import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
lowercase_  : Optional[Any]								=					logging.get_logger('transformers.models.speecht5')

lowercase_  : List[str]								=					{
    'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
    'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
    'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
    'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
lowercase_  : Union[str, Any]								=					{
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
lowercase_  : str								=					{
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
    'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
    'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
    'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
lowercase_  : Optional[Any]								=					{
    'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
    'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
    'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
    'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
    'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
    'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
    'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
    'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
    'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
    'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
    'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
    'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
lowercase_  : int								=					{
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
lowercase_  : Any								=					{
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
lowercase_  : Optional[int]								=					{
    'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
    'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
    'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
    'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
    'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
    'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
    'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
    'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
    'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
lowercase_  : Dict								=					{
    'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
    'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
    'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
    'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
    'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
    'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
    'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
    'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
    'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
    'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
    'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
    'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
    'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
lowercase_  : Any								=					{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowercase_  : Optional[int]								=					{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase_  : Any								=					{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase_  : Optional[int]								=					[]
lowercase_  : List[str]								=					[
    'encoder.version',
    'encoder.layers.*.norm_k.weight',
    'encoder.layers.*.norm_k.bias',
    'decoder.version',
    'decoder.layers.*.norm_k.weight',
    'decoder.layers.*.norm_k.bias',
    'decoder.pos_emb.pe_k',
    'speech_encoder_prenet.embed_positions._float_tensor',
    'text_decoder_prenet.embed_positions._float_tensor',
]
lowercase_  : List[str]								=					IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
lowercase_  : List[Any]								=					IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
lowercase_  : Any								=					IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]







def       A__   (						snake_case_			:		List[Any]							,					snake_case_			:		Dict							,					snake_case_			:		Any							,					snake_case_			:		Dict							,					snake_case_			:		List[str]   ):
 for attribute in key.split('''.'''   ):
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   getattr(snake_case_							,					snake_case_   )

 if weight_type is not None:
  SCREAMING_SNAKE_CASE__: Tuple=   getattr(snake_case_							,					snake_case_   ).shape
 else:
  SCREAMING_SNAKE_CASE__: int=   hf_pointer.shape

 if hf_shape != value.shape:
  raise ValueError(
      F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
      F' {value.shape} for {full_name}'   )

 if weight_type == "weight":
  SCREAMING_SNAKE_CASE__: Dict=   value
 elif weight_type == "weight_g":
  SCREAMING_SNAKE_CASE__: int=   value
 elif weight_type == "weight_v":
  SCREAMING_SNAKE_CASE__: Optional[int]=   value
 elif weight_type == "bias":
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   value
 elif weight_type == "running_mean":
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   value
 elif weight_type == "running_var":
  SCREAMING_SNAKE_CASE__: Tuple=   value
 elif weight_type == "num_batches_tracked":
  SCREAMING_SNAKE_CASE__: Any=   value
 else:
  SCREAMING_SNAKE_CASE__: int=   value

 logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'   )







def       A__   (						snake_case_			:		str							,					snake_case_			:		Tuple   ):
 for key in ignore_keys:
  if key.endswith('''.*'''   ):
   if name.startswith(key[:-1]   ):
    return True
  elif ".*." in key:
   SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: int=   key.split('''.*.'''   )
   if prefix in name and suffix in name:
    return True
  elif key in name:
   return True
 return False







def       A__   (						snake_case_			:		List[str]							,					snake_case_			:		Tuple							,					snake_case_			:		Union[str, Any]   ):
 SCREAMING_SNAKE_CASE__: Tuple=   []

 if task == "s2t":
  SCREAMING_SNAKE_CASE__: List[str]=   hf_model.speechta.encoder.prenet.feature_encoder
  SCREAMING_SNAKE_CASE__: Optional[Any]=   MAPPING_S2T
  SCREAMING_SNAKE_CASE__: Optional[int]=   IGNORE_KEYS_S2T
 elif task == "t2s":
  SCREAMING_SNAKE_CASE__: Tuple=   None
  SCREAMING_SNAKE_CASE__: Any=   MAPPING_T2S
  SCREAMING_SNAKE_CASE__: Any=   IGNORE_KEYS_T2S
 elif task == "s2s":
  SCREAMING_SNAKE_CASE__: List[Any]=   hf_model.speechta.encoder.prenet.feature_encoder
  SCREAMING_SNAKE_CASE__: int=   MAPPING_S2S
  SCREAMING_SNAKE_CASE__: Optional[Any]=   IGNORE_KEYS_S2S
 else:
  raise ValueError(F'Unsupported task: {task}'   )

 for name, value in fairseq_dict.items():
  if should_ignore(snake_case_							,					snake_case_   ):
   logger.info(F'{name} was ignored'   )
   continue

  SCREAMING_SNAKE_CASE__: Optional[Any]=   False
  if "conv_layers" in name:
   load_conv_layer(
       snake_case_							,					snake_case_							,					snake_case_							,					snake_case_							,					hf_model.config.feat_extract_norm == '''group'''							,					)
   SCREAMING_SNAKE_CASE__: List[Any]=   True
  else:
   for key, mapped_key in MAPPING.items():
    # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

    if "*" in key:
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: str=   key.split('''.*.'''   )
     if prefix in name and suffix in name:
      SCREAMING_SNAKE_CASE__: Tuple=   suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
    if key in name:
     SCREAMING_SNAKE_CASE__: Dict=   True
     if "*" in mapped_key:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   name.split(snake_case_   )[0].split('''.'''   )[-2]
      SCREAMING_SNAKE_CASE__: List[str]=   mapped_key.replace('''*'''							,					snake_case_   )
     if "weight_g" in name:
      SCREAMING_SNAKE_CASE__: Tuple=   '''weight_g'''
     elif "weight_v" in name:
      SCREAMING_SNAKE_CASE__: Optional[int]=   '''weight_v'''
     elif "bias" in name:
      SCREAMING_SNAKE_CASE__: List[str]=   '''bias'''
     elif "weight" in name:
      SCREAMING_SNAKE_CASE__: Dict=   '''weight'''
     elif "running_mean" in name:
      SCREAMING_SNAKE_CASE__: int=   '''running_mean'''
     elif "running_var" in name:
      SCREAMING_SNAKE_CASE__: List[str]=   '''running_var'''
     elif "num_batches_tracked" in name:
      SCREAMING_SNAKE_CASE__: Dict=   '''num_batches_tracked'''
     else:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   None
     set_recursively(snake_case_							,					snake_case_							,					snake_case_							,					snake_case_							,					snake_case_   )
    continue
  if not is_used:
   unused_weights.append(snake_case_   )

 logger.warning(F'Unused weights: {unused_weights}'   )







def       A__   (						snake_case_			:		Optional[Any]							,					snake_case_			:		Any							,					snake_case_			:		str							,					snake_case_			:		Optional[int]							,					snake_case_			:		List[Any]   ):
 SCREAMING_SNAKE_CASE__: Any=   full_name.split('''conv_layers.'''   )[-1]
 SCREAMING_SNAKE_CASE__: Optional[Any]=   name.split('''.'''   )
 SCREAMING_SNAKE_CASE__: Optional[int]=   int(items[0]   )
 SCREAMING_SNAKE_CASE__: Dict=   int(items[1]   )

 if type_id == 0:
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
    raise ValueError(
        F'{full_name} has size {value.shape}, but'
        F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'   )
   SCREAMING_SNAKE_CASE__: str=   value
   logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'   )
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
    raise ValueError(
        F'{full_name} has size {value.shape}, but'
        F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'   )
   SCREAMING_SNAKE_CASE__: Optional[Any]=   value
   logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'   )
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
    raise ValueError(
        F'{full_name} has size {value.shape}, but'
        F' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'   )
   SCREAMING_SNAKE_CASE__: str=   value
   logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
    raise ValueError(
        F'{full_name} has size {value.shape}, but'
        F' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'   )
   SCREAMING_SNAKE_CASE__: Tuple=   value
   logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
 else:
  unused_weights.append(snake_case_   )







@torch.no_grad()
def       A__   (						snake_case_			:		Dict							,					snake_case_			:		Tuple							,					snake_case_			:		Tuple							,					snake_case_			:		int=None							,					snake_case_			:		Any=None							,					snake_case_			:		str=None							,					):
 if config_path is not None:
  SCREAMING_SNAKE_CASE__: Optional[int]=   SpeechTaConfig.from_pretrained(snake_case_   )
 else:
  SCREAMING_SNAKE_CASE__: Tuple=   SpeechTaConfig()

 if task == "s2t":
  SCREAMING_SNAKE_CASE__: List[Any]=   config.max_text_positions
  SCREAMING_SNAKE_CASE__: List[str]=   SpeechTaForSpeechToText(snake_case_   )
 elif task == "t2s":
  SCREAMING_SNAKE_CASE__: List[str]=   1_876
  SCREAMING_SNAKE_CASE__: str=   600
  SCREAMING_SNAKE_CASE__: Tuple=   config.max_speech_positions
  SCREAMING_SNAKE_CASE__: str=   SpeechTaForTextToSpeech(snake_case_   )
 elif task == "s2s":
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   1_876
  SCREAMING_SNAKE_CASE__: Optional[int]=   config.max_speech_positions
  SCREAMING_SNAKE_CASE__: Tuple=   SpeechTaForSpeechToSpeech(snake_case_   )
 else:
  raise ValueError(F'Unknown task name: {task}'   )

 if vocab_path:
  SCREAMING_SNAKE_CASE__: List[str]=   SpeechTaTokenizer(snake_case_							,					model_max_length=config.max_text_positions   )

  # Mask token behaves like a normal word, i.e. include the space before it
  SCREAMING_SNAKE_CASE__: List[Any]=   AddedToken('''<mask>'''							,					lstrip=snake_case_							,					rstrip=snake_case_   )
  SCREAMING_SNAKE_CASE__: Dict=   mask_token
  tokenizer.add_special_tokens({'''mask_token''': mask_token}   )
  tokenizer.add_tokens(['''<ctc_blank>''']   )

 SCREAMING_SNAKE_CASE__: Union[str, Any]=   SpeechTaFeatureExtractor()
 SCREAMING_SNAKE_CASE__: List[Any]=   SpeechTaProcessor(tokenizer=snake_case_							,					feature_extractor=snake_case_   )
 processor.save_pretrained(snake_case_   )

 SCREAMING_SNAKE_CASE__: List[str]=   torch.load(snake_case_   )
 recursively_load_weights(fairseq_checkpoint['''model''']							,					snake_case_							,					snake_case_   )

 model.save_pretrained(snake_case_   )

 if repo_id:
  print('''Pushing to the hub...'''   )
  processor.push_to_hub(snake_case_   )
  model.push_to_hub(snake_case_   )


if __name__ == "__main__":
      lowercase_  : Any								=					argparse.ArgumentParser()
      parser.add_argument(
          '--task',
          default='s2t',
          type=str,
          help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
      )
      parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
      parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
      parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
      parser.add_argument(
          '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
      )
      parser.add_argument(
          '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
      )

      lowercase_  : Any								=					parser.parse_args()
      convert_speechta_checkpoint(
          args.task,
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.vocab_path,
          args.push_to_hub,
      )



"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


lowerCAmelCase__				=					r'\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `" / "`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `" // "`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `"train"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `"compressed"`)\n            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n            `"compressed"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a "dummy" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n'





@add_start_docstrings(__lowercase )
class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   '''rag'''
							UpperCAmelCase__      =   True

							def __init__(self							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=" / "							,							SCREAMING_SNAKE_CASE_=" // "							,							SCREAMING_SNAKE_CASE_=5							,							SCREAMING_SNAKE_CASE_=3_00							,							SCREAMING_SNAKE_CASE_=7_68							,							SCREAMING_SNAKE_CASE_=8							,							SCREAMING_SNAKE_CASE_="wiki_dpr"							,							SCREAMING_SNAKE_CASE_="train"							,							SCREAMING_SNAKE_CASE_="compressed"							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=0.0							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									super().__init__(
									    bos_token_id=SCREAMING_SNAKE_CASE_							,							pad_token_id=SCREAMING_SNAKE_CASE_							,							eos_token_id=SCREAMING_SNAKE_CASE_							,							decoder_start_token_id=SCREAMING_SNAKE_CASE_							,							forced_eos_token_id=SCREAMING_SNAKE_CASE_							,							is_encoder_decoder=SCREAMING_SNAKE_CASE_							,							prefix=SCREAMING_SNAKE_CASE_							,							vocab_size=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_							,							)
									assert (
									    "question_encoder" in kwargs and "generator" in kwargs
									), "Config has to be initialized with question_encoder and generator config"
									SCREAMING_SNAKE_CASE_    			=			kwargs.pop('''question_encoder'''     )
									SCREAMING_SNAKE_CASE_    			=			question_encoder_config.pop('''model_type'''     )
									SCREAMING_SNAKE_CASE_    			=			kwargs.pop('''generator'''     )
									SCREAMING_SNAKE_CASE_    			=			decoder_config.pop('''model_type'''     )

									from ..auto.configuration_auto import AutoConfig

									SCREAMING_SNAKE_CASE_    			=			AutoConfig.for_model(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			AutoConfig.for_model(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			reduce_loss
									SCREAMING_SNAKE_CASE_    			=			label_smoothing
									SCREAMING_SNAKE_CASE_    			=			exclude_bos_score
									SCREAMING_SNAKE_CASE_    			=			do_marginalize

									SCREAMING_SNAKE_CASE_    			=			title_sep
									SCREAMING_SNAKE_CASE_    			=			doc_sep
									SCREAMING_SNAKE_CASE_    			=			n_docs
									SCREAMING_SNAKE_CASE_    			=			max_combined_length

									SCREAMING_SNAKE_CASE_    			=			dataset
									SCREAMING_SNAKE_CASE_    			=			dataset_split
									SCREAMING_SNAKE_CASE_    			=			index_name

									SCREAMING_SNAKE_CASE_    			=			retrieval_vector_size
									SCREAMING_SNAKE_CASE_    			=			retrieval_batch_size
									SCREAMING_SNAKE_CASE_    			=			passages_path
									SCREAMING_SNAKE_CASE_    			=			index_path
									SCREAMING_SNAKE_CASE_    			=			use_dummy_dataset

									SCREAMING_SNAKE_CASE_    			=			output_retrieved

									SCREAMING_SNAKE_CASE_    			=			do_deduplication

									SCREAMING_SNAKE_CASE_    			=			use_cache

									if self.forced_eos_token_id is None:
											SCREAMING_SNAKE_CASE_    			=			getattr(self.generator							,							'''forced_eos_token_id'''							,							SCREAMING_SNAKE_CASE_     )

							@classmethod
							def _lowercase       (cls							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return cls(question_encoder=question_encoder_config.to_dict()							,							generator=generator_config.to_dict()							,							**SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			copy.deepcopy(self.__dict__     )
									SCREAMING_SNAKE_CASE_    			=			self.question_encoder.to_dict()
									SCREAMING_SNAKE_CASE_    			=			self.generator.to_dict()
									SCREAMING_SNAKE_CASE_    			=			self.__class__.model_type
									return output