




"""simple docstring"""







from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
		import numpy as np
		import tensorflow as tf

		from transformers import (
		    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
		    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_MASKED_LM_MAPPING,
		    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
		    TF_MODEL_FOR_PRETRAINING_MAPPING,
		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    TFSharedEmbeddings,
		)

		if _tf_gpu_memory_limit is not None:
				a								=							tf.config.list_physical_devices('GPU')
				for gpu in gpus:
						# Restrict TensorFlow to only allocate x GB of memory on the GPUs
						try:
								tf.config.set_logical_device_configuration(
								    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
								)
								a								=							tf.config.list_logical_devices('GPU')
								print('Logical GPUs', logical_gpus)
						except RuntimeError as e:
								# Virtual devices must be set before GPUs have been initialized
								print(e)






@require_tf
class  SCREAMING_SNAKE_CASE__   :






						"""simple docstring"""




						_a        =  None
						_a        =  ()
						_a        =  ()
						_a        =  True
						_a        =  True
						_a        =  True
						_a        =  False


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     List[Any]  ,  lowerCAmelCase			:     List[str]=False   ):
										lowerCAmelCase		=			copy.deepcopy(__lowerCAmelCase   )

										if model_class in get_values(__lowerCAmelCase   ):
														lowerCAmelCase		=			{
														    k: tf.tile(tf.expand_dims(__lowerCAmelCase  ,  1   )  ,  (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)   )
														    if isinstance(__lowerCAmelCase  ,  tf.Tensor   ) and v.ndim > 0
														    else v
														    for k, v in inputs_dict.items()
														}

										if return_labels:
														if model_class in get_values(__lowerCAmelCase   ):
																		lowerCAmelCase		=			tf.ones(self.model_tester.batch_size  ,  dtype=tf.intaa   )
														elif model_class in get_values(__lowerCAmelCase   ):
																		lowerCAmelCase		=			tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa   )
																		lowerCAmelCase		=			tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa   )
														elif model_class in [
														    *get_values(__lowerCAmelCase   ),
														    *get_values(__lowerCAmelCase   ),
														]:
																		lowerCAmelCase		=			tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa   )
														elif model_class in get_values(__lowerCAmelCase   ):
																		lowerCAmelCase		=			tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa   )
														elif model_class in [
														    *get_values(__lowerCAmelCase   ),
														    *get_values(__lowerCAmelCase   ),
														    *get_values(__lowerCAmelCase   ),
														    *get_values(__lowerCAmelCase   ),
														    *get_values(__lowerCAmelCase   ),
														]:
																		lowerCAmelCase		=			tf.zeros(
																		    (self.model_tester.batch_size, self.model_tester.seq_length)  ,  dtype=tf.intaa   )
										return inputs_dict


						@slow
						def 					__lowercase	(							self			:     str   ):
										lowerCAmelCase	,  lowerCAmelCase		=			self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
														lowerCAmelCase		=			self._prepare_for_class(__lowerCAmelCase  ,  __lowerCAmelCase   )
														lowerCAmelCase		=			model_class(__lowerCAmelCase   )

														@tf.function
														def run_in_graph_mode():
																		return model(__lowerCAmelCase   )

														lowerCAmelCase		=			run_in_graph_mode()
														self.assertIsNotNone(__lowerCAmelCase   )


						@slow
						def 					__lowercase	(							self			:     Optional[int]   ):
										lowerCAmelCase	,  lowerCAmelCase		=			self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
														lowerCAmelCase		=			self._prepare_for_class(__lowerCAmelCase  ,  __lowerCAmelCase   )
														lowerCAmelCase		=			model_class(__lowerCAmelCase   )

														@tf.function(experimental_compile=__lowerCAmelCase   )
														def run_in_graph_mode():
																		return model(__lowerCAmelCase   )

														lowerCAmelCase		=			run_in_graph_mode()
														self.assertIsNotNone(__lowerCAmelCase   )


						@slow
						def 					__lowercase	(							self			:     Optional[Any]   ):
										lowerCAmelCase	,  lowerCAmelCase		=			self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
														lowerCAmelCase		=			model_class(__lowerCAmelCase   )
														if getattr(__lowerCAmelCase  ,  """hf_compute_loss"""  ,  __lowerCAmelCase   ):
																		# Test that model correctly compute the loss with kwargs
																		lowerCAmelCase		=			self._prepare_for_class(inputs_dict.copy()  ,  __lowerCAmelCase  ,  return_labels=__lowerCAmelCase   )
																		# Is there a better way to remove these decoder inputs?
																		lowerCAmelCase		=			{
																		    key: val
																		    for key, val in prepared_for_class.items()
																		    if key not in ("""head_mask""", """decoder_head_mask""", """cross_attn_head_mask""", """decoder_input_ids""")
																		}

																		lowerCAmelCase		=			{
																		    """labels""",
																		    """label""",
																		    """label_ids""",
																		    """start_positions""",
																		    """start_position""",
																		    """end_positions""",
																		    """end_position""",
																		    """next_sentence_label""",
																		}
																		lowerCAmelCase		=			possible_label_cols.intersection(set(__lowerCAmelCase   )   )
																		self.assertGreater(len(__lowerCAmelCase   )  ,  0  ,  msg="""No matching label names found!"""   )
																		lowerCAmelCase		=			{key: val for key, val in prepared_for_class.items() if key in label_names}
																		lowerCAmelCase		=			{key: val for key, val in prepared_for_class.items() if key not in label_names}
																		self.assertGreater(len(__lowerCAmelCase   )  ,  0   )

																		# Make sure it works with XLA!
																		model.compile(optimizer=tf.keras.optimizers.SGD(0.0   )  ,  jit_compile=__lowerCAmelCase   )
																		# Make sure the model fits without crashing regardless of where we pass the labels
																		lowerCAmelCase		=			model.fit(
																		    __lowerCAmelCase  ,  validation_data=__lowerCAmelCase  ,  steps_per_epoch=1  ,  validation_steps=1  ,  shuffle=__lowerCAmelCase  ,  verbose=0  ,  )
																		lowerCAmelCase		=			history.history["""loss"""][0]
																		self.assertTrue(not isnan(__lowerCAmelCase   )   )
																		lowerCAmelCase		=			history.history["""val_loss"""][0]
																		self.assertTrue(not isnan(__lowerCAmelCase   )   )

																		# Now test it with separate labels, to make sure that path works in XLA too.
																		lowerCAmelCase		=			model_class(__lowerCAmelCase   )
																		model.compile(optimizer=tf.keras.optimizers.SGD(0.0   )  ,  jit_compile=__lowerCAmelCase   )
																		lowerCAmelCase		=			model.fit(
																		    __lowerCAmelCase  ,  __lowerCAmelCase  ,  validation_data=(inputs_minus_labels, labels)  ,  steps_per_epoch=1  ,  validation_steps=1  ,  shuffle=__lowerCAmelCase  ,  verbose=0  ,  )

																		lowerCAmelCase		=			history.history["""loss"""][0]
																		self.assertTrue(not isnan(__lowerCAmelCase   )   )
																		lowerCAmelCase		=			history.history["""val_loss"""][0]
																		self.assertTrue(not isnan(__lowerCAmelCase   )   )


						@slow
						def 					__lowercase	(							self			:     Any   ):
										lowerCAmelCase	,  lowerCAmelCase		=			self.model_tester.prepare_config_and_inputs_for_common()
										lowerCAmelCase		=			True
										lowerCAmelCase		=			True

										if hasattr(__lowerCAmelCase  ,  """use_cache"""   ):
														lowerCAmelCase		=			True

										lowerCAmelCase		=			getattr(self.model_tester  ,  """encoder_seq_length"""  ,  self.model_tester.seq_length   )
										lowerCAmelCase		=			getattr(self.model_tester  ,  """key_length"""  ,  __lowerCAmelCase   )

										for model_class in self.all_model_classes[:2]:
														lowerCAmelCase		=			self._prepare_for_class(__lowerCAmelCase  ,  __lowerCAmelCase   )
														lowerCAmelCase		=			model_class(__lowerCAmelCase   )
														model.build()
														lowerCAmelCase		=			len(model(__lowerCAmelCase   )   )

														for key in list(class_inputs_dict.keys()   ):
																		# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
																		if key not in model.input_signature:
																						del class_inputs_dict[key]
																		# Check it's a tensor, in case the inputs dict has some bools in it too
																		elif isinstance(class_inputs_dict[key]  ,  tf.Tensor   ) and class_inputs_dict[key].dtype.is_integer:
																						lowerCAmelCase		=			tf.cast(class_inputs_dict[key]  ,  tf.intaa   )

														if set(class_inputs_dict.keys()   ) != set(model.input_signature.keys()   ):
																		continue  # Some models have inputs that the preparation functions don't create, we skip those

														with tempfile.TemporaryDirectory() as tmpdirname:
																		model.save_pretrained(__lowerCAmelCase  ,  saved_model=__lowerCAmelCase   )
																		lowerCAmelCase		=			os.path.join(__lowerCAmelCase  ,  """saved_model"""  ,  """1"""   )
																		lowerCAmelCase		=			tf.keras.models.load_model(__lowerCAmelCase   )
																		lowerCAmelCase		=			model(__lowerCAmelCase   )

																		if self.is_encoder_decoder:
																						lowerCAmelCase		=			outputs["""encoder_hidden_states"""]
																						lowerCAmelCase		=			outputs["""encoder_attentions"""]
																		else:
																						lowerCAmelCase		=			outputs["""hidden_states"""]
																						lowerCAmelCase		=			outputs["""attentions"""]

																		self.assertEqual(len(__lowerCAmelCase   )  ,  __lowerCAmelCase   )

																		lowerCAmelCase		=			getattr(
																		    self.model_tester  ,  """expected_num_hidden_layers"""  ,  self.model_tester.num_hidden_layers + 1   )

																		self.assertEqual(len(__lowerCAmelCase   )  ,  __lowerCAmelCase   )
																		self.assertListEqual(
																		    list(output_hidden_states[0].shape[-2:]   )  ,  [self.model_tester.seq_length, self.model_tester.hidden_size]  ,  )

																		self.assertEqual(len(__lowerCAmelCase   )  ,  self.model_tester.num_hidden_layers   )
																		self.assertListEqual(
																		    list(output_attentions[0].shape[-3:]   )  ,  [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]  ,  )


						@slow
						def 					__lowercase	(							self			:     List[str]   ):
										tf.keras.mixed_precision.set_global_policy("""mixed_float16"""   )

										# try/finally block to ensure subsequent tests run in float32
										try:
														lowerCAmelCase	,  lowerCAmelCase		=			self.model_tester.prepare_config_and_inputs_for_common()
														for model_class in self.all_model_classes[:2]:
																		lowerCAmelCase		=			self._prepare_for_class(__lowerCAmelCase  ,  __lowerCAmelCase   )
																		lowerCAmelCase		=			model_class(__lowerCAmelCase   )
																		lowerCAmelCase		=			model(__lowerCAmelCase   )

																		self.assertIsNotNone(__lowerCAmelCase   )
										finally:
														tf.keras.mixed_precision.set_global_policy("""float32"""   )


						@slow
						def 					__lowercase	(							self			:     List[Any]   ):
										lowerCAmelCase	,  lowerCAmelCase		=			self.model_tester.prepare_config_and_inputs_for_common()
										# head_mask and decoder_head_mask has different shapes than other input args
										if "head_mask" in inputs_dict:
														del inputs_dict["head_mask"]
										if "decoder_head_mask" in inputs_dict:
														del inputs_dict["decoder_head_mask"]
										if "cross_attn_head_mask" in inputs_dict:
														del inputs_dict["cross_attn_head_mask"]
										lowerCAmelCase		=			{
										    module_member
										    for model_class in self.all_model_classes
										    for module in (import_module(model_class.__module__   ),)
										    for module_member_name in dir(__lowerCAmelCase   )
										    if module_member_name.endswith("""MainLayer"""   )
										    for module_member in (getattr(__lowerCAmelCase  ,  __lowerCAmelCase   ),)
										    if isinstance(__lowerCAmelCase  ,  __lowerCAmelCase   )
										    and tf.keras.layers.Layer in module_member.__bases__
										    and getattr(__lowerCAmelCase  ,  """_keras_serializable"""  ,  __lowerCAmelCase   )
										}

										for main_layer_class in tf_main_layer_classes:
														# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
														if "T5" in main_layer_class.__name__:
																		# Take the same values than in TFT5ModelTester for this shared layer
																		lowerCAmelCase		=			TFSharedEmbeddings(self.model_tester.vocab_size  ,  self.model_tester.hidden_size  ,  name="""shared"""   )
																		lowerCAmelCase		=			False
																		lowerCAmelCase		=			main_layer_class(__lowerCAmelCase  ,  embed_tokens=__lowerCAmelCase   )
														else:
																		lowerCAmelCase		=			main_layer_class(__lowerCAmelCase   )

														lowerCAmelCase		=			{
														    name: tf.keras.Input(tensor.shape[1:]  ,  dtype=tensor.dtype   ) for name, tensor in inputs_dict.items()
														}

														if hasattr(self.model_tester  ,  """num_labels"""   ):
																		lowerCAmelCase		=			self.model_tester.num_labels
														else:
																		lowerCAmelCase		=			2

														lowerCAmelCase		=			tf.data.Dataset.from_tensor_slices(
														    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)   ))   ).batch(1   )

														lowerCAmelCase		=			main_layer(__lowerCAmelCase   )[0]
														lowerCAmelCase		=			tf.keras.layers.Dense(__lowerCAmelCase  ,  activation="""softmax"""  ,  name="""outputs"""   )(__lowerCAmelCase   )
														lowerCAmelCase		=			tf.keras.models.Model(inputs=__lowerCAmelCase  ,  outputs=[outputs]   )

														model.compile(loss="""binary_crossentropy"""  ,  optimizer="""adam"""  ,  metrics=["""binary_accuracy"""]   )
														model.fit(__lowerCAmelCase  ,  epochs=1   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																		lowerCAmelCase		=			os.path.join(__lowerCAmelCase  ,  """keras_model.h5"""   )
																		model.save(__lowerCAmelCase   )
																		if "T5" in main_layer_class.__name__:
																						lowerCAmelCase		=			tf.keras.models.load_model(
																						    __lowerCAmelCase  ,  custom_objects={
																						        main_layer_class.__name__: main_layer_class,
																						        """TFSharedEmbeddings""": TFSharedEmbeddings,
																						    }  ,  )
																		else:
																						lowerCAmelCase		=			tf.keras.models.load_model(
																						    __lowerCAmelCase  ,  custom_objects={main_layer_class.__name__: main_layer_class}   )
																		assert isinstance(__lowerCAmelCase  ,  tf.keras.Model   )
																		model(__lowerCAmelCase   )


						@slow
						def 					__lowercase	(							self			:     Any   ):
										lowerCAmelCase	,  lowerCAmelCase		=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes[:2]:
														lowerCAmelCase		=			model_class(__lowerCAmelCase   )

														lowerCAmelCase		=			copy.deepcopy(__lowerCAmelCase   )

														if not self.is_encoder_decoder:
																		lowerCAmelCase		=			inputs["""input_ids"""]
																		del inputs["input_ids"]
														else:
																		lowerCAmelCase		=			inputs["""input_ids"""]
																		lowerCAmelCase		=			inputs.get("""decoder_input_ids"""  ,  __lowerCAmelCase   )
																		del inputs["input_ids"]
																		inputs.pop("""decoder_input_ids"""  ,  __lowerCAmelCase   )

														if not self.is_encoder_decoder:
																		lowerCAmelCase		=			model.get_input_embeddings()(__lowerCAmelCase   )
														else:
																		lowerCAmelCase		=			model.get_input_embeddings()(__lowerCAmelCase   )
																		lowerCAmelCase		=			model.get_input_embeddings()(__lowerCAmelCase   )

														lowerCAmelCase		=			self._prepare_for_class(__lowerCAmelCase  ,  __lowerCAmelCase   )

														@tf.function
														def run_in_graph_mode():
																		return model(__lowerCAmelCase   )

														lowerCAmelCase		=			run_in_graph_mode()
														self.assertIsNotNone(__lowerCAmelCase   )


						def 					__lowercase	(							self			:     Dict  ,  lowerCAmelCase			:     List[Any]  ,  lowerCAmelCase			:     List[Any]   ):
										lowerCAmelCase		=			[]
										if model.config.bos_token_id is not None:
														special_tokens.append(model.config.bos_token_id   )
										if model.config.pad_token_id is not None:
														special_tokens.append(model.config.pad_token_id   )
										if model.config.eos_token_id is not None:
														special_tokens.append(model.config.eos_token_id   )

										# create random bad tokens that are not special tokens
										lowerCAmelCase		=			[]
										while len(__lowerCAmelCase   ) < num_bad_tokens:
														lowerCAmelCase		=			tf.squeeze(ids_tensor((1, 1)  ,  self.model_tester.vocab_size   )  ,  0   ).numpy()[0]
														if token not in special_tokens:
																		bad_tokens.append(__lowerCAmelCase   )
										return bad_tokens


						def 					__lowercase	(							self			:     Tuple  ,  lowerCAmelCase			:     Any   ):


										for token_id in output_ids[0].numpy().tolist():
														self.assertGreaterEqual(__lowerCAmelCase  ,  0   )
														self.assertLess(__lowerCAmelCase  ,  self.model_tester.vocab_size   )




						def 					__lowercase	(							self			:     Optional[int]  ,  lowerCAmelCase			:     List[Any]  ,  lowerCAmelCase			:     Union[str, Any]   ):


										for bad_word_ids in bad_words_ids:
														# for all slices in batch
														for generated_ids_slice in generated_ids:
																		# for all word idx
																		for i in range(len(__lowerCAmelCase   )  ,  len(__lowerCAmelCase   )   ):
																						# if tokens match
																						if generated_ids_slice[i - len(__lowerCAmelCase   ) : i] == bad_word_ids:
																										return True
										return False











"""simple docstring"""







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, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import OwlViTImageProcessor






class  SCREAMING_SNAKE_CASE__   (   unittest.TestCase   ):


						def __init__(							self			:     int  ,  lowerCAmelCase			:     Optional[int]  ,  lowerCAmelCase			:     Optional[Any]=7  ,  lowerCAmelCase			:     str=3  ,  lowerCAmelCase			:     str=18  ,  lowerCAmelCase			:     Tuple=30  ,  lowerCAmelCase			:     Union[str, Any]=400  ,  lowerCAmelCase			:     List[str]=True  ,  lowerCAmelCase			:     List[str]=None  ,  lowerCAmelCase			:     int=True  ,  lowerCAmelCase			:     int=None  ,  lowerCAmelCase			:     Optional[int]=True  ,  lowerCAmelCase			:     Dict=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,  lowerCAmelCase			:     str=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,  lowerCAmelCase			:     Any=True  ,  ):
										lowerCAmelCase		=			parent
										lowerCAmelCase		=			batch_size
										lowerCAmelCase		=			num_channels
										lowerCAmelCase		=			image_size
										lowerCAmelCase		=			min_resolution
										lowerCAmelCase		=			max_resolution
										lowerCAmelCase		=			do_resize
										lowerCAmelCase		=			size if size is not None else {"""height""": 18, """width""": 18}
										lowerCAmelCase		=			do_center_crop
										lowerCAmelCase		=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
										lowerCAmelCase		=			do_normalize
										lowerCAmelCase		=			image_mean
										lowerCAmelCase		=			image_std
										lowerCAmelCase		=			do_convert_rgb




						def 					__lowercase	(							self			:     Tuple   ):
										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,
										}






@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE__   (   _a  ,			unittest.TestCase   ):
						_a        =  OwlViTImageProcessor if is_vision_available() else None


						def 					__lowercase	(							self			:     Union[str, Any]   ):
										lowerCAmelCase		=			OwlViTImageProcessingTester(self   )


						@property
						def 					__lowercase	(							self			:     List[Any]   ):
										return self.image_processor_tester.prepare_image_processor_dict()


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


						def 					__lowercase	(							self			:     List[Any]   ):
										lowerCAmelCase		=			self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size  ,  {"""height""": 18, """width""": 18}   )
										self.assertEqual(image_processor.crop_size  ,  {"""height""": 18, """width""": 18}   )

										lowerCAmelCase		=			self.image_processing_class.from_dict(self.image_processor_dict  ,  size=42  ,  crop_size=84   )
										self.assertEqual(image_processor.size  ,  {"""height""": 42, """width""": 42}   )
										self.assertEqual(image_processor.crop_size  ,  {"""height""": 84, """width""": 84}   )


						def 					__lowercase	(							self			:     str   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase   )

										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  Image.Image   )

										# Test not batched input
										lowerCAmelCase		=			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		=			image_processing(lowerCAmelCase  ,  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 					__lowercase	(							self			:     Tuple   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase  ,  numpify=lowerCAmelCase   )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  np.ndarray   )

										# Test not batched input
										lowerCAmelCase		=			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		=			image_processing(lowerCAmelCase  ,  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 					__lowercase	(							self			:     Optional[int]   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase  ,  torchify=lowerCAmelCase   )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  torch.Tensor   )

										# Test not batched input
										lowerCAmelCase		=			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		=			image_processing(lowerCAmelCase  ,  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"""],
										    )  ,  )





