'''simple docstring'''





import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list




@require_torchaudio
@require_sentencepiece
class 		SCREAMING_SNAKE_CASE     (					unittest.TestCase	):







				'''simple docstring'''





				def  snake_case__				(						self					:						Tuple					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						"laion/clap-htsat-unfused"
										_UpperCamelCase			:			Optional[int]   			=						tempfile.mkdtemp()





				def  snake_case__				(						self					:						int       ,		**lowercase__					:						Union[str, Any]					)							->List[str]:


										'''simple docstring'''




										return RobertaTokenizer.from_pretrained(self.checkpoint       ,		**lowercase__					)





				def  snake_case__				(						self					:						int       ,		**lowercase__					:						List[Any]					)							->List[Any]:


										'''simple docstring'''




										return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,		**lowercase__					)





				def  snake_case__				(						self					:						Union[str, Any]					)							->str:


										'''simple docstring'''




										shutil.rmtree(self.tmpdirname					)





				def  snake_case__				(						self					:						Union[str, Any]					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			Dict   			=						self.get_tokenizer()
										_UpperCamelCase			:			Dict   			=						self.get_feature_extractor()

										_UpperCamelCase			:			Optional[Any]   			=						ClapProcessor(tokenizer=lowercase__       ,		feature_extractor=lowercase__					)

										processor.save_pretrained(self.tmpdirname					)
										_UpperCamelCase			:			int   			=						ClapProcessor.from_pretrained(self.tmpdirname					)

										self.assertEqual(processor.tokenizer.get_vocab()       ,		tokenizer.get_vocab()					)
										self.assertIsInstance(processor.tokenizer       ,		lowercase__					)

										self.assertEqual(processor.feature_extractor.to_json_string()       ,		feature_extractor.to_json_string()					)
										self.assertIsInstance(processor.feature_extractor       ,		lowercase__					)





				def  snake_case__				(						self					:						int					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[int]   			=						ClapProcessor(tokenizer=self.get_tokenizer()       ,		feature_extractor=self.get_feature_extractor()					)
										processor.save_pretrained(self.tmpdirname					)

										_UpperCamelCase			:			Union[str, Any]   			=						self.get_tokenizer(bos_token="(BOS)"       ,		eos_token="(EOS)"					)
										_UpperCamelCase			:			Optional[int]   			=						self.get_feature_extractor(do_normalize=lowercase__       ,		padding_value=1.0					)

										_UpperCamelCase			:			Optional[Any]   			=						ClapProcessor.from_pretrained(
										    self.tmpdirname       ,		bos_token="(BOS)"       ,		eos_token="(EOS)"       ,		do_normalize=lowercase__       ,		padding_value=1.0					)

										self.assertEqual(processor.tokenizer.get_vocab()       ,		tokenizer_add_kwargs.get_vocab()					)
										self.assertIsInstance(processor.tokenizer       ,		lowercase__					)

										self.assertEqual(processor.feature_extractor.to_json_string()       ,		feature_extractor_add_kwargs.to_json_string()					)
										self.assertIsInstance(processor.feature_extractor       ,		lowercase__					)





				def  snake_case__				(						self					:						Optional[Any]					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						self.get_feature_extractor()
										_UpperCamelCase			:			Optional[int]   			=						self.get_tokenizer()

										_UpperCamelCase			:			Any   			=						ClapProcessor(tokenizer=lowercase__       ,		feature_extractor=lowercase__					)

										_UpperCamelCase			:			List[Any]   			=						floats_list((3, 1_000)					)

										_UpperCamelCase			:			Dict   			=						feature_extractor(lowercase__       ,		return_tensors="np"					)
										_UpperCamelCase			:			Optional[int]   			=						processor(audios=lowercase__       ,		return_tensors="np"					)

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





				def  snake_case__				(						self					:						Optional[int]					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						self.get_feature_extractor()
										_UpperCamelCase			:			Dict   			=						self.get_tokenizer()

										_UpperCamelCase			:			List[Any]   			=						ClapProcessor(tokenizer=lowercase__       ,		feature_extractor=lowercase__					)

										_UpperCamelCase			:			Tuple   			=						"This is a test string"

										_UpperCamelCase			:			Dict   			=						processor(text=lowercase__					)

										_UpperCamelCase			:			List[Any]   			=						tokenizer(lowercase__					)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key]       ,		encoded_processor[key]					)





				def  snake_case__				(						self					:						Optional[int]					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						self.get_feature_extractor()
										_UpperCamelCase			:			str   			=						self.get_tokenizer()

										_UpperCamelCase			:			str   			=						ClapProcessor(tokenizer=lowercase__       ,		feature_extractor=lowercase__					)

										_UpperCamelCase			:			str   			=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase			:			Tuple   			=						processor.batch_decode(lowercase__					)
										_UpperCamelCase			:			Optional[Any]   			=						tokenizer.batch_decode(lowercase__					)

										self.assertListEqual(lowercase__       ,		lowercase__					)







				def  snake_case__				(						self					:						Tuple					)							->Any:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						self.get_feature_extractor()
										_UpperCamelCase			:			str   			=						self.get_tokenizer()

										_UpperCamelCase			:			Any   			=						ClapProcessor(tokenizer=lowercase__       ,		feature_extractor=lowercase__					)

										self.assertListEqual(
										    processor.model_input_names[2:]       ,		feature_extractor.model_input_names       ,		msg="`processor` and `feature_extractor` model input names do not match"       ,		)



'''simple docstring'''





from typing import TYPE_CHECKING

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


lowerCAmelCase_	:		Union[str, Any]			=     {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		Tuple			=     [
			    """OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """OPTForCausalLM""",
			    """OPTModel""",
			    """OPTPreTrainedModel""",
			    """OPTForSequenceClassification""",
			    """OPTForQuestionAnswering""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		str			=     ["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		List[Any]			=     [
			    """FlaxOPTForCausalLM""",
			    """FlaxOPTModel""",
			    """FlaxOPTPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_opt import (
						    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    OPTForCausalLM,
						    OPTForQuestionAnswering,
						    OPTForSequenceClassification,
						    OPTModel,
						    OPTPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
			import sys

			lowerCAmelCase_	:		Any			=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


