


# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


snake_case    :   Dict	    =					get_logger(__name__)







class 		_snake_case  :
 SCREAMING_SNAKE_CASE__				=	'dummy_data'
 SCREAMING_SNAKE_CASE__				=	'datasets'
 SCREAMING_SNAKE_CASE__				=	False




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  _lowerCamelCase = False ,  _lowerCamelCase = True ,  _lowerCamelCase = None ,  ):
       a						:str										=	0
       a						:List[str]										=	dataset_name
       a						:str										=	cache_dir
       a						:List[str]										=	use_local_dummy_data
       a						:List[str]										=	config
       # download_callbacks take a single url as input
       a						:List[Callable]										=	download_callbacks or []
       # if False, it doesn't load existing files and it returns the paths of the dummy files relative
       # to the dummy_data zip file root
       a						:Optional[Any]										=	load_existing_dummy_data

       # TODO(PVP, QL) might need to make this more general
       a						:int										=	str(_lowerCamelCase   )
       # to be downloaded
       a						:Union[str, Any]										=	None
       a						:str										=	None




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       if self._dummy_file is None:
             a						:str										=	self.download_dummy_data()
       return self._dummy_file




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       if self.config is not None:
             # structure is dummy / config_name / version_name
             return os.path.join('''dummy''' ,  self.config.name ,  self.version_name   )
       # structure is dummy / version_name
       return os.path.join('''dummy''' ,  self.version_name   )




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return os.path.join(self.dummy_data_folder ,  '''dummy_data.zip'''   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Any										=	(
           self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
       )

       a						:Dict										=	cached_path(
           _lowerCamelCase ,  cache_dir=self.cache_dir ,  extract_compressed_file=_lowerCamelCase ,  force_extract=_lowerCamelCase   )

       return os.path.join(_lowerCamelCase ,  self.dummy_file_name   )




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return os.path.join(self.datasets_scripts_dir ,  self.dataset_name ,  self.dummy_zip_file   )




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       if self._bucket_url is None:
             a						:Optional[int]										=	hf_github_url(self.dataset_name ,  self.dummy_zip_file.replace(os.sep ,  '''/'''   )   )
       return self._bucket_url




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # return full path if its a dir
       if os.path.isdir(self.dummy_file   ):
             return self.dummy_file
       # else cut off path to file -> example `xsum`.
       return "/".join(self.dummy_file.replace(os.sep ,  '''/'''   ).split('''/'''   )[:-1]   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  *_lowerCamelCase   ):
       if self.load_existing_dummy_data:
             # dummy data is downloaded and tested
             a						:Optional[Any]										=	self.dummy_file
       else:
             # dummy data cannot be downloaded and only the path to dummy file is returned
             a						:Union[str, Any]										=	self.dummy_file_name

       # special case when data_url is a dict
       if isinstance(_lowerCamelCase ,  _lowerCamelCase   ):
             return self.create_dummy_data_dict(_lowerCamelCase ,  _lowerCamelCase   )
       elif isinstance(_lowerCamelCase ,  (list, tuple)   ):
             return self.create_dummy_data_list(_lowerCamelCase ,  _lowerCamelCase   )
       else:
             return self.create_dummy_data_single(_lowerCamelCase ,  _lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  *_lowerCamelCase   ):
       return self.download_and_extract(_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
       return self.download_and_extract(_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  *_lowerCamelCase ,  **_lowerCamelCase   ):
       return path




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return {}




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:List[Any]										=	{}
       for key, single_urls in data_url.items():
             for download_callback in self.download_callbacks:
                   if isinstance(_lowerCamelCase ,  _lowerCamelCase   ):
                         for single_url in single_urls:
                               download_callback(_lowerCamelCase   )
                   else:
                         a						:Optional[Any]										=	single_urls
                         download_callback(_lowerCamelCase   )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
             if isinstance(_lowerCamelCase ,  _lowerCamelCase   ):
                   a						:str										=	[os.path.join(_lowerCamelCase ,  urllib.parse.quote_plus(Path(_lowerCamelCase   ).name   )   ) for x in single_urls]
             else:
                   a						:List[Any]										=	single_urls
                   a						:List[Any]										=	os.path.join(_lowerCamelCase ,  urllib.parse.quote_plus(Path(_lowerCamelCase   ).name   )   )
             a						:str										=	value

       # make sure that values are unique
       if all(isinstance(_lowerCamelCase ,  _lowerCamelCase   ) for i in dummy_data_dict.values()   ) and len(set(dummy_data_dict.values()   )   ) < len(
           dummy_data_dict.values()   ):
             # append key to value to make its name unique
             a						:Union[str, Any]										=	{key: value + key for key, value in dummy_data_dict.items()}

       return dummy_data_dict




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:Any										=	[]
       # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
       a						:Optional[int]										=	all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}''' ,  _lowerCamelCase   )   ) for url in data_url   )
       a						:int										=	all(
           url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'''   ) for url in data_url   )
       if data_url and (is_tf_records or is_pubmed_records):
             a						:List[Any]										=	[data_url[0]] * len(_lowerCamelCase   )
       for single_url in data_url:
             for download_callback in self.download_callbacks:
                   download_callback(_lowerCamelCase   )
             # we force the name of each key to be the last file / folder name of the url path
             # if the url has arguments, we need to encode them with urllib.parse.quote_plus
             a						:Optional[int]										=	os.path.join(_lowerCamelCase ,  urllib.parse.quote_plus(single_url.split('''/'''   )[-1]   )   )
             dummy_data_list.append(_lowerCamelCase   )
       return dummy_data_list




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
       for download_callback in self.download_callbacks:
             download_callback(_lowerCamelCase   )
       # we force the name of each key to be the last file / folder name of the url path
       # if the url has arguments, we need to encode them with urllib.parse.quote_plus
       a						:int										=	os.path.join(_lowerCamelCase ,  urllib.parse.quote_plus(data_url.split('''/'''   )[-1]   )   )
       if os.path.exists(_lowerCamelCase   ) or not self.load_existing_dummy_data:
             return value
       else:
             # Backward compatibility, maybe deprecate at one point.
             # For many datasets with single url calls to dl_manager.download_and_extract,
             # the dummy_data.zip file is actually the zipped downloaded file
             # while now we expected the dummy_data.zip file to be a directory containing
             # the downloaded file.
             return path_to_dummy_data




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       pass




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       pass




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       def _iter_archive_members(_lowerCamelCase   ):
             # this preserves the order of the members inside the ZIP archive
             a						:Dict										=	Path(self.dummy_file   ).parent
             a						:Any										=	path.relative_to(_lowerCamelCase   )
             with ZipFile(self.local_path_to_dummy_data   ) as zip_file:
                   a						:Optional[Any]										=	zip_file.namelist()
             for member in members:
                   if member.startswith(relative_path.as_posix()   ):
                         yield dummy_parent_path.joinpath(_lowerCamelCase   )

       a						:Dict										=	Path(_lowerCamelCase   )
       a						:List[Any]										=	_iter_archive_members(_lowerCamelCase   ) if self.use_local_dummy_data else path.rglob('''*'''   )
       for file_path in file_paths:
             if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')   ):
                   yield file_path.relative_to(_lowerCamelCase   ).as_posix(), file_path.open('''rb'''   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       if not isinstance(_lowerCamelCase ,  _lowerCamelCase   ):
             a						:Optional[Any]										=	[paths]
       for path in paths:
             if os.path.isfile(_lowerCamelCase   ):
                   if os.path.basename(_lowerCamelCase   ).startswith(('''.''', '''__''')   ):
                         return
                   yield path
             else:
                   for dirpath, dirnames, filenames in os.walk(_lowerCamelCase   ):
                         if os.path.basename(_lowerCamelCase   ).startswith(('''.''', '''__''')   ):
                               continue
                         dirnames.sort()
                         for filename in sorted(_lowerCamelCase   ):
                               if filename.startswith(('''.''', '''__''')   ):
                                     continue
                               yield os.path.join(_lowerCamelCase ,  _lowerCamelCase   )









# 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()





