"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase     					=     {
    'configuration_big_bird': ['BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BigBirdConfig', 'BigBirdOnnxConfig'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     ['BigBirdTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     ['BigBirdTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BigBirdForCausalLM',
				    'BigBirdForMaskedLM',
				    'BigBirdForMultipleChoice',
				    'BigBirdForPreTraining',
				    'BigBirdForQuestionAnswering',
				    'BigBirdForSequenceClassification',
				    'BigBirdForTokenClassification',
				    'BigBirdLayer',
				    'BigBirdModel',
				    'BigBirdPreTrainedModel',
				    'load_tf_weights_in_big_bird',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'FlaxBigBirdForCausalLM',
				    'FlaxBigBirdForMaskedLM',
				    'FlaxBigBirdForMultipleChoice',
				    'FlaxBigBirdForPreTraining',
				    'FlaxBigBirdForQuestionAnswering',
				    'FlaxBigBirdForSequenceClassification',
				    'FlaxBigBirdForTokenClassification',
				    'FlaxBigBirdModel',
				    'FlaxBigBirdPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_big_bird import BigBirdTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_big_bird_fast import BigBirdTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_big_bird import (
								    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BigBirdForCausalLM,
								    BigBirdForMaskedLM,
								    BigBirdForMultipleChoice,
								    BigBirdForPreTraining,
								    BigBirdForQuestionAnswering,
								    BigBirdForSequenceClassification,
								    BigBirdForTokenClassification,
								    BigBirdLayer,
								    BigBirdModel,
								    BigBirdPreTrainedModel,
								    load_tf_weights_in_big_bird,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_big_bird import (
								    FlaxBigBirdForCausalLM,
								    FlaxBigBirdForMaskedLM,
								    FlaxBigBirdForMultipleChoice,
								    FlaxBigBirdForPreTraining,
								    FlaxBigBirdForQuestionAnswering,
								    FlaxBigBirdForSequenceClassification,
								    FlaxBigBirdForTokenClassification,
								    FlaxBigBirdModel,
								    FlaxBigBirdPreTrainedModel,
								)

else:
				import sys

				UpperCamelCase     					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference
@slow
@require_torch_gpu
class   UpperCAmelCase__				(    unittest.TestCase			):


	"""simple docstring"""







	def       A    (			self )    ->	List[str]:
							a_							:			List[str]     		=					"runwayml/stable-diffusion-v1-5"
							a_							:			Optional[int]     		=					TextToVideoZeroPipeline.from_pretrained(_SCREAMING_SNAKE_CASE       ,     torch_dtype=torch.floataa ).to("cuda" )
							a_							:			Dict     		=					DDIMScheduler.from_config(pipe.scheduler.config )
							a_							:			Optional[Any]     		=					torch.Generator(device="cuda" ).manual_seed(0 )

							a_							:			List[Any]     		=					"A bear is playing a guitar on Times Square"
							a_							:			List[Any]     		=					pipe(prompt=_SCREAMING_SNAKE_CASE       ,     generator=_SCREAMING_SNAKE_CASE ).images

							a_							:			Optional[int]     		=					load_pt(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt" )

							assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )




