
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
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
				__snake_case								=  """platform"""

				import jax
				import jax.numpy as jnp

				from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    shift_tokens_right,
				)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_=None	,	UpperCamelCase_=None	,	UpperCamelCase_=None	,	UpperCamelCase_=None	,	UpperCamelCase_=None	,	UpperCamelCase_=None	,	)		->		Dict:
					'''simple docstring'''


					if attention_mask is None:
										SCREAMING_SNAKE_CASE__													= np.where(input_ids != config.pad_token_id	,	1	,	0		)
					if decoder_attention_mask is None:
										SCREAMING_SNAKE_CASE__													= np.where(decoder_input_ids != config.pad_token_id	,	1	,	0		)
					if head_mask is None:
										SCREAMING_SNAKE_CASE__													= np.ones((config.encoder_layers, config.encoder_attention_heads)		)
					if decoder_head_mask is None:
										SCREAMING_SNAKE_CASE__													= np.ones((config.decoder_layers, config.decoder_attention_heads)		)
					if cross_attn_head_mask is None:
										SCREAMING_SNAKE_CASE__													= np.ones((config.decoder_layers, config.decoder_attention_heads)		)
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": attention_mask,
					}




class 						lowercase__  :
	def __init__(			self							:   Tuple       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   List[Any]=13       ,   UpperCAmelCase_							:   Tuple=7       ,   UpperCAmelCase_							:   Union[str, Any]=True       ,   UpperCAmelCase_							:   int=False       ,   UpperCAmelCase_							:   Union[str, Any]=99       ,   UpperCAmelCase_							:   Dict=16       ,   UpperCAmelCase_							:   Any=2       ,   UpperCAmelCase_							:   int=4       ,   UpperCAmelCase_							:   int=4       ,   UpperCAmelCase_							:   str="gelu"       ,   UpperCAmelCase_							:   List[Any]=0.1       ,   UpperCAmelCase_							:   Optional[int]=0.1       ,   UpperCAmelCase_							:   Any=32       ,   UpperCAmelCase_							:   List[str]=2       ,   UpperCAmelCase_							:   Any=1       ,   UpperCAmelCase_							:   int=0       ,   UpperCAmelCase_							:   int=0.02       ,   ):
						SCREAMING_SNAKE_CASE__													= parent
						SCREAMING_SNAKE_CASE__													= batch_size
						SCREAMING_SNAKE_CASE__													= seq_length
						SCREAMING_SNAKE_CASE__													= is_training
						SCREAMING_SNAKE_CASE__													= use_labels
						SCREAMING_SNAKE_CASE__													= vocab_size
						SCREAMING_SNAKE_CASE__													= hidden_size
						SCREAMING_SNAKE_CASE__													= num_hidden_layers
						SCREAMING_SNAKE_CASE__													= num_attention_heads
						SCREAMING_SNAKE_CASE__													= intermediate_size
						SCREAMING_SNAKE_CASE__													= hidden_act
						SCREAMING_SNAKE_CASE__													= hidden_dropout_prob
						SCREAMING_SNAKE_CASE__													= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__													= max_position_embeddings
						SCREAMING_SNAKE_CASE__													= eos_token_id
						SCREAMING_SNAKE_CASE__													= pad_token_id
						SCREAMING_SNAKE_CASE__													= bos_token_id
						SCREAMING_SNAKE_CASE__													= initializer_range
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,   self.vocab_size							)       ,   3       ,   self.vocab_size							)
						SCREAMING_SNAKE_CASE__													= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,   dtype=np.intaa							))       ,   -1							)

						SCREAMING_SNAKE_CASE__													= shift_tokens_right(UpperCAmelCase_       ,   1       ,   2							)

						SCREAMING_SNAKE_CASE__													= BlenderbotSmallConfig(
						    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_id=self.eos_token_id       ,   bos_token_id=self.bos_token_id       ,   pad_token_id=self.pad_token_id       ,   initializer_range=self.initializer_range       ,   use_cache=UpperCAmelCase_       ,   )
						SCREAMING_SNAKE_CASE__													= prepare_blenderbot_inputs_dict(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
						return config, inputs_dict
	def      A_				(			self							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= self.prepare_config_and_inputs()
						return config, inputs_dict
	def      A_				(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   Optional[int]       ,   UpperCAmelCase_							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= 20
						SCREAMING_SNAKE_CASE__													= model_class_name(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= model.encode(inputs_dict['input_ids']							)

						SCREAMING_SNAKE_CASE__													= (
						    inputs_dict["decoder_input_ids"],
						    inputs_dict["decoder_attention_mask"],
						)

						SCREAMING_SNAKE_CASE__													= model.init_cache(decoder_input_ids.shape[0]       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,   dtype='i4'							)

						SCREAMING_SNAKE_CASE__													= jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]       ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,   )
						SCREAMING_SNAKE_CASE__													= model.decode(
						    decoder_input_ids[:, :-1]       ,   UpperCAmelCase_       ,   decoder_attention_mask=UpperCAmelCase_       ,   past_key_values=UpperCAmelCase_       ,   decoder_position_ids=UpperCAmelCase_       ,   )

						SCREAMING_SNAKE_CASE__													= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,   dtype='i4'							)
						SCREAMING_SNAKE_CASE__													= model.decode(
						    decoder_input_ids[:, -1:]       ,   UpperCAmelCase_       ,   decoder_attention_mask=UpperCAmelCase_       ,   past_key_values=outputs_cache.past_key_values       ,   decoder_position_ids=UpperCAmelCase_       ,   )

						SCREAMING_SNAKE_CASE__													= model.decode(UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= 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      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= 20
						SCREAMING_SNAKE_CASE__													= model_class_name(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= model.encode(inputs_dict['input_ids']							)

						SCREAMING_SNAKE_CASE__													= (
						    inputs_dict["decoder_input_ids"],
						    inputs_dict["decoder_attention_mask"],
						)

						SCREAMING_SNAKE_CASE__													= jnp.concatenate(
						    [
						        decoder_attention_mask,
						        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
						    ]       ,   axis=-1       ,   )

						SCREAMING_SNAKE_CASE__													= model.init_cache(decoder_input_ids.shape[0]       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]       ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,   )

						SCREAMING_SNAKE_CASE__													= model.decode(
						    decoder_input_ids[:, :-1]       ,   UpperCAmelCase_       ,   decoder_attention_mask=UpperCAmelCase_       ,   past_key_values=UpperCAmelCase_       ,   decoder_position_ids=UpperCAmelCase_       ,   )
						SCREAMING_SNAKE_CASE__													= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,   dtype='i4'							)
						SCREAMING_SNAKE_CASE__													= model.decode(
						    decoder_input_ids[:, -1:]       ,   UpperCAmelCase_       ,   past_key_values=outputs_cache.past_key_values       ,   decoder_attention_mask=UpperCAmelCase_       ,   decoder_position_ids=UpperCAmelCase_       ,   )

						SCREAMING_SNAKE_CASE__													= model.decode(UpperCAmelCase_       ,   UpperCAmelCase_       ,   decoder_attention_mask=UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= 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}'							)





@require_flax
class 						lowercase__  (						unittest.TestCase  ):
	A__    :   Optional[int]				       =9_9
	def      A_				(			self							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= np.array(
						    [
						        [71, 82, 18, 33, 46, 91, 2],
						        [68, 34, 26, 58, 30, 82, 2],
						        [5, 97, 17, 39, 94, 40, 2],
						        [76, 83, 94, 25, 70, 78, 2],
						        [87, 59, 41, 35, 48, 66, 2],
						        [55, 13, 16, 58, 5, 2, 1],  # note padding
						        [64, 27, 31, 51, 12, 75, 2],
						        [52, 64, 86, 17, 83, 39, 2],
						        [48, 61, 9, 24, 71, 82, 2],
						        [26, 1, 60, 48, 22, 13, 2],
						        [21, 5, 62, 28, 14, 76, 2],
						        [45, 98, 37, 86, 59, 48, 2],
						        [70, 70, 50, 9, 28, 0, 2],
						    ]       ,   dtype=np.intaa       ,   )

						SCREAMING_SNAKE_CASE__													= input_ids.shape[0]
						SCREAMING_SNAKE_CASE__													= BlenderbotSmallConfig(
						    vocab_size=self.vocab_size       ,   d_model=24       ,   encoder_layers=2       ,   decoder_layers=2       ,   encoder_attention_heads=2       ,   decoder_attention_heads=2       ,   encoder_ffn_dim=32       ,   decoder_ffn_dim=32       ,   max_position_embeddings=48       ,   eos_token_id=2       ,   pad_token_id=1       ,   bos_token_id=0       ,   )
						return config, input_ids, batch_size
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= self._get_config_and_data()
						SCREAMING_SNAKE_CASE__													= FlaxBlenderbotSmallForConditionalGeneration(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= lm_model(input_ids=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= (batch_size, input_ids.shape[1], config.vocab_size)
						self.assertEqual(outputs['logits'].shape       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= BlenderbotSmallConfig(
						    vocab_size=self.vocab_size       ,   d_model=14       ,   encoder_layers=2       ,   decoder_layers=2       ,   encoder_attention_heads=2       ,   decoder_attention_heads=2       ,   encoder_ffn_dim=8       ,   decoder_ffn_dim=8       ,   max_position_embeddings=48       ,   )
						SCREAMING_SNAKE_CASE__													= FlaxBlenderbotSmallForConditionalGeneration(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]       ,   dtype=np.intaa							)
						SCREAMING_SNAKE_CASE__													= np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]       ,   dtype=np.intaa							)
						SCREAMING_SNAKE_CASE__													= lm_model(input_ids=UpperCAmelCase_       ,   decoder_input_ids=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= (*summary.shape, config.vocab_size)
						self.assertEqual(outputs['logits'].shape       ,   UpperCAmelCase_							)





	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]       ,   dtype=np.intaa							)
						SCREAMING_SNAKE_CASE__													= shift_tokens_right(UpperCAmelCase_       ,   1       ,   2							)
						SCREAMING_SNAKE_CASE__													= np.equal(UpperCAmelCase_       ,   1							).astype(np.floataa							).sum()
						SCREAMING_SNAKE_CASE__													= np.equal(UpperCAmelCase_       ,   1							).astype(np.floataa							).sum()
						self.assertEqual(shifted.shape       ,   input_ids.shape							)
						self.assertEqual(UpperCAmelCase_       ,   n_pad_before - 1							)
						self.assertTrue(np.equal(shifted[:, 0]       ,   2							).all()							)





@require_flax
class 						lowercase__  (						snake_case__     ,       unittest.TestCase     ,       snake_case__  ):
	A__    :   List[Any]				       =True
	A__    :   Tuple				       =(
	    (
	        FlaxBlenderbotSmallModel,
	        FlaxBlenderbotSmallForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	A__    :   List[Any]				       =(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= FlaxBlenderbotSmallModelTester(self							)
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward_with_attn_mask(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Dict							):
						SCREAMING_SNAKE_CASE__													= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__							):
																SCREAMING_SNAKE_CASE__													= self._prepare_for_class(UpperCAmelCase_       ,   UpperCAmelCase_							)
																SCREAMING_SNAKE_CASE__													= model_class(UpperCAmelCase_							)

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

																with self.subTest('JIT Enabled'							):
																					SCREAMING_SNAKE_CASE__													= encode_jitted(**UpperCAmelCase_							).to_tuple()

																with self.subTest('JIT Disabled'							):
																					with jax.disable_jit():
																										SCREAMING_SNAKE_CASE__													= 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      A_				(			self							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__							):
																SCREAMING_SNAKE_CASE__													= model_class(UpperCAmelCase_							)
																SCREAMING_SNAKE_CASE__													= model.encode(inputs_dict['input_ids']       ,   inputs_dict['attention_mask']							)

																SCREAMING_SNAKE_CASE__													= {
																    "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_							:   Dict       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   List[str]							):
																					return model.decode(
																					    decoder_input_ids=UpperCAmelCase_       ,   decoder_attention_mask=UpperCAmelCase_       ,   encoder_outputs=UpperCAmelCase_       ,   )

																with self.subTest('JIT Enabled'							):
																					SCREAMING_SNAKE_CASE__													= decode_jitted(**UpperCAmelCase_							).to_tuple()

																with self.subTest('JIT Disabled'							):
																					with jax.disable_jit():
																										SCREAMING_SNAKE_CASE__													= 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      A_				(			self							:   Union[str, Any]							):
						for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE__													= model_class_name.from_pretrained('facebook/blenderbot_small-90M'							)
											# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
											SCREAMING_SNAKE_CASE__													= np.ones((1, 1)							) * model.config.eos_token_id
											SCREAMING_SNAKE_CASE__													= model(UpperCAmelCase_							)
											self.assertIsNotNone(UpperCAmelCase_							)






'''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_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VivitImageProcessor

class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def __init__(       self						:				int						,					A						:				Dict						,					A						:				Optional[int]=7						,					A						:				Tuple=3						,					A						:				Optional[Any]=10						,					A						:				int=18						,					A						:				Dict=30						,					A						:				List[str]=400						,					A						:				int=True						,					A						:				Optional[Any]=None						,					A						:				Optional[Any]=True						,					A						:				List[Any]=[0.5, 0.5, 0.5]						,					A						:				List[str]=[0.5, 0.5, 0.5]						,					A						:				Optional[int]=None						,					):
             _UpperCAmelCase     :  Dict							      =    size if size is not None else {"shortest_edge": 18}
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 18, "width": 18}

             _UpperCAmelCase     :  Tuple							      =    parent
             _UpperCAmelCase     :  Any							      =    batch_size
             _UpperCAmelCase     :  Optional[int]							      =    num_channels
             _UpperCAmelCase     :  Optional[Any]							      =    num_frames
             _UpperCAmelCase     :  Any							      =    image_size
             _UpperCAmelCase     :  Dict							      =    min_resolution
             _UpperCAmelCase     :  Any							      =    max_resolution
             _UpperCAmelCase     :  Optional[int]							      =    do_resize
             _UpperCAmelCase     :  str							      =    size
             _UpperCAmelCase     :  List[Any]							      =    do_normalize
             _UpperCAmelCase     :  Any							      =    image_mean
             _UpperCAmelCase     :  Tuple							      =    image_std
             _UpperCAmelCase     :  Any							      =    crop_size





      def      _A	(       self						:				List[Any]       ):
             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "crop_size": self.crop_size,
             }

@require_torch
@require_vision
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						VivitImageProcessor if is_vision_available() else None




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Tuple							      =    VivitImageProcessingTester(self       )




      @property
      def      _A	(       self						:				Optional[Any]       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  str							      =    self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(A						,					"image_mean"       )       )
             self.assertTrue(hasattr(A						,					"image_std"       )       )
             self.assertTrue(hasattr(A						,					"do_normalize"       )       )
             self.assertTrue(hasattr(A						,					"do_resize"       )       )
             self.assertTrue(hasattr(A						,					"do_center_crop"       )       )
             self.assertTrue(hasattr(A						,					"size"       )       )




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    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}       )

             _UpperCAmelCase     :  Optional[int]							      =    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      _A	(       self						:				Tuple       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Optional[Any]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL videos
             _UpperCAmelCase     :  Any							      =    prepare_video_inputs(self.image_processor_tester						,					equal_resolution=A       )
             for video in video_inputs:
                    self.assertIsInstance(A						,					A       )
                    self.assertIsInstance(video[0]						,					Image.Image       )

             # Test not batched input
             _UpperCAmelCase     :  str							      =    image_processing(video_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     1,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  List[Any]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)




      def      _A	(       self						:				List[Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  List[Any]							      =    prepare_video_inputs(self.image_processor_tester						,					equal_resolution=A						,					numpify=A       )
             for video in video_inputs:
                    self.assertIsInstance(A						,					A       )
                    self.assertIsInstance(video[0]						,					np.ndarray       )

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

             # Test batched
             _UpperCAmelCase     :  Optional[int]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)





      def      _A	(       self						:				List[Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Optional[Any]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  Optional[int]							      =    prepare_video_inputs(self.image_processor_tester						,					equal_resolution=A						,					torchify=A       )
             for video in video_inputs:
                    self.assertIsInstance(A						,					A       )
                    self.assertIsInstance(video[0]						,					torch.Tensor       )

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

             # Test batched
             _UpperCAmelCase     :  List[Any]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

