"""simple docstring"""





import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				__A  =				'''platform'''
				import jax
				import jax.numpy as jnp
				import numpy as np

				from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class  _snake_case     :
						snake_case__			  =			PegasusConfig
						snake_case__			  =			{}
						snake_case__			  =			'gelu'






						def __init__(       self      :			Any							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Tuple=13							,					UpperCAmelCase      :			Tuple=7							,					UpperCAmelCase      :			Tuple=True							,					UpperCAmelCase      :			Any=False							,					UpperCAmelCase      :			Any=99							,					UpperCAmelCase      :			Optional[int]=32							,					UpperCAmelCase      :			Dict=5							,					UpperCAmelCase      :			List[str]=4							,					UpperCAmelCase      :			Dict=37							,					UpperCAmelCase      :			int=0.1							,					UpperCAmelCase      :			Dict=0.1							,					UpperCAmelCase      :			Optional[Any]=20							,					UpperCAmelCase      :			Tuple=2							,					UpperCAmelCase      :			Optional[int]=1							,					UpperCAmelCase      :			Tuple=0							,					):
													__lowerCamelCase					:		Any		   =     parent
													__lowerCamelCase					:		Union[str, Any]		   =     batch_size
													__lowerCamelCase					:		int		   =     seq_length
													__lowerCamelCase					:		Optional[Any]		   =     is_training
													__lowerCamelCase					:		str		   =     use_labels
													__lowerCamelCase					:		Union[str, Any]		   =     vocab_size
													__lowerCamelCase					:		List[str]		   =     hidden_size
													__lowerCamelCase					:		Optional[Any]		   =     num_hidden_layers
													__lowerCamelCase					:		int		   =     num_attention_heads
													__lowerCamelCase					:		List[Any]		   =     intermediate_size

													__lowerCamelCase					:		Dict		   =     hidden_dropout_prob
													__lowerCamelCase					:		List[str]		   =     attention_probs_dropout_prob
													__lowerCamelCase					:		Any		   =     max_position_embeddings
													__lowerCamelCase					:		Tuple		   =     eos_token_id
													__lowerCamelCase					:		Any		   =     pad_token_id
													__lowerCamelCase					:		str		   =     bos_token_id






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Dict		   =     ids_tensor([self.batch_size, self.seq_length - 1]							,					self.vocab_size    ).clip(3							,					self.vocab_size    )
													__lowerCamelCase					:		List[Any]		   =     np.expand_dims(np.array([self.eos_token_id] * self.batch_size    )							,					1    )
													__lowerCamelCase					:		Tuple		   =     np.concatenate([input_ids, eos_tensor]							,					axis=1    )

													__lowerCamelCase					:		int		   =     ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

													__lowerCamelCase					:		List[Any]		   =     self.config_cls(
													    vocab_size=self.vocab_size							,					d_model=self.hidden_size							,					encoder_layers=self.num_hidden_layers							,					decoder_layers=self.num_hidden_layers							,					encoder_attention_heads=self.num_attention_heads							,					decoder_attention_heads=self.num_attention_heads							,					encoder_ffn_dim=self.intermediate_size							,					decoder_ffn_dim=self.intermediate_size							,					dropout=self.hidden_dropout_prob							,					attention_dropout=self.attention_probs_dropout_prob							,					max_position_embeddings=self.max_position_embeddings							,					eos_token_ids=[2]							,					bos_token_id=self.bos_token_id							,					pad_token_id=self.pad_token_id							,					decoder_start_token_id=self.pad_token_id							,					**self.config_updates							,					)
													__lowerCamelCase					:		List[Any]		   =     prepare_pegasus_inputs_dict(__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase    )
													return config, inputs_dict






						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			List[Any]    ):
													__lowerCamelCase					:		Optional[int]		   =     20
													__lowerCamelCase					:		Dict		   =     model_class_name(__UpperCamelCase    )

													__lowerCamelCase					:		Tuple		   =     model.encode(inputs_dict["input_ids"]    )

													__lowerCamelCase   ,  __lowerCamelCase					:		Optional[Any]		   =     (
													    inputs_dict["decoder_input_ids"],
													    inputs_dict["decoder_attention_mask"],
													)

													__lowerCamelCase					:		Dict		   =     model.init_cache(decoder_input_ids.shape[0]							,					__UpperCamelCase							,					__UpperCamelCase    )
													__lowerCamelCase					:		Dict		   =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,					dtype="i4"    )

													__lowerCamelCase					:		List[str]		   =     jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]							,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,					)
													__lowerCamelCase					:		Tuple		   =     model.decode(
													    decoder_input_ids[:, :-1]							,					__UpperCamelCase							,					decoder_attention_mask=__UpperCamelCase							,					past_key_values=__UpperCamelCase							,					decoder_position_ids=__UpperCamelCase							,					)

													__lowerCamelCase					:		Union[str, Any]		   =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,					dtype="i4"    )
													__lowerCamelCase					:		Union[str, Any]		   =     model.decode(
													    decoder_input_ids[:, -1:]							,					__UpperCamelCase							,					decoder_attention_mask=__UpperCamelCase							,					past_key_values=outputs_cache.past_key_values							,					decoder_position_ids=__UpperCamelCase							,					)

													__lowerCamelCase					:		List[Any]		   =     model.decode(__UpperCamelCase							,					__UpperCamelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
													self.parent.assertTrue(diff < 1E-3							,					msg=F"""Max diff is {diff}"""    )







						def       lowerCamelCase__     (       self      :			Any							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Union[str, Any]    ):
													__lowerCamelCase					:		Dict		   =     20
													__lowerCamelCase					:		Optional[Any]		   =     model_class_name(__UpperCamelCase    )

													__lowerCamelCase					:		Tuple		   =     model.encode(inputs_dict["input_ids"]    )

													__lowerCamelCase   ,  __lowerCamelCase					:		Any		   =     (
													    inputs_dict["decoder_input_ids"],
													    inputs_dict["decoder_attention_mask"],
													)

													__lowerCamelCase					:		Union[str, Any]		   =     jnp.concatenate(
													    [
													        decoder_attention_mask,
													        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])    ),
													    ]							,					axis=-1							,					)

													__lowerCamelCase					:		Any		   =     model.init_cache(decoder_input_ids.shape[0]							,					__UpperCamelCase							,					__UpperCamelCase    )
													__lowerCamelCase					:		List[Any]		   =     jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]							,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,					)

													__lowerCamelCase					:		int		   =     model.decode(
													    decoder_input_ids[:, :-1]							,					__UpperCamelCase							,					decoder_attention_mask=__UpperCamelCase							,					past_key_values=__UpperCamelCase							,					decoder_position_ids=__UpperCamelCase							,					)
													__lowerCamelCase					:		List[str]		   =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,					dtype="i4"    )
													__lowerCamelCase					:		Union[str, Any]		   =     model.decode(
													    decoder_input_ids[:, -1:]							,					__UpperCamelCase							,					past_key_values=outputs_cache.past_key_values							,					decoder_attention_mask=__UpperCamelCase							,					decoder_position_ids=__UpperCamelCase							,					)

													__lowerCamelCase					:		List[str]		   =     model.decode(__UpperCamelCase							,					__UpperCamelCase							,					decoder_attention_mask=__UpperCamelCase    )

													__lowerCamelCase					:		Optional[int]		   =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
													self.parent.assertTrue(diff < 1E-3							,					msg=F"""Max diff is {diff}"""    )


def    lowercase_   (   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	Tuple						,   _lowerCamelCase:	int						,   _lowerCamelCase:	str=None						,   _lowerCamelCase:	Union[str, Any]=None						,   ) ->      int:



							'''simple docstring'''

							if attention_mask is None:
														__lowerCamelCase					:		Tuple		   =     np.not_equal(_A						,   config.pad_token_id      ).astype(np.inta      )
							if decoder_attention_mask is None:
														__lowerCamelCase					:		Dict		   =     np.concatenate(
														    [
														        np.ones(decoder_input_ids[:, :1].shape						,   dtype=np.inta      ),
														        np.not_equal(decoder_input_ids[:, 1:]						,   config.pad_token_id      ).astype(np.inta      ),
														    ]						,   axis=-1						,   )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							}







@require_flax
class  _snake_case     (					_UpperCAmelCase				, unittest.TestCase		):
						snake_case__			  =			(
						    (
						        FlaxPegasusForConditionalGeneration,
						        FlaxPegasusModel,
						    )
						    if is_flax_available()
						    else ()
						)
						snake_case__			  =			(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
						snake_case__			  =			True
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		List[Any]		   =     FlaxPegasusModelTester(self    )
													__lowerCamelCase					:		Optional[Any]		   =     ConfigTester(self							,					config_class=__UpperCamelCase    )






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													self.config_tester.run_common_tests()






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase   ,  __lowerCamelCase					:		List[Any]		   =     self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes:
																				self.model_tester.check_use_cache_forward(__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase    )






						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase   ,  __lowerCamelCase					:		List[str]		   =     self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes:
																				self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase    )






						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase   ,  __lowerCamelCase					:		Tuple		   =     self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__    ):
																											__lowerCamelCase					:		Any		   =     self._prepare_for_class(__UpperCamelCase							,					__UpperCamelCase    )
																											__lowerCamelCase					:		Optional[Any]		   =     model_class(__UpperCamelCase    )

																											@jax.jit
																											def encode_jitted(UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			str=None							,					**UpperCAmelCase      :			int    ):
																																		return model.encode(input_ids=__UpperCamelCase							,					attention_mask=__UpperCamelCase    )

																											with self.subTest("JIT Enabled"    ):
																																		__lowerCamelCase					:		Dict		   =     encode_jitted(**__UpperCamelCase    ).to_tuple()

																											with self.subTest("JIT Disabled"    ):
																																		with jax.disable_jit():
																																									__lowerCamelCase					:		Optional[Any]		   =     encode_jitted(**__UpperCamelCase    ).to_tuple()

																											self.assertEqual(len(__UpperCamelCase    )							,					len(__UpperCamelCase    )    )
																											for jitted_output, output in zip(__UpperCamelCase							,					__UpperCamelCase    ):
																																		self.assertEqual(jitted_output.shape							,					output.shape    )






						def       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase   ,  __lowerCamelCase					:		Optional[int]		   =     self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__    ):
																											__lowerCamelCase					:		Any		   =     model_class(__UpperCamelCase    )
																											__lowerCamelCase					:		Optional[Any]		   =     model.encode(inputs_dict["input_ids"]							,					inputs_dict["attention_mask"]    )

																											__lowerCamelCase					:		List[str]		   =     {
																											    "decoder_input_ids": inputs_dict["decoder_input_ids"],
																											    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
																											    "encoder_outputs": encoder_outputs,
																											}

																											@jax.jit
																											def decode_jitted(UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Tuple    ):
																																		return model.decode(
																																		    decoder_input_ids=__UpperCamelCase							,					decoder_attention_mask=__UpperCamelCase							,					encoder_outputs=__UpperCamelCase							,					)

																											with self.subTest("JIT Enabled"    ):
																																		__lowerCamelCase					:		str		   =     decode_jitted(**__UpperCamelCase    ).to_tuple()

																											with self.subTest("JIT Disabled"    ):
																																		with jax.disable_jit():
																																									__lowerCamelCase					:		Union[str, Any]		   =     decode_jitted(**__UpperCamelCase    ).to_tuple()

																											self.assertEqual(len(__UpperCamelCase    )							,					len(__UpperCamelCase    )    )
																											for jitted_output, output in zip(__UpperCamelCase							,					__UpperCamelCase    ):
																																		self.assertEqual(jitted_output.shape							,					output.shape    )






						@slow
						def       lowerCamelCase__     (       self      :			int    ):






													for model_class_name in self.all_model_classes:
																				__lowerCamelCase					:		Any		   =     model_class_name.from_pretrained("google/pegasus-large"							,					from_pt=__UpperCamelCase    )
																				__lowerCamelCase					:		List[str]		   =     np.ones((1, 1)    )
																				__lowerCamelCase					:		Union[str, Any]		   =     model(__UpperCamelCase    )
																				self.assertIsNotNone(__UpperCamelCase    )







						@slow
						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase					:		str		   =     FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"    )
													__lowerCamelCase					:		Any		   =     PegasusTokenizer.from_pretrained("google/pegasus-xsum"    )

													__lowerCamelCase					:		str		   =     [
													    " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
													    " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
													]

													__lowerCamelCase					:		str		   =     [
													    "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
													    "Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
													]

													__lowerCamelCase					:		Optional[int]		   =     tokenizer(__UpperCamelCase							,					return_tensors="np"							,					truncation=__UpperCamelCase							,					max_length=512							,					padding=__UpperCamelCase    )
													__lowerCamelCase					:		int		   =     model.generate(**__UpperCamelCase							,					num_beams=2    ).sequences
													__lowerCamelCase					:		int		   =     tokenizer.batch_decode(__UpperCamelCase							,					skip_special_tokens=__UpperCamelCase    )
													assert tgt_text == decoded
"""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 LevitImageProcessor
class  _snake_case     (					unittest.TestCase		):






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Optional[int]=7							,					UpperCAmelCase      :			Any=3							,					UpperCAmelCase      :			str=18							,					UpperCAmelCase      :			str=30							,					UpperCAmelCase      :			Any=400							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			Optional[Any]=True							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			List[str]=True							,					UpperCAmelCase      :			int=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			Tuple=[0.5, 0.5, 0.5]							,					):
													__lowerCamelCase					:		Any		   =     size if size is not None else {"shortest_edge": 18}
													__lowerCamelCase					:		Dict		   =     crop_size if crop_size is not None else {"height": 18, "width": 18}
													__lowerCamelCase					:		List[str]		   =     parent
													__lowerCamelCase					:		int		   =     batch_size
													__lowerCamelCase					:		Any		   =     num_channels
													__lowerCamelCase					:		Tuple		   =     image_size
													__lowerCamelCase					:		int		   =     min_resolution
													__lowerCamelCase					:		List[Any]		   =     max_resolution
													__lowerCamelCase					:		List[str]		   =     do_resize
													__lowerCamelCase					:		str		   =     size
													__lowerCamelCase					:		Tuple		   =     do_center_crop
													__lowerCamelCase					:		Optional[int]		   =     crop_size
													__lowerCamelCase					:		Optional[Any]		   =     do_normalize
													__lowerCamelCase					:		Optional[Any]		   =     image_mean
													__lowerCamelCase					:		List[Any]		   =     image_std







						def       lowerCamelCase__     (       self      :			int    ):
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "do_center_crop": self.do_center_crop,
													    "size": self.size,
													    "crop_size": self.crop_size,
													}

@require_torch
@require_vision
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			LevitImageProcessor if is_vision_available() else None






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[int]		   =     LevitImageProcessingTester(self    )






						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Dict		   =     self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_mean"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_std"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_normalize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_resize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_center_crop"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"size"    )    )






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		str		   =     self.image_processing_class.from_dict(self.image_processor_dict    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 18}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 18, "width": 18}    )

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






						def       lowerCamelCase__     (       self      :			Dict    ):
													pass






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													__lowerCamelCase					:		List[str]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					Image.Image    )

													# Test not batched input
													__lowerCamelCase					:		Dict		   =     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					:		int		   =     image_processing(UpperCAmelCase							,					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       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													__lowerCamelCase					:		Tuple		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					numpify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					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					:		Dict		   =     image_processing(UpperCAmelCase							,					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       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													# Initialize image_processing
													__lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													__lowerCamelCase					:		List[Any]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					torchify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					torch.Tensor    )

													# Test not batched input
													__lowerCamelCase					:		List[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					:		List[str]		   =     image_processing(UpperCAmelCase							,					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"],
													    )							,					)