




'''simple docstring'''







import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A									=get_tests_dir('fixtures/test_sentencepiece.model')

if is_sentencepiece_available():
   import sentencepiece as sp


A									=5
A									=10


@require_sentencepiece
@require_tokenizers
class     _a						(			__a							,							unittest.TestCase				):
       __a       :  Tuple            =      SpeechaTextTokenizer
       __a       :  Tuple            =      False
       __a       :  Tuple            =      True
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           super().setUp()

           UpperCAmelCase     =	sp.SentencePieceProcessor()
           spm_model.Load(snake_case__       )
           UpperCAmelCase     =	['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']

           vocab += [spm_model.IdToPiece(id_       ) for id_ in range(len(snake_case__       )       )]
           UpperCAmelCase     =	dict(zip(snake_case__     ,       range(len(snake_case__       )       )       )       )

           UpperCAmelCase     =	Path(self.tmpdirname       )
           save_json(snake_case__     ,       save_dir / VOCAB_FILES_NAMES['''vocab_file''']       )
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
               copyfile(snake_case__     ,       save_dir / VOCAB_FILES_NAMES['''spm_file''']       )

           UpperCAmelCase     =	SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
           tokenizer.save_pretrained(self.tmpdirname       )
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''<pad>'''
           UpperCAmelCase     =	1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__       )     ,       snake_case__       )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__       )     ,       snake_case__       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	list(self.get_tokenizer().get_vocab().keys()       )

           self.assertEqual(vocab_keys[0]     ,       '''<s>'''       )
           self.assertEqual(vocab_keys[1]     ,       '''<pad>'''       )
           self.assertEqual(vocab_keys[-1]     ,       '''j'''       )
           self.assertEqual(len(snake_case__       )     ,       1_001       )
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size     ,       1_001       )
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )

           UpperCAmelCase     =	tokenizer.tokenize('''This is a test'''       )
           self.assertListEqual(snake_case__     ,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(snake_case__       )     ,       [289, 50, 14, 174, 386]     ,       )

           UpperCAmelCase     =	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
           self.assertListEqual(
               snake_case__     ,       [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.''']     ,       )
           UpperCAmelCase     =	tokenizer.convert_tokens_to_ids(snake_case__       )
           self.assertListEqual(snake_case__     ,       [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8]       )

           UpperCAmelCase     =	tokenizer.convert_ids_to_tokens(snake_case__       )
           self.assertListEqual(
               snake_case__     ,       [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.''']     ,       )







       @slow
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	{'''input_ids''': [[3_791, 797, 31, 11, 64, 797, 31, 2_429, 433, 12, 1_176, 12, 20, 786, 915, 142, 2_413, 240, 37, 3_238, 797, 31, 11, 35, 93, 915, 142, 2_413, 240, 37, 5_540, 567, 1_276, 93, 37, 610, 40, 62, 455, 657, 1_042, 123, 780, 177, 37, 309, 241, 1_298, 514, 20, 292, 2_737, 114, 2_469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3_388, 511, 459, 4, 3_555, 40, 321, 302, 705, 4, 3_388, 511, 583, 326, 5, 5, 5, 62, 3_310, 560, 177, 2_680, 217, 1_508, 32, 31, 853, 418, 64, 583, 511, 1_605, 62, 35, 93, 560, 177, 2_680, 217, 1_508, 1_521, 64, 583, 511, 519, 62, 20, 1_515, 764, 20, 149, 261, 5_625, 7_972, 20, 5_540, 567, 1_276, 93, 3_925, 1_675, 11, 15, 802, 7_972, 576, 217, 1_508, 11, 35, 93, 1_253, 2_441, 15, 289, 652, 31, 416, 321, 3_842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2_681, 1_153, 3_434, 20, 5_540, 37, 567, 126, 1_253, 2_441, 3_376, 449, 210, 431, 1_563, 177, 767, 5_540, 11, 1_203, 472, 11, 2_953, 685, 285, 364, 706, 1_153, 20, 6_799, 20, 2_869, 20, 4_464, 126, 40, 2_429, 20, 1_040, 866, 2_664, 418, 20, 318, 20, 1_726, 186, 20, 265, 522, 35, 93, 2_191, 4_634, 20, 1_040, 12, 6_799, 15, 228, 2_356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_575, 2_666, 684, 1_582, 1_176, 12, 627, 149, 619, 20, 4_902, 563, 11, 20, 149, 261, 3_420, 2_356, 174, 142, 4_714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=snake_case__     ,       model_name='''facebook/s2t-small-mustc-en-de-st'''     ,       revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad'''     ,       )






@require_sentencepiece
class     _a						(			unittest.TestCase				):
       __a       :  Dict            =      '''valhalla/s2t_mustc_multilinguial_medium'''

       __a       :  Any            =      '''C\'est trop cool'''
       __a       :  Dict            =      '''Esto es genial'''
       @classmethod
       def 					A   (			cls		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name       )
           return cls
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           self.assertEqual(self.tokenizer.lang_code_to_id['''pt''']     ,       4       )
           self.assertEqual(self.tokenizer.lang_code_to_id['''ru''']     ,       6       )
           self.assertEqual(self.tokenizer.lang_code_to_id['''it''']     ,       9       )
           self.assertEqual(self.tokenizer.lang_code_to_id['''de''']     ,       11       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           self.assertEqual(self.tokenizer.vocab_size     ,       10_000       )
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           self.assertIn(snake_case__     ,       self.tokenizer.all_special_ids       )
           UpperCAmelCase     =	[ES_CODE, 4, 1_601, 47, 7_647, 2]
           UpperCAmelCase     =	self.tokenizer.decode(snake_case__     ,       skip_special_tokens=snake_case__       )
           UpperCAmelCase     =	self.tokenizer.decode(generated_ids[1:]     ,       skip_special_tokens=snake_case__       )
           self.assertEqual(snake_case__     ,       snake_case__       )
           self.assertNotIn(self.tokenizer.eos_token     ,       snake_case__       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''fr'''
           UpperCAmelCase     =	self.tokenizer(self.french_text       ).input_ids
           self.assertEqual(encoded[0]     ,       snake_case__       )
           self.assertEqual(encoded[-1]     ,       self.tokenizer.eos_token_id       )







       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''fr'''
           self.assertListEqual(self.tokenizer.prefix_tokens     ,       [FR_CODE]       )

           UpperCAmelCase     =	'''es'''
           self.assertListEqual(self.tokenizer.prefix_tokens     ,       [ES_CODE]       )










'''simple docstring'''







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


class     _a						(			__a							,							unittest.TestCase				):
       __a       :  Tuple            =      ShapEPipeline
       __a       :  Tuple            =      ["""prompt"""]
       __a       :  Tuple            =      ["""prompt"""]
       __a       :  str            =      [
           """num_images_per_prompt""",
           """num_inference_steps""",
           """generator""",
           """latents""",
           """guidance_scale""",
           """frame_size""",
           """output_type""",
           """return_dict""",
       ]
       __a       :  Union[str, Any]            =      False
       @property
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           return 32
       @property
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           return 32
       @property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           return self.time_input_dim * 4
       @property
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           return 8
       @property
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )
           return tokenizer
       @property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           torch.manual_seed(0       )
           UpperCAmelCase     =	CLIPTextConfig(
               bos_token_id=0     ,       eos_token_id=2     ,       hidden_size=self.text_embedder_hidden_size     ,       projection_dim=self.text_embedder_hidden_size     ,       intermediate_size=37     ,       layer_norm_eps=1E-05     ,       num_attention_heads=4     ,       num_hidden_layers=5     ,       pad_token_id=1     ,       vocab_size=1_000     ,       )
           return CLIPTextModelWithProjection(lowercase       )
       @property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           torch.manual_seed(0       )

           UpperCAmelCase     =	{
               '''num_attention_heads''': 2,
               '''attention_head_dim''': 16,
               '''embedding_dim''': self.time_input_dim,
               '''num_embeddings''': 32,
               '''embedding_proj_dim''': self.text_embedder_hidden_size,
               '''time_embed_dim''': self.time_embed_dim,
               '''num_layers''': 1,
               '''clip_embed_dim''': self.time_input_dim * 2,
               '''additional_embeddings''': 0,
               '''time_embed_act_fn''': '''gelu''',
               '''norm_in_type''': '''layer''',
               '''encoder_hid_proj_type''': None,
               '''added_emb_type''': None,
           }

           UpperCAmelCase     =	PriorTransformer(**lowercase       )
           return model
       @property
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           torch.manual_seed(0       )

           UpperCAmelCase     =	{
               '''param_shapes''': (
                   (self.renderer_dim, 93),
                   (self.renderer_dim, 8),
                   (self.renderer_dim, 8),
                   (self.renderer_dim, 8),
               ),
               '''d_latent''': self.time_input_dim,
               '''d_hidden''': self.renderer_dim,
               '''n_output''': 12,
               '''background''': (
                   0.1,
                   0.1,
                   0.1,
               ),
           }
           UpperCAmelCase     =	ShapERenderer(**lowercase       )
           return model
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.dummy_prior
           UpperCAmelCase     =	self.dummy_text_encoder
           UpperCAmelCase     =	self.dummy_tokenizer
           UpperCAmelCase     =	self.dummy_renderer

           UpperCAmelCase     =	HeunDiscreteScheduler(
               beta_schedule='''exp'''     ,       num_train_timesteps=1_024     ,       prediction_type='''sample'''     ,       use_karras_sigmas=lowercase     ,       clip_sample=lowercase     ,       clip_sample_range=1.0     ,       )
           UpperCAmelCase     =	{
               '''prior''': prior,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''renderer''': renderer,
               '''scheduler''': scheduler,
           }

           return components
       def 					A   (			self		:			Dict     ,       lowercase		:			Optional[int]     ,       lowercase		:			Optional[int]=0       ):



           '''simple docstring'''

           if str(lowercase       ).startswith('''mps'''       ):
               UpperCAmelCase     =	torch.manual_seed(lowercase       )
           else:
               UpperCAmelCase     =	torch.Generator(device=lowercase       ).manual_seed(lowercase       )
           UpperCAmelCase     =	{
               '''prompt''': '''horse''',
               '''generator''': generator,
               '''num_inference_steps''': 1,
               '''frame_size''': 32,
               '''output_type''': '''np''',
           }
           return inputs
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''cpu'''

           UpperCAmelCase     =	self.get_dummy_components()

           UpperCAmelCase     =	self.pipeline_class(**lowercase       )
           UpperCAmelCase     =	pipe.to(lowercase       )

           pipe.set_progress_bar_config(disable=lowercase       )

           UpperCAmelCase     =	pipe(**self.get_dummy_inputs(lowercase       )       )
           UpperCAmelCase     =	output.images[0]
           UpperCAmelCase     =	image[0, -3:, -3:, -1]

           assert image.shape == (20, 32, 32, 3)

           UpperCAmelCase     =	np.array(
               [
                   0.0003_9216,
                   0.0003_9216,
                   0.0003_9216,
                   0.0003_9216,
                   0.0003_9216,
                   0.0003_9216,
                   0.0003_9216,
                   0.0003_9216,
                   0.0003_9216,
               ]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           self._test_inference_batch_consistent(batch_sizes=[1, 2]       )
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	torch_device == '''cpu'''
           UpperCAmelCase     =	True

           self._test_inference_batch_single_identical(
               batch_size=2     ,       test_max_difference=lowercase     ,       relax_max_difference=lowercase     ,       )







       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_dummy_components()
           UpperCAmelCase     =	self.pipeline_class(**lowercase       )
           UpperCAmelCase     =	pipe.to(lowercase       )
           pipe.set_progress_bar_config(disable=lowercase       )

           UpperCAmelCase     =	1
           UpperCAmelCase     =	2

           UpperCAmelCase     =	self.get_dummy_inputs(lowercase       )

           for key in inputs.keys():
               if key in self.batch_params:
                   UpperCAmelCase     =	batch_size * [inputs[key]]

           UpperCAmelCase     =	pipe(**lowercase     ,       num_images_per_prompt=lowercase       )[0]

           assert images.shape[0] == batch_size * num_images_per_prompt






@slow
@require_torch_gpu
class     _a						(			unittest.TestCase				):
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/shap_e/test_shap_e_np_out.npy'''       )
           UpperCAmelCase     =	ShapEPipeline.from_pretrained('''openai/shap-e'''       )
           UpperCAmelCase     =	pipe.to(lowercase       )
           pipe.set_progress_bar_config(disable=lowercase       )

           UpperCAmelCase     =	torch.Generator(device=lowercase       ).manual_seed(0       )

           UpperCAmelCase     =	pipe(
               '''a shark'''     ,       generator=lowercase     ,       guidance_scale=15.0     ,       num_inference_steps=64     ,       frame_size=64     ,       output_type='''np'''     ,       ).images[0]

           assert images.shape == (20, 64, 64, 3)

           assert_mean_pixel_difference(lowercase     ,       lowercase       )




