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


_SCREAMING_SNAKE_CASE   		=			get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")
@require_torch
class SCREAMING_SNAKE_CASE_		(			unittest.TestCase		):







   def 	lowerCamelCase_  (    self	:	List[str]							):





    """simple docstring"""


    UpperCamelCase      =			tempfile.mkdtemp()

    UpperCamelCase      =			SpeechTaTokenizer(lowerCamelCase_							)
    tokenizer.save_pretrained(self.tmpdirname							)

    UpperCamelCase      =			{
        """feature_size""": 1,
        """padding_value""": 0.0,
        """sampling_rate""": 1_6000,
        """do_normalize""": False,
        """num_mel_bins""": 80,
        """hop_length""": 16,
        """win_length""": 64,
        """win_function""": """hann_window""",
        """fmin""": 80,
        """fmax""": 7600,
        """mel_floor""": 1E-10,
        """reduction_factor""": 2,
        """return_attention_mask""": True,
    }

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







   def 	lowerCamelCase_  (    self	:	int      ,      **lowerCamelCase_	:	str							):





    """simple docstring"""


    return SpeechTaTokenizer.from_pretrained(self.tmpdirname      ,      **lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Dict      ,      **lowerCamelCase_	:	List[str]							):





    """simple docstring"""


    return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname      ,      **lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Union[str, Any]							):





    """simple docstring"""


    shutil.rmtree(self.tmpdirname							)







   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    UpperCamelCase      =			self.get_tokenizer()
    UpperCamelCase      =			self.get_feature_extractor()

    UpperCamelCase      =			SpeechTaProcessor(tokenizer=lowerCamelCase_      ,      feature_extractor=lowerCamelCase_							)

    processor.save_pretrained(self.tmpdirname							)
    UpperCamelCase      =			SpeechTaProcessor.from_pretrained(self.tmpdirname							)

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

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







   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    UpperCamelCase      =			SpeechTaProcessor(tokenizer=self.get_tokenizer()      ,      feature_extractor=self.get_feature_extractor()							)
    processor.save_pretrained(self.tmpdirname							)

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

    UpperCamelCase      =			SpeechTaProcessor.from_pretrained(
        self.tmpdirname      ,      bos_token="""(BOS)"""      ,      eos_token="""(EOS)"""      ,      do_normalize=lowerCamelCase_      ,      padding_value=1.0							)

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

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







   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    UpperCamelCase      =			self.get_feature_extractor()
    UpperCamelCase      =			self.get_tokenizer()

    UpperCamelCase      =			SpeechTaProcessor(tokenizer=lowerCamelCase_      ,      feature_extractor=lowerCamelCase_							)

    UpperCamelCase      =			floats_list((3, 1000)							)

    UpperCamelCase      =			feature_extractor(audio=lowerCamelCase_      ,      return_tensors="""np"""							)
    UpperCamelCase      =			processor(audio=lowerCamelCase_      ,      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 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    UpperCamelCase      =			self.get_feature_extractor()
    UpperCamelCase      =			self.get_tokenizer()

    UpperCamelCase      =			SpeechTaProcessor(tokenizer=lowerCamelCase_      ,      feature_extractor=lowerCamelCase_							)

    UpperCamelCase      =			floats_list((3, 1000)							)

    UpperCamelCase      =			feature_extractor(audio_target=lowerCamelCase_      ,      return_tensors="""np"""							)
    UpperCamelCase      =			processor(audio_target=lowerCamelCase_      ,      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 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    UpperCamelCase      =			self.get_feature_extractor()
    UpperCamelCase      =			self.get_tokenizer()

    UpperCamelCase      =			SpeechTaProcessor(tokenizer=lowerCamelCase_      ,      feature_extractor=lowerCamelCase_							)

    UpperCamelCase      =			"""This is a test string"""

    UpperCamelCase      =			processor(text=lowerCamelCase_							)
    UpperCamelCase      =			tokenizer(lowerCamelCase_							)

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







   def 	lowerCamelCase_  (    self	:	Dict							):





    """simple docstring"""


    UpperCamelCase      =			self.get_feature_extractor()
    UpperCamelCase      =			self.get_tokenizer()

    UpperCamelCase      =			SpeechTaProcessor(tokenizer=lowerCamelCase_      ,      feature_extractor=lowerCamelCase_							)

    UpperCamelCase      =			"""This is a test string"""

    UpperCamelCase      =			processor(text_target=lowerCamelCase_							)
    UpperCamelCase      =			tokenizer(lowerCamelCase_							)

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







   def 	lowerCamelCase_  (    self	:	List[str]							):





    """simple docstring"""


    UpperCamelCase      =			self.get_feature_extractor()
    UpperCamelCase      =			self.get_tokenizer()

    UpperCamelCase      =			SpeechTaProcessor(tokenizer=lowerCamelCase_      ,      feature_extractor=lowerCamelCase_							)

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

    UpperCamelCase      =			processor.batch_decode(lowerCamelCase_							)
    UpperCamelCase      =			tokenizer.batch_decode(lowerCamelCase_							)

    self.assertListEqual(lowerCamelCase_      ,      lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase      =			self.get_feature_extractor()
    UpperCamelCase      =			self.get_tokenizer()

    UpperCamelCase      =			SpeechTaProcessor(tokenizer=lowerCamelCase_      ,      feature_extractor=lowerCamelCase_							)

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

import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin
class SCREAMING_SNAKE_CASE_		(			unittest.TestCase     ,    __lowerCAmelCase		):







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase      =			load_tool("""document-question-answering"""							)
    self.tool.setup()
    UpperCamelCase      =			load_tool("""document-question-answering"""      ,      remote=lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase      =			load_dataset("""hf-internal-testing/example-documents"""      ,      split="""test"""							)
    UpperCamelCase      =			dataset[0]["""image"""]

    UpperCamelCase      =			self.tool(lowerCamelCase_      ,      """When is the coffee break?"""							)
    self.assertEqual(lowerCamelCase_      ,      """11-14 to 11:39 a.m."""							)







   def 	lowerCamelCase_  (    self	:	Union[str, Any]							):





    """simple docstring"""


    UpperCamelCase      =			load_dataset("""hf-internal-testing/example-documents"""      ,      split="""test"""							)
    UpperCamelCase      =			dataset[0]["""image"""]

    UpperCamelCase      =			self.remote_tool(lowerCamelCase_      ,      """When is the coffee break?"""							)
    self.assertEqual(lowerCamelCase_      ,      """11-14 to 11:39 a.m."""							)







   def 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


    UpperCamelCase      =			load_dataset("""hf-internal-testing/example-documents"""      ,      split="""test"""							)
    UpperCamelCase      =			dataset[0]["""image"""]

    self.tool(document=lowerCamelCase_      ,      question="""When is the coffee break?"""							)







   def 	lowerCamelCase_  (    self	:	Any							):





    """simple docstring"""


    UpperCamelCase      =			load_dataset("""hf-internal-testing/example-documents"""      ,      split="""test"""							)
    UpperCamelCase      =			dataset[0]["""image"""]

    UpperCamelCase      =			self.remote_tool(document=lowerCamelCase_      ,      question="""When is the coffee break?"""							)
    self.assertEqual(lowerCamelCase_      ,      """11-14 to 11:39 a.m."""							)
