



'''simple docstring'''


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  """openai/whisper-base"""
    _SCREAMING_SNAKE_CASE	  =  (
        """This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
        """transcribed text."""
    )
    _SCREAMING_SNAKE_CASE	  =  """transcriber"""
    _SCREAMING_SNAKE_CASE	  =  WhisperProcessor
    _SCREAMING_SNAKE_CASE	  =  WhisperForConditionalGeneration

    _SCREAMING_SNAKE_CASE	  =  ["""audio"""]
    _SCREAMING_SNAKE_CASE	  =  ["""text"""]






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Any ):


       """simple docstring"""







       return self.pre_processor(UpperCamelCase__  ,						return_tensors='pt' ).input_features






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					Any ):


       """simple docstring"""







       return self.model.generate(inputs=UpperCamelCase__ )






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       return self.pre_processor.batch_decode(UpperCamelCase__  ,						skip_special_tokens=UpperCamelCase__ )[0]








'''simple docstring'''







import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


lowerCAmelCase_					:    Any      	=			get_tests_dir('fixtures')
lowerCAmelCase_					:    Union[str, Any]      	=			get_tests_dir('fixtures/dummy_feature_extractor_config.json')
lowerCAmelCase_					:    Dict      	=			get_tests_dir('fixtures/dummy-config.json')
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= 0




				def   UpperCamelCase__   (  self     :					str					):
								_a								= AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h"					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= AutoFeatureExtractor.from_pretrained(__a					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								with tempfile.TemporaryDirectory() as tmpdirname:
												_a								= WavaVecaConfig()

												# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
												_a								= AutoFeatureExtractor.from_pretrained(__a					).to_dict()

												config_dict.pop("feature_extractor_type"					)
												_a								= WavaVecaFeatureExtractor(**__a					)

												# save in new folder
												model_config.save_pretrained(__a					)
												config.save_pretrained(__a					)

												_a								= AutoFeatureExtractor.from_pretrained(__a					)

												# make sure private variable is not incorrectly saved
												_a								= json.loads(config.to_json_string()					)
												self.assertTrue("_processor_class" not in dict_as_saved					)

								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= AutoFeatureExtractor.from_pretrained(__a					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								with self.assertRaisesRegex(
								    __a					,				"bert-base is not a local folder and is not a valid model identifier"					):
												_a								= AutoFeatureExtractor.from_pretrained("bert-base"					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								with self.assertRaisesRegex(
								    __a					,				r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"					):
												_a								= AutoFeatureExtractor.from_pretrained(__a					,				revision="aaaaaa"					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								with self.assertRaisesRegex(
								    __a					,				"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."					,				):
												_a								= AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model"					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								# If remote code is not set, we will time out when asking whether to load the model.
								with self.assertRaises(__a					):
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					)
								# If remote code is disabled, we can't load this config.
								with self.assertRaises(__a					):
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					,				trust_remote_code=__a					)

								_a								= AutoFeatureExtractor.from_pretrained(
								    "hf-internal-testing/test_dynamic_feature_extractor"					,				trust_remote_code=__a					)
								self.assertEqual(feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)

								# Test feature extractor can be reloaded.
								with tempfile.TemporaryDirectory() as tmp_dir:
												feature_extractor.save_pretrained(__a					)
												_a								= AutoFeatureExtractor.from_pretrained(__a					,				trust_remote_code=__a					)
								self.assertEqual(reloaded_feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)




				def   UpperCamelCase__   (  self     :					Any					):
								try:
												AutoConfig.register("custom"					,				__a					)
												AutoFeatureExtractor.register(__a					,				__a					)
												# Trying to register something existing in the Transformers library will raise an error
												with self.assertRaises(__a					):
																AutoFeatureExtractor.register(__a					,				__a					)

												# Now that the config is registered, it can be used as any other config with the auto-API
												_a								= CustomFeatureExtractor.from_pretrained(__a					)
												with tempfile.TemporaryDirectory() as tmp_dir:
																feature_extractor.save_pretrained(__a					)
																_a								= AutoFeatureExtractor.from_pretrained(__a					)
																self.assertIsInstance(__a					,				__a					)

								finally:
												if "custom" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["custom"]
												if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]




				def   UpperCamelCase__   (  self     :					Tuple					):
								class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






												"""simple docstring"""







												__a            =True

								try:
												AutoConfig.register("custom"					,				__a					)
												AutoFeatureExtractor.register(__a					,				__a					)
												# If remote code is not set, the default is to use local
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					)
												self.assertEqual(feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)
												self.assertTrue(feature_extractor.is_local					)

												# If remote code is disabled, we load the local one.
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					,				trust_remote_code=__a					)
												self.assertEqual(feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)
												self.assertTrue(feature_extractor.is_local					)

												# If remote is enabled, we load from the Hub
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					,				trust_remote_code=__a					)
												self.assertEqual(feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)
												self.assertTrue(not hasattr(__a					,				"is_local"					)					)

								finally:
												if "custom" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["custom"]
												if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
