import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_MAPPING,
						    EfficientFormerForImageClassification,
						    EfficientFormerForImageClassificationWithTeacher,
						    EfficientFormerModel,
						)
						from transformers.models.efficientformer.modeling_efficientformer import (
						    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						)


if is_vision_available():
						from PIL import Image

						from transformers import EfficientFormerImageProcessor
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = 13       ,  lowerCAmelCase_ = 64       ,  lowerCAmelCase_ = 2       ,  lowerCAmelCase_ = 3       ,  lowerCAmelCase_ = 3       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = 128       ,  lowerCAmelCase_=[16, 32, 64, 128]       ,  lowerCAmelCase_ = 7       ,  lowerCAmelCase_ = 4       ,  lowerCAmelCase_ = 37       ,  lowerCAmelCase_ = "gelu"       ,  lowerCAmelCase_ = 0.1       ,  lowerCAmelCase_ = 0.1       ,  lowerCAmelCase_ = 10       ,  lowerCAmelCase_ = 0.0_2       ,  lowerCAmelCase_ = 2       ,  lowerCAmelCase_ = 1       ,  lowerCAmelCase_ = 128       ,  lowerCAmelCase_ = [2, 2, 2, 2]       ,  lowerCAmelCase_ = 2       ,  lowerCAmelCase_ = 2       ,  ):
		_lowercase   =parent
		_lowercase   =batch_size
		_lowercase   =image_size
		_lowercase   =patch_size
		_lowercase   =num_channels
		_lowercase   =is_training
		_lowercase   =use_labels
		_lowercase   =hidden_size
		_lowercase   =num_hidden_layers
		_lowercase   =num_attention_heads
		_lowercase   =intermediate_size
		_lowercase   =hidden_act
		_lowercase   =hidden_dropout_prob
		_lowercase   =attention_probs_dropout_prob
		_lowercase   =type_sequence_label_size
		_lowercase   =initializer_range
		_lowercase   =encoder_stride
		_lowercase   =num_attention_outputs
		_lowercase   =embed_dim
		_lowercase   =embed_dim + 1
		_lowercase   =resolution
		_lowercase   =depths
		_lowercase   =hidden_sizes
		_lowercase   =dim
		_lowercase   =mlp_expansion_ratio


	def __lowerCAmelCase      (   self       ):
		_lowercase   =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

		_lowercase   =None
		if self.use_labels:
			_lowercase   =ids_tensor([self.batch_size]       ,  self.type_sequence_label_size       )

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


	def __lowerCAmelCase      (   self       ):
		return EfficientFormerConfig(
		    image_size=self.image_size       ,  patch_size=self.patch_size       ,  num_channels=self.num_channels       ,  hidden_size=self.hidden_size       ,  num_hidden_layers=self.num_hidden_layers       ,  num_attention_heads=self.num_attention_heads       ,  intermediate_size=self.intermediate_size       ,  hidden_act=self.hidden_act       ,  hidden_dropout_prob=self.hidden_dropout_prob       ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,  is_decoder=lowerCAmelCase_       ,  initializer_range=self.initializer_range       ,  encoder_stride=self.encoder_stride       ,  resolution=self.resolution       ,  depths=self.depths       ,  hidden_sizes=self.hidden_sizes       ,  dim=self.dim       ,  mlp_expansion_ratio=self.mlp_expansion_ratio       ,  )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =EfficientFormerModel(config=lowerCAmelCase_       )
		model.to(lowerCAmelCase_       )
		model.eval()
		_lowercase   =model(lowerCAmelCase_       )
		self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =self.type_sequence_label_size
		_lowercase   =EfficientFormerForImageClassification(lowerCAmelCase_       )
		model.to(lowerCAmelCase_       )
		model.eval()
		_lowercase   =model(lowerCAmelCase_       ,  labels=lowerCAmelCase_       )
		self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)       )

		# test greyscale images
		_lowercase   =1
		_lowercase   =EfficientFormerForImageClassification(lowerCAmelCase_       )
		model.to(lowerCAmelCase_       )
		model.eval()

		_lowercase   =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
		_lowercase   =model(lowerCAmelCase_       )
		self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.prepare_config_and_inputs()
		(
		    (
		    _lowercase
		)   ,  (
		    _lowercase
		)   ,  (
		    _lowercase
		)   ,  
		)   =config_and_inputs
		_lowercase   ={"pixel_values": pixel_values}
		return config, inputs_dict

@require_torch
class 	_a  (							lowerCamelCase_							,		lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					(
	    (
	        EfficientFormerModel,
	        EfficientFormerForImageClassificationWithTeacher,
	        EfficientFormerForImageClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	__SCREAMING_SNAKE_CASE              =					(
	    {
	        'feature-extraction': EfficientFormerModel,
	        'image-classification': (
	            EfficientFormerForImageClassification,
	            EfficientFormerForImageClassificationWithTeacher,
	        ),
	    }
	    if is_torch_available()
	    else {}
	)
	__SCREAMING_SNAKE_CASE              =					False

	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False


	def __lowerCAmelCase      (   self       ):
		_lowercase   =EfficientFormerModelTester(self       )
		_lowercase   =ConfigTester(
		    self       ,  config_class=lowerCAmelCase_       ,  has_text_modality=lowerCAmelCase_       ,  hidden_size=37       )


	def __lowerCAmelCase      (   self       ):
		self.config_tester.run_common_tests()


	@unittest.skip(reason="EfficientFormer does not use inputs_embeds"       )
	def __lowerCAmelCase      (   self       ):
		pass


	@unittest.skip(reason="EfficientFormer does not support input and output embeddings"       )
	def __lowerCAmelCase      (   self       ):
		pass


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

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

			_lowercase   =["pixel_values"]
			self.assertListEqual(arg_names[:1]       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		def check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.eval()

			with torch.no_grad():
				_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )

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

			_lowercase   =getattr(
			    self.model_tester       ,  "expected_num_hidden_layers"       ,  self.model_tester.num_hidden_layers + 1       )
			self.assertEqual(len(lowerCAmelCase_       )       ,  lowerCAmelCase_       )

			if hasattr(self.model_tester       ,  "encoder_seq_length"       ):
				_lowercase   =self.model_tester.encoder_seq_length
				if hasattr(self.model_tester       ,  "chunk_length"       ) and self.model_tester.chunk_length > 1:
					_lowercase   =seq_length * self.model_tester.chunk_length
			else:
				_lowercase   =self.model_tester.seq_length

			self.assertListEqual(
			    list(hidden_states[-1].shape[-2:]       )       ,  [seq_length, self.model_tester.hidden_size]       ,  )

			if config.is_encoder_decoder:
				_lowercase   =outputs.decoder_hidden_states

				self.assertIsInstance(lowerCAmelCase_       ,  (list, tuple)       )
				self.assertEqual(len(lowerCAmelCase_       )       ,  lowerCAmelCase_       )
				_lowercase   =getattr(self.model_tester       ,  "seq_length"       ,  lowerCAmelCase_       )
				_lowercase   =getattr(self.model_tester       ,  "decoder_seq_length"       ,  lowerCAmelCase_       )

				self.assertListEqual(
				    list(hidden_states[-1].shape[-2:]       )       ,  [decoder_seq_length, self.model_tester.hidden_size]       ,  )

		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			_lowercase   =True
			check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )

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

			check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=False       ):
		_lowercase   =super()._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       ,  return_labels=lowerCAmelCase_       )

		if return_labels:
			if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
				del inputs_dict["labels"]

		return inputs_dict


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCAmelCase_       )


	@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"       )
	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		if not self.model_tester.is_training:
			return

		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()
		_lowercase   =True

		for model_class in self.all_model_classes:
			# EfficientFormerForImageClassificationWithTeacher supports inference-only
			if (
			    model_class in get_values(lowerCAmelCase_       )
			    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
			):
				continue
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.train()
			_lowercase   =self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       ,  return_labels=lowerCAmelCase_       )
			_lowercase   =model(**lowerCAmelCase_       ).loss
			loss.backward()


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

		_lowercase   =[
		    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
		    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
		    {"title": "regression", "num_labels": 1, "dtype": torch.float},
		]

		for model_class in self.all_model_classes:
			if (
			    model_class
			    not in [
			        *get_values(lowerCAmelCase_       ),
			    ]
			    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
			):
				continue

			for problem_type in problem_types:
				with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''       ):
					_lowercase   =problem_type["title"]
					_lowercase   =problem_type["num_labels"]

					_lowercase   =model_class(lowerCAmelCase_       )
					model.to(lowerCAmelCase_       )
					model.train()

					_lowercase   =self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       ,  return_labels=lowerCAmelCase_       )

					if problem_type["num_labels"] > 1:
						_lowercase   =inputs["labels"].unsqueeze(1       ).repeat(1       ,  problem_type["num_labels"]       )

					_lowercase   =inputs["labels"].to(problem_type["dtype"]       )

					# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
					# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
					# they have the same size." which is a symptom something in wrong for the regression problem.
					# See https://github.com/huggingface/transformers/issues/11780
					with warnings.catch_warnings(record=lowerCAmelCase_       ) as warning_list:
						_lowercase   =model(**lowerCAmelCase_       ).loss
					for w in warning_list:
						if "Using a target size that is different to the input size" in str(w.message       ):
							raise ValueError(
							    F'''Something is going wrong in the regression problem: intercepted {w.message}'''       )

					loss.backward()


	@slow
	def __lowerCAmelCase      (   self       ):
		for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			_lowercase   =EfficientFormerModel.from_pretrained(lowerCAmelCase_       )
			self.assertIsNotNone(lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()
		_lowercase   =True

		_lowercase   =getattr(self.model_tester       ,  "seq_length"       ,  lowerCAmelCase_       )
		_lowercase   =getattr(self.model_tester       ,  "encoder_seq_length"       ,  lowerCAmelCase_       )
		_lowercase   =getattr(self.model_tester       ,  "key_length"       ,  lowerCAmelCase_       )
		_lowercase   =getattr(self.model_tester       ,  "chunk_length"       ,  lowerCAmelCase_       )

		if chunk_length is not None and hasattr(self.model_tester       ,  "num_hashes"       ):
			_lowercase   =encoder_seq_length * self.model_tester.num_hashes

		for model_class in self.all_model_classes:
			_lowercase   =True
			_lowercase   =False
			_lowercase   =True
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.eval()
			with torch.no_grad():
				_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )
			_lowercase   =outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
			self.assertEqual(len(lowerCAmelCase_       )       ,  self.model_tester.num_attention_outputs       )

			# check that output_attentions also work using config
			del inputs_dict["output_attentions"]
			_lowercase   =True
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.eval()
			with torch.no_grad():
				_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )
			_lowercase   =outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
			self.assertEqual(len(lowerCAmelCase_       )       ,  self.model_tester.num_attention_outputs       )

			if chunk_length is not None:
				self.assertListEqual(
				    list(attentions[0].shape[-4:]       )       ,  [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]       ,  )
			else:
				self.assertListEqual(
				    list(attentions[0].shape[-3:]       )       ,  [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]       ,  )

def 	__lowerCamelCase							(				)      ->       str:
	_lowercase   =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
	return image
@require_torch
@require_vision
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	@cached_property
	def __lowerCAmelCase      (   self       ):
		return (
		    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"       )
		    if is_vision_available()
		    else None
		)


	@slow
	def __lowerCAmelCase      (   self       ):
		_lowercase   =EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"       ).to(
		    lowerCAmelCase_       )

		_lowercase   =self.default_image_processor
		_lowercase   =prepare_img()
		_lowercase   =image_processor(images=lowerCAmelCase_       ,  return_tensors="pt"       ).to(lowerCAmelCase_       )

		# forward pass
		with torch.no_grad():
			_lowercase   =model(**lowerCAmelCase_       )

		# verify the logits
		_lowercase   =(1, 1000)
		self.assertEqual(outputs.logits.shape       ,  lowerCAmelCase_       )

		_lowercase   =torch.tensor([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]       ).to(lowerCAmelCase_       )
		self.assertTrue(torch.allclose(outputs.logits[0][:3]       ,  lowerCAmelCase_       ,  atol=1e-4       )       )


	@slow
	def __lowerCAmelCase      (   self       ):
		_lowercase   =EfficientFormerForImageClassificationWithTeacher.from_pretrained(
		    "snap-research/efficientformer-l1-300"       ).to(lowerCAmelCase_       )

		_lowercase   =self.default_image_processor
		_lowercase   =prepare_img()
		_lowercase   =image_processor(images=lowerCAmelCase_       ,  return_tensors="pt"       ).to(lowerCAmelCase_       )

		# forward pass
		with torch.no_grad():
			_lowercase   =model(**lowerCAmelCase_       )

		# verify the logits
		_lowercase   =(1, 1000)
		self.assertEqual(outputs.logits.shape       ,  lowerCAmelCase_       )

		_lowercase   =torch.tensor([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]       ).to(lowerCAmelCase_       )
		self.assertTrue(torch.allclose(outputs.logits[0][:3]       ,  lowerCAmelCase_       ,  atol=1e-4       )       )







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import CLIPImageProcessor





class A__     (			unittest.TestCase			):



 """simple docstring"""







 def __init__(		self			:						Union[str, Any]  ,    A_			:						Optional[Any]  ,    A_			:						List[str]=7  ,    A_			:						Optional[Any]=3  ,    A_			:						int=1_8  ,    A_			:						List[Any]=3_0  ,    A_			:						str=4_0_0  ,    A_			:						Union[str, Any]=True  ,    A_			:						Union[str, Any]=None  ,    A_			:						Union[str, Any]=True  ,    A_			:						Optional[int]=None  ,    A_			:						List[str]=True  ,    A_			:						Union[str, Any]=[0.48145466, 0.4578275, 0.40821073]  ,    A_			:						int=[0.26862954, 0.26130258, 0.27577711]  ,    A_			:						Optional[int]=True  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[Any]	   =     size if size is not None else {"shortest_edge": 2_0}
      _lowerCAmelCase :       Union[str, Any]	   =     crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
      _lowerCAmelCase :       Tuple	   =     parent
      _lowerCAmelCase :       int	   =     batch_size
      _lowerCAmelCase :       str	   =     num_channels
      _lowerCAmelCase :       int	   =     image_size
      _lowerCAmelCase :       Optional[int]	   =     min_resolution
      _lowerCAmelCase :       Any	   =     max_resolution
      _lowerCAmelCase :       str	   =     do_resize
      _lowerCAmelCase :       Union[str, Any]	   =     size
      _lowerCAmelCase :       Optional[int]	   =     do_center_crop
      _lowerCAmelCase :       Union[str, Any]	   =     crop_size
      _lowerCAmelCase :       Optional[int]	   =     do_normalize
      _lowerCAmelCase :       Optional[int]	   =     image_mean
      _lowerCAmelCase :       int	   =     image_std
      _lowerCAmelCase :       Optional[Any]	   =     do_convert_rgb

 def  __magic_name__							(		self			:						Any							):






      '''simple docstring'''




      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_convert_rgb": self.do_convert_rgb,
      }


 def  __magic_name__							(		self			:						List[Any]  ,    A_			:						List[Any]=False  ,    A_			:						Optional[Any]=False  ,    A_			:						Tuple=False							):






      '''simple docstring'''




      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      if equal_resolution:
           _lowerCAmelCase :       Tuple	   =     []
           for i in range(self.batch_size							):
                image_inputs.append(
                    np.random.randint(
                        2_5_5  ,    size=(self.num_channels, self.max_resolution, self.max_resolution)  ,    dtype=np.uinta							)							)
      else:
           _lowerCAmelCase :       Optional[int]	   =     []
           for i in range(self.batch_size							):
                _lowerCAmelCase   ,      _lowerCAmelCase :       Union[str, Any]	   =     np.random.choice(np.arange(self.min_resolution  ,    self.max_resolution							)  ,    2							)
                image_inputs.append(np.random.randint(2_5_5  ,    size=(self.num_channels, width, height)  ,    dtype=np.uinta							)							)

      if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           _lowerCAmelCase :       List[str]	   =     [Image.fromarray(np.moveaxis(A_  ,    0  ,    -1							)							) for x in image_inputs]

      if torchify:
           _lowerCAmelCase :       Tuple	   =     [torch.from_numpy(A_							) for x in image_inputs]

      return image_inputs





@require_torch
@require_vision
class A__     (			A	,		unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       Any  			=    CLIPImageProcessor if is_vision_available() else None

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






      '''simple docstring'''




      _lowerCAmelCase :       Tuple	   =     CLIPImageProcessingTester(self							)

 @property
 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()

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






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     self.image_processing_class(**self.image_processor_dict							)
      self.assertTrue(hasattr(A_  ,    "do_resize"							)							)
      self.assertTrue(hasattr(A_  ,    "size"							)							)
      self.assertTrue(hasattr(A_  ,    "do_center_crop"							)							)
      self.assertTrue(hasattr(A_  ,    "center_crop"							)							)
      self.assertTrue(hasattr(A_  ,    "do_normalize"							)							)
      self.assertTrue(hasattr(A_  ,    "image_mean"							)							)
      self.assertTrue(hasattr(A_  ,    "image_std"							)							)
      self.assertTrue(hasattr(A_  ,    "do_convert_rgb"							)							)

 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       Union[str, Any]	   =     self.image_processing_class.from_dict(self.image_processor_dict							)
      self.assertEqual(image_processor.size  ,    {"shortest_edge": 2_0}							)
      self.assertEqual(image_processor.crop_size  ,    {"height": 1_8, "width": 1_8}							)

      _lowerCAmelCase :       List[Any]	   =     self.image_processing_class.from_dict(self.image_processor_dict  ,    size=4_2  ,    crop_size=8_4							)
      self.assertEqual(image_processor.size  ,    {"shortest_edge": 4_2}							)
      self.assertEqual(image_processor.crop_size  ,    {"height": 8_4, "width": 8_4}							)

 def  __magic_name__							(		self			:						Dict							):






      '''simple docstring'''




      pass

 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[Any]	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random PIL images
      _lowerCAmelCase :       Union[str, Any]	   =     self.image_processor_tester.prepare_inputs(equal_resolution=A_							)
      for image in image_inputs:
           self.assertIsInstance(A_  ,    Image.Image							)

      # Test not batched input
      _lowerCAmelCase :       Optional[Any]	   =     image_processing(image_inputs[0]  ,    return_tensors="pt"							).pixel_values
      self.assertEqual(
          encoded_images.shape  ,    (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       Optional[Any]	   =     image_processing(A_  ,    return_tensors="pt"							).pixel_values
      self.assertEqual(
          encoded_images.shape  ,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,    )

 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       List[Any]	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random numpy tensors
      _lowerCAmelCase :       Dict	   =     self.image_processor_tester.prepare_inputs(equal_resolution=A_  ,    numpify=A_							)
      for image in image_inputs:
           self.assertIsInstance(A_  ,    np.ndarray							)

      # Test not batched input
      _lowerCAmelCase :       Optional[Any]	   =     image_processing(image_inputs[0]  ,    return_tensors="pt"							).pixel_values
      self.assertEqual(
          encoded_images.shape  ,    (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       Union[str, Any]	   =     image_processing(A_  ,    return_tensors="pt"							).pixel_values
      self.assertEqual(
          encoded_images.shape  ,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,    )


 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase :       Any	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random PyTorch tensors
      _lowerCAmelCase :       Any	   =     self.image_processor_tester.prepare_inputs(equal_resolution=A_  ,    torchify=A_							)
      for image in image_inputs:
           self.assertIsInstance(A_  ,    torch.Tensor							)

      # Test not batched input
      _lowerCAmelCase :       Tuple	   =     image_processing(image_inputs[0]  ,    return_tensors="pt"							).pixel_values
      self.assertEqual(
          encoded_images.shape  ,    (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       List[Any]	   =     image_processing(A_  ,    return_tensors="pt"							).pixel_values
      self.assertEqual(
          encoded_images.shape  ,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,    )





@require_torch
@require_vision
class A__     (			A	,		unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       List[str]  			=    CLIPImageProcessor if is_vision_available() else None

 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     CLIPImageProcessingTester(self  ,    num_channels=4							)
      _lowerCAmelCase :       Tuple	   =     3

 @property
 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()

 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       List[str]	   =     self.image_processing_class(**self.image_processor_dict							)
      self.assertTrue(hasattr(A_  ,    "do_resize"							)							)
      self.assertTrue(hasattr(A_  ,    "size"							)							)
      self.assertTrue(hasattr(A_  ,    "do_center_crop"							)							)
      self.assertTrue(hasattr(A_  ,    "center_crop"							)							)
      self.assertTrue(hasattr(A_  ,    "do_normalize"							)							)
      self.assertTrue(hasattr(A_  ,    "image_mean"							)							)
      self.assertTrue(hasattr(A_  ,    "image_std"							)							)
      self.assertTrue(hasattr(A_  ,    "do_convert_rgb"							)							)

 def  __magic_name__							(		self			:						Optional[Any]							):






      '''simple docstring'''




      pass


 def  __magic_name__							(		self			:						Any							):






      '''simple docstring'''




      _lowerCAmelCase :       Tuple	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random PIL images
      _lowerCAmelCase :       Optional[int]	   =     self.image_processor_tester.prepare_inputs(equal_resolution=A_							)
      for image in image_inputs:
           self.assertIsInstance(A_  ,    Image.Image							)

      # Test not batched input
      _lowerCAmelCase :       Optional[Any]	   =     image_processing(image_inputs[0]  ,    return_tensors="pt"							).pixel_values
      self.assertEqual(
          encoded_images.shape  ,    (
              1,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       int	   =     image_processing(A_  ,    return_tensors="pt"							).pixel_values
      self.assertEqual(
          encoded_images.shape  ,    (
              self.image_processor_tester.batch_size,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,    )






