertEqual(outputs.logits.shape	,  _a       )

											_A							:  Union[str, Any]    =		tf.constant([0.9285, 0.9015, -0.3150]       )
											self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()	,  _a	,  atol=1e-4       )       )







import operator







def        lowerCAmelCase_			(  snake_case_,snake_case_ = False,snake_case_ = None  ):
      _A							:  str    =		operator.lt if reverse else operator.gt
      _A							:  Optional[Any]    =		solution or []

      if not arr:
            return solution

      _A							:  Dict    =		[arr.pop(0  )]
      for i, item in enumerate(snake_case_  ):
            if _operator(snake_case_,sublist[-1]  ):
                  sublist.append(snake_case_  )
                  arr.pop(snake_case_  )

    #  merging sublist into solution list
      if not solution:
            solution.extend(snake_case_  )
      else:
            while sublist:
                  _A							:  Union[str, Any]    =		sublist.pop(0  )
                  for i, xx in enumerate(snake_case_  ):
                        if not _operator(snake_case_,snake_case_  ):
                              solution.insert(snake_case_,snake_case_  )
                              break
                  else:
                        solution.append(snake_case_  )

      strand_sort(snake_case_,snake_case_,snake_case_  )
      return solution


if __name__ == "__main__":
  assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
  assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]

