


"""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   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			"laion/clap-htsat-unfused"
										__lowerCAmelCase				    =			tempfile.mkdtemp()

							def 			snake_case				(     self    ,   **__a	):
										return RobertaTokenizer.from_pretrained(self.checkpoint    ,   **__a	)

							def 			snake_case				(     self    ,   **__a	):
										return ClapFeatureExtractor.from_pretrained(self.checkpoint    ,   **__a	)

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_tokenizer()
										__lowerCAmelCase				    =			self.get_feature_extractor()

										__lowerCAmelCase				    =			ClapProcessor(tokenizer=__a    ,   feature_extractor=__a	)

										processor.save_pretrained(self.tmpdirname	)
										__lowerCAmelCase				    =			ClapProcessor.from_pretrained(self.tmpdirname	)

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

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			ClapProcessor(tokenizer=self.get_tokenizer()    ,   feature_extractor=self.get_feature_extractor()	)
										processor.save_pretrained(self.tmpdirname	)

										__lowerCAmelCase				    =			self.get_tokenizer(bos_token="(BOS)"    ,   eos_token="(EOS)"	)
										__lowerCAmelCase				    =			self.get_feature_extractor(do_normalize=__a    ,   padding_value=1.0	)

										__lowerCAmelCase				    =			ClapProcessor.from_pretrained(
										    self.tmpdirname    ,   bos_token="(BOS)"    ,   eos_token="(EOS)"    ,   do_normalize=__a    ,   padding_value=1.0	)

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

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_feature_extractor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ClapProcessor(tokenizer=__a    ,   feature_extractor=__a	)

										__lowerCAmelCase				    =			floats_list((3, 10_00)	)

										__lowerCAmelCase				    =			feature_extractor(__a    ,   return_tensors="np"	)
										__lowerCAmelCase				    =			processor(audios=__a    ,   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	):
										__lowerCAmelCase				    =			self.get_feature_extractor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ClapProcessor(tokenizer=__a    ,   feature_extractor=__a	)

										__lowerCAmelCase				    =			"This is a test string"

										__lowerCAmelCase				    =			processor(text=__a	)

										__lowerCAmelCase				    =			tokenizer(__a	)

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_feature_extractor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ClapProcessor(tokenizer=__a    ,   feature_extractor=__a	)

										__lowerCAmelCase				    =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										__lowerCAmelCase				    =			processor.batch_decode(__a	)
										__lowerCAmelCase				    =			tokenizer.batch_decode(__a	)

										self.assertListEqual(__a    ,   __a	)



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_feature_extractor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ClapProcessor(tokenizer=__a    ,   feature_extractor=__a	)

										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"""


import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
	import torch
@is_pipeline_test
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							__UpperCAmelCase						:    Union[str, Any]				      =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			TextaTextGenerationPipeline(model=__a    ,   tokenizer=__a	)
										return generator, ["Something to write", "Something else"]

							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			generator("Something there"	)
										self.assertEqual(__a    ,   [{"generated_text": ANY(__a	)}]	)
										# These are encoder decoder, they don't just append to incoming string
										self.assertFalse(outputs[0]["generated_text"].startswith("Something there"	)	)

										__lowerCAmelCase				    =			generator(["This is great !", "Something else"]    ,   num_return_sequences=2    ,   do_sample=__a	)
										self.assertEqual(
										    __a    ,   [
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										    ]    ,   )

										__lowerCAmelCase				    =			generator(
										    ["This is great !", "Something else"]    ,   num_return_sequences=2    ,   batch_size=2    ,   do_sample=__a	)
										self.assertEqual(
										    __a    ,   [
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										    ]    ,   )

										with self.assertRaises(__a	):
													generator(4	)

							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline("text2text-generation"    ,   model="patrickvonplaten/t5-tiny-random"    ,   framework="pt"	)
										# do_sample=False necessary for reproducibility
										__lowerCAmelCase				    =			generator("Something there"    ,   do_sample=__a	)
										self.assertEqual(__a    ,   [{"generated_text": ""}]	)

										__lowerCAmelCase				    =			3
										__lowerCAmelCase				    =			generator(
										    "Something there"    ,   num_return_sequences=__a    ,   num_beams=__a    ,   )
										__lowerCAmelCase				    =			[
										    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
										    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
										    {"generated_text": ""},
										]
										self.assertEqual(__a    ,   __a	)

										__lowerCAmelCase				    =			generator("This is a test"    ,   do_sample=__a    ,   num_return_sequences=2    ,   return_tensors=__a	)
										self.assertEqual(
										    __a    ,   [
										        {"generated_token_ids": ANY(torch.Tensor	)},
										        {"generated_token_ids": ANY(torch.Tensor	)},
										    ]    ,   )
										__lowerCAmelCase				    =			generator.model.config.eos_token_id
										__lowerCAmelCase				    =			"<pad>"
										__lowerCAmelCase				    =			generator(
										    ["This is a test", "This is a second test"]    ,   do_sample=__a    ,   num_return_sequences=2    ,   batch_size=2    ,   return_tensors=__a    ,   )
										self.assertEqual(
										    __a    ,   [
										        [
										            {"generated_token_ids": ANY(torch.Tensor	)},
										            {"generated_token_ids": ANY(torch.Tensor	)},
										        ],
										        [
										            {"generated_token_ids": ANY(torch.Tensor	)},
										            {"generated_token_ids": ANY(torch.Tensor	)},
										        ],
										    ]    ,   )



							@require_tf
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline("text2text-generation"    ,   model="patrickvonplaten/t5-tiny-random"    ,   framework="tf"	)
										# do_sample=False necessary for reproducibility
										__lowerCAmelCase				    =			generator("Something there"    ,   do_sample=__a	)
										self.assertEqual(__a    ,   [{"generated_text": ""}]	)




