


import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
    '''kwargs, expected'''			,  [
        ({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
        ({'''num_shards''': 10, '''max_num_jobs''': 1}, [range(10						)]),
        ({'''num_shards''': 10, '''max_num_jobs''': 10}, [range(UpperCAmelCase_			,  i + 1						) for i in range(10						)]),
        ({'''num_shards''': 1, '''max_num_jobs''': 10}, [range(1						)]),
        ({'''num_shards''': 10, '''max_num_jobs''': 3}, [range(0			,  4						), range(4			,  7						), range(7			,  10						)]),
        ({'''num_shards''': 3, '''max_num_jobs''': 10}, [range(0			,  1						), range(1			,  2						), range(2			,  3						)]),
    ]			,  )
def     __lowerCamelCase						(			UpperCAmelCase_						:		int			,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      a						:List[Any]										=	_distribute_shards(**UpperCAmelCase_						)
      assert out == expected
@pytest.mark.parametrize(
    '''gen_kwargs, max_num_jobs, expected'''			,  [
        ({'''foo''': 0}, 10, [{'''foo''': 0}]),
        ({'''shards''': [0, 1, 2, 3]}, 1, [{'''shards''': [0, 1, 2, 3]}]),
        ({'''shards''': [0, 1, 2, 3]}, 4, [{'''shards''': [0]}, {'''shards''': [1]}, {'''shards''': [2]}, {'''shards''': [3]}]),
        ({'''shards''': [0, 1]}, 4, [{'''shards''': [0]}, {'''shards''': [1]}]),
        ({'''shards''': [0, 1, 2, 3]}, 2, [{'''shards''': [0, 1]}, {'''shards''': [2, 3]}]),
    ]			,  )
def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		Optional[Any]			,  UpperCAmelCase_						:		Optional[Any]						):




      """simple docstring"""





      a						:Optional[Any]										=	_split_gen_kwargs(UpperCAmelCase_			,  UpperCAmelCase_						)
      assert out == expected






@pytest.mark.parametrize(
    '''gen_kwargs, expected'''			,  [
        ({'''foo''': 0}, 1),
        ({'''shards''': [0]}, 1),
        ({'''shards''': [0, 1, 2, 3]}, 4),
        ({'''shards''': [0, 1, 2, 3], '''foo''': 0}, 4),
        ({'''shards''': [0, 1, 2, 3], '''other''': (0, 1)}, 4),
        ({'''shards''': [0, 1, 2, 3], '''shards2''': [0, 1]}, RuntimeError),
    ]			,  )
def     __lowerCamelCase						(			UpperCAmelCase_						:		List[str]			,  UpperCAmelCase_						:		Dict						):




      """simple docstring"""





      if expected is RuntimeError:
            with pytest.raises(UpperCAmelCase_						):
                  _number_of_shards_in_gen_kwargs(UpperCAmelCase_						)
      else:
            a						:int										=	_number_of_shards_in_gen_kwargs(UpperCAmelCase_						)
            assert out == expected









# 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.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
     from datasets import load_dataset







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'microsoft/speecht5_tts'
 SCREAMING_SNAKE_CASE__				=	(
     'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '
     'text to read (in English) and returns a waveform object containing the sound.'
 )
 SCREAMING_SNAKE_CASE__				=	'text_reader'
 SCREAMING_SNAKE_CASE__				=	SpeechTaProcessor
 SCREAMING_SNAKE_CASE__				=	SpeechTaForTextToSpeech
 SCREAMING_SNAKE_CASE__				=	SpeechTaHifiGan

 SCREAMING_SNAKE_CASE__				=	['text']
 SCREAMING_SNAKE_CASE__				=	['audio']




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       if self.post_processor is None:
             a						:List[Any]										=	'''microsoft/speecht5_hifigan'''
       super().setup()




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None   ):
       a						:Tuple										=	self.pre_processor(text=_lowerCamelCase ,  return_tensors='''pt''' ,  truncation=_lowerCamelCase   )

       if speaker_embeddings is None:
             if not is_datasets_available():
                   raise ImportError('''Datasets needs to be installed if not passing speaker embeddings.'''   )

             a						:List[Any]										=	load_dataset('''Matthijs/cmu-arctic-xvectors''' ,  split='''validation'''   )
             a						:int										=	torch.tensor(embeddings_dataset[7305]['''xvector''']   ).unsqueeze(0   )

       return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       with torch.no_grad():
             return self.model.generate_speech(**_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       with torch.no_grad():
             return self.post_processor(_lowerCamelCase   ).cpu().detach()





