
"""simple docstring"""


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
						from transformers import TvltImageProcessor

if is_speech_available():
						from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class  __lowerCamelCase  ( unittest.TestCase			):




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  '''ZinengTang/tvlt-base'''
							lowerCamelCase_					=  tempfile.mkdtemp()




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return TvltImageProcessor.from_pretrained(self.checkpoint							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return TvltFeatureExtractor.from_pretrained(self.checkpoint							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							shutil.rmtree(self.tmpdirname   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_feature_extractor()

							lowerCamelCase_					=  TvltProcessor(image_processor=UpperCAmelCase							,						feature_extractor=UpperCAmelCase   )
							processor.save_pretrained(self.tmpdirname   )
							lowerCamelCase_					=  TvltProcessor.from_pretrained(self.tmpdirname   )

							self.assertIsInstance(processor.feature_extractor							,						UpperCAmelCase   )
							self.assertIsInstance(processor.image_processor							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_feature_extractor()

							lowerCamelCase_					=  TvltProcessor(image_processor=UpperCAmelCase							,						feature_extractor=UpperCAmelCase   )

							lowerCamelCase_					=  np.ones([1_2000]   )

							lowerCamelCase_					=  feature_extractor(UpperCAmelCase							,						return_tensors='''np'''   )
							lowerCamelCase_					=  processor(audio=UpperCAmelCase							,						return_tensors='''np'''   )

							for key in audio_dict.keys():
										self.assertAlmostEqual(audio_dict[key].sum()							,						input_processor[key].sum()							,						delta=1e-2   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_feature_extractor()

							lowerCamelCase_					=  TvltProcessor(image_processor=UpperCAmelCase							,						feature_extractor=UpperCAmelCase   )

							lowerCamelCase_					=  np.ones([3, 224, 224]   )

							lowerCamelCase_					=  image_processor(UpperCAmelCase							,						return_tensors='''np'''   )
							lowerCamelCase_					=  processor(images=UpperCAmelCase							,						return_tensors='''np'''   )

							for key in image_dict.keys():
										self.assertAlmostEqual(image_dict[key].sum()							,						input_processor[key].sum()							,						delta=1e-2   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_feature_extractor()

							lowerCamelCase_					=  TvltProcessor(image_processor=UpperCAmelCase							,						feature_extractor=UpperCAmelCase   )

							lowerCamelCase_					=  np.ones([1_2000]   )
							lowerCamelCase_					=  np.ones([3, 224, 224]   )

							lowerCamelCase_					=  processor(audio=UpperCAmelCase							,						images=UpperCAmelCase   )

							self.assertListEqual(list(inputs.keys()   )							,						['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']   )

							# test if it raises when no input is passed
							with pytest.raises(UpperCAmelCase   ):
										processor()





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_feature_extractor()

							lowerCamelCase_					=  TvltProcessor(image_processor=UpperCAmelCase							,						feature_extractor=UpperCAmelCase   )

							self.assertListEqual(
							    processor.model_input_names							,						image_processor.model_input_names + feature_extractor.model_input_names							,						msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''							,						)





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A_    							=					{
    """configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """GPTNeoForCausalLM""",
						    """GPTNeoForQuestionAnswering""",
						    """GPTNeoForSequenceClassification""",
						    """GPTNeoForTokenClassification""",
						    """GPTNeoModel""",
						    """GPTNeoPreTrainedModel""",
						    """load_tf_weights_in_gpt_neo""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """FlaxGPTNeoForCausalLM""",
						    """FlaxGPTNeoModel""",
						    """FlaxGPTNeoPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_neo import (
												    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTNeoForCausalLM,
												    GPTNeoForQuestionAnswering,
												    GPTNeoForSequenceClassification,
												    GPTNeoForTokenClassification,
												    GPTNeoModel,
												    GPTNeoPreTrainedModel,
												    load_tf_weights_in_gpt_neo,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
						import sys

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



