



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	):







					'''simple docstring'''







					def  A__		(      self  ):
							UpperCAmelCase_						       =					"ZinengTang/tvlt-base"
							UpperCAmelCase_						       =					tempfile.mkdtemp()



					def  A__		(      self     ,						**lowerCAmelCase  ):
							return TvltImageProcessor.from_pretrained(self.checkpoint     ,						**snake_case__  )



					def  A__		(      self     ,						**lowerCAmelCase  ):
							return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,						**snake_case__  )



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



					def  A__		(      self  ):
							UpperCAmelCase_						       =					self.get_image_processor()
							UpperCAmelCase_						       =					self.get_feature_extractor()

							UpperCAmelCase_						       =					TvltProcessor(image_processor=snake_case__     ,						feature_extractor=snake_case__  )
							processor.save_pretrained(self.tmpdirname  )
							UpperCAmelCase_						       =					TvltProcessor.from_pretrained(self.tmpdirname  )

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



					def  A__		(      self  ):
							UpperCAmelCase_						       =					self.get_image_processor()
							UpperCAmelCase_						       =					self.get_feature_extractor()

							UpperCAmelCase_						       =					TvltProcessor(image_processor=snake_case__     ,						feature_extractor=snake_case__  )

							UpperCAmelCase_						       =					np.ones([1_2000]  )

							UpperCAmelCase_						       =					feature_extractor(snake_case__     ,						return_tensors="np"  )
							UpperCAmelCase_						       =					processor(audio=snake_case__     ,						return_tensors="np"  )

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



					def  A__		(      self  ):
							UpperCAmelCase_						       =					self.get_image_processor()
							UpperCAmelCase_						       =					self.get_feature_extractor()

							UpperCAmelCase_						       =					TvltProcessor(image_processor=snake_case__     ,						feature_extractor=snake_case__  )

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

							UpperCAmelCase_						       =					image_processor(snake_case__     ,						return_tensors="np"  )
							UpperCAmelCase_						       =					processor(images=snake_case__     ,						return_tensors="np"  )

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



					def  A__		(      self  ):
							UpperCAmelCase_						       =					self.get_image_processor()
							UpperCAmelCase_						       =					self.get_feature_extractor()

							UpperCAmelCase_						       =					TvltProcessor(image_processor=snake_case__     ,						feature_extractor=snake_case__  )

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

							UpperCAmelCase_						       =					processor(audio=snake_case__     ,						images=snake_case__  )

							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(snake_case__  ):
									processor()



					def  A__		(      self  ):
							UpperCAmelCase_						       =					self.get_image_processor()
							UpperCAmelCase_						       =					self.get_feature_extractor()

							UpperCAmelCase_						       =					TvltProcessor(image_processor=snake_case__     ,						feature_extractor=snake_case__  )

							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"     ,						)







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Tuple									= ['''RoFormerTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Any									= [
						    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RoFormerForCausalLM''',
						    '''RoFormerForMaskedLM''',
						    '''RoFormerForMultipleChoice''',
						    '''RoFormerForQuestionAnswering''',
						    '''RoFormerForSequenceClassification''',
						    '''RoFormerForTokenClassification''',
						    '''RoFormerLayer''',
						    '''RoFormerModel''',
						    '''RoFormerPreTrainedModel''',
						    '''load_tf_weights_in_roformer''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Dict									= [
						    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFRoFormerForCausalLM''',
						    '''TFRoFormerForMaskedLM''',
						    '''TFRoFormerForMultipleChoice''',
						    '''TFRoFormerForQuestionAnswering''',
						    '''TFRoFormerForSequenceClassification''',
						    '''TFRoFormerForTokenClassification''',
						    '''TFRoFormerLayer''',
						    '''TFRoFormerModel''',
						    '''TFRoFormerPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     str									= [
						    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FlaxRoFormerForMaskedLM''',
						    '''FlaxRoFormerForMultipleChoice''',
						    '''FlaxRoFormerForQuestionAnswering''',
						    '''FlaxRoFormerForSequenceClassification''',
						    '''FlaxRoFormerForTokenClassification''',
						    '''FlaxRoFormerModel''',
						    '''FlaxRoFormerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
						from .tokenization_roformer import RoFormerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roformer_fast import RoFormerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roformer import (
												    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoFormerForCausalLM,
												    RoFormerForMaskedLM,
												    RoFormerForMultipleChoice,
												    RoFormerForQuestionAnswering,
												    RoFormerForSequenceClassification,
												    RoFormerForTokenClassification,
												    RoFormerLayer,
												    RoFormerModel,
												    RoFormerPreTrainedModel,
												    load_tf_weights_in_roformer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roformer import (
												    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRoFormerForCausalLM,
												    TFRoFormerForMaskedLM,
												    TFRoFormerForMultipleChoice,
												    TFRoFormerForQuestionAnswering,
												    TFRoFormerForSequenceClassification,
												    TFRoFormerForTokenClassification,
												    TFRoFormerLayer,
												    TFRoFormerModel,
												    TFRoFormerPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roformer import (
												    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxRoFormerForMaskedLM,
												    FlaxRoFormerForMultipleChoice,
												    FlaxRoFormerForQuestionAnswering,
												    FlaxRoFormerForSequenceClassification,
												    FlaxRoFormerForTokenClassification,
												    FlaxRoFormerModel,
												    FlaxRoFormerPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE:     Any									= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)