


import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
snake_case    :   Dict	    =					logging.get_logger(__name__)

snake_case    :   int	    =					[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]
def     __lowerCamelCase						(			UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      if k == "embeddings.weight":
            return "shared.weight"

      for parlai_name, hf_name in PATTERNS:
            a						:Dict										=	k.replace(_lowercase			,  _lowercase						)

      if k.startswith('''encoder'''						):
            a						:str										=	k.replace('''.attn'''			,  '''.self_attn'''						)
            a						:List[str]										=	k.replace('''norm1'''			,  '''self_attn_layer_norm'''						)
            a						:Union[str, Any]										=	k.replace('''norm2'''			,  '''final_layer_norm'''						)
      elif k.startswith('''decoder'''						):
            a						:Optional[Any]										=	k.replace('''norm1'''			,  '''self_attn_layer_norm'''						)
            a						:Tuple										=	k.replace('''norm2'''			,  '''encoder_attn_layer_norm'''						)
            a						:List[str]										=	k.replace('''norm3'''			,  '''final_layer_norm'''						)
      return k
def     __lowerCamelCase						(			UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a						:Tuple										=	[
          '''model.encoder.layernorm_embedding.weight''',
          '''model.encoder.layernorm_embedding.bias''',
          '''model.decoder.layernorm_embedding.weight''',
          '''model.decoder.layernorm_embedding.bias''',
      ]
      for k in keys:
            a						:int										=	sd.pop(_lowercase						)
            a						:int										=	k.replace('''layernorm_embedding'''			,  '''layer_norm'''						)
            assert new_k not in sd
            a						:Optional[int]										=	v


snake_case    :   Tuple	    =					['''START''']
@torch.no_grad()
def     __lowerCamelCase						(			UpperCAmelCase_						:		Dict			,  UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a						:int										=	torch.load(_lowercase			,  map_location='''cpu'''						)
      a						:Dict										=	model['''model''']
      a						:List[str]										=	BlenderbotConfig.from_json_file(_lowercase						)
      a						:List[Any]										=	BlenderbotForConditionalGeneration(_lowercase						)
      a						:List[str]										=	m.model.state_dict().keys()
      a						:Union[str, Any]										=	[]
      a						:Optional[Any]										=	{}
      for k, v in sd.items():
            if k in IGNORE_KEYS:
                  continue

            a						:Dict										=	rename_state_dict_key(_lowercase						)
            if new_k not in valid_keys:
                  failures.append([k, new_k]						)
            else:
                  a						:Optional[int]										=	v
      if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
            rename_layernorm_keys(_lowercase						)
      m.model.load_state_dict(_lowercase			,  strict=_lowercase						)
      m.half()
      m.save_pretrained(_lowercase						)


if __name__ == "__main__":
     snake_case    :   Optional[int]	    =					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
     parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
     parser.add_argument(
         '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
     )
     snake_case    :   List[Any]	    =					parser.parse_args()
     convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)









# 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()





