



import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


UpperCAmelCase								=   """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""

class 	__snake_case (				unittest.TestCase     ,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def _a (							self						):
											a__			 =						load_tool("""summarization"""						)
											self.tool.setup()
											a__			 =						load_tool("""summarization"""					,					remote=a_						)


							def _a (							self						):
											a__			 =						self.tool(a_						)
											self.assertEqual(
											    a_					,					"""Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""					,					)


							def _a (							self						):
											a__			 =						self.remote_tool(a_						)
											self.assertEqual(
											    a_					,					"""Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""					,					)


							def _a (							self						):
											a__			 =						self.tool(text=a_						)
											self.assertEqual(
											    a_					,					"""Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""					,					)







							def _a (							self						):
											a__			 =						self.remote_tool(text=a_						)
											self.assertEqual(
											    a_					,					"""Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""					,					)






import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
	from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

	from .test_feature_extraction_speechta import floats_list


UpperCAmelCase								=   get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")

@require_torch
class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							def _a (							self						):
											a__			 =						tempfile.mkdtemp()

											a__			 =						SpeechTaTokenizer(a_						)
											tokenizer.save_pretrained(self.tmpdirname						)

											a__			 =						{
											    """feature_size""": 1,
											    """padding_value""": 0.0,
											    """sampling_rate""": 16_000,
											    """do_normalize""": False,
											    """num_mel_bins""": 80,
											    """hop_length""": 16,
											    """win_length""": 64,
											    """win_function""": """hann_window""",
											    """fmin""": 80,
											    """fmax""": 7_600,
											    """mel_floor""": 1E-1_0,
											    """reduction_factor""": 2,
											    """return_attention_mask""": True,
											}

											a__			 =						os.path.join(self.tmpdirname					,					a_						)
											with open(self.feature_extraction_file					,					"""w"""					,					encoding="""utf-8"""						) as fp:
															fp.write(json.dumps(a_						) + """\n"""						)


							def _a (							self					,					**a_						):
											return SpeechTaTokenizer.from_pretrained(self.tmpdirname					,					**a_						)


							def _a (							self					,					**a_						):
											return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname					,					**a_						)


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


							def _a (							self						):
											a__			 =						self.get_tokenizer()
											a__			 =						self.get_feature_extractor()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											processor.save_pretrained(self.tmpdirname						)
											a__			 =						SpeechTaProcessor.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 _a (							self						):
											a__			 =						SpeechTaProcessor(tokenizer=self.get_tokenizer()					,					feature_extractor=self.get_feature_extractor()						)
											processor.save_pretrained(self.tmpdirname						)

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

											a__			 =						SpeechTaProcessor.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 _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											a__			 =						floats_list((3, 1_000)						)

											a__			 =						feature_extractor(audio=a_					,					return_tensors="""np"""						)
											a__			 =						processor(audio=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 _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											a__			 =						floats_list((3, 1_000)						)

											a__			 =						feature_extractor(audio_target=a_					,					return_tensors="""np"""						)
											a__			 =						processor(audio_target=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 _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											a__			 =						"""This is a test string"""

											a__			 =						processor(text=a_						)
											a__			 =						tokenizer(a_						)

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


							def _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											a__			 =						"""This is a test string"""

											a__			 =						processor(text_target=a_						)
											a__			 =						tokenizer(a_						)

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


							def _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

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

											a__			 =						processor.batch_decode(a_						)
											a__			 =						tokenizer.batch_decode(a_						)

											self.assertListEqual(a_					,					a_						)







							def _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

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

