"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__  		=						logging.get_logger(__name__)

UpperCAmelCase__  		=						{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
UpperCAmelCase__  		=						[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  for attribute in key.split("""."""		):
    _UpperCAmelCase						 =      getattr(lowercase   ,lowercase		)

  if weight_type is not None:
    _UpperCAmelCase						 =      getattr(lowercase   ,lowercase		).shape
  else:
    _UpperCAmelCase						 =      hf_pointer.shape

  assert hf_shape == value.shape, (
      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":
    _UpperCAmelCase						 =      value
  elif weight_type == "weight_g":
    _UpperCAmelCase						 =      value
  elif weight_type == "weight_v":
    _UpperCAmelCase						 =      value
  elif weight_type == "bias":
    _UpperCAmelCase						 =      value
  else:
    _UpperCAmelCase						 =      value

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






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      []
  _UpperCAmelCase						 =      fairseq_model.state_dict()

  _UpperCAmelCase						 =      hf_model.feature_extractor

  # if encoder has different dim to decoder -> use proj_weight
  _UpperCAmelCase						 =      None

  for name, value in fairseq_dict.items():
    _UpperCAmelCase						 =      False
    if "conv_layers" in name:
      load_conv_layer(
          lowercase   ,lowercase   ,lowercase   ,lowercase   ,hf_model.config.feat_extract_norm == """group"""   ,)
      _UpperCAmelCase						 =      True
    elif name.split("""."""		)[0] == "proj":
      _UpperCAmelCase						 =      fairseq_model.proj
      _UpperCAmelCase						 =      True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
          _UpperCAmelCase						 =      True
          if "*" in mapped_key:
            _UpperCAmelCase						 =      name.split(lowercase		)[0].split("""."""		)[-2]
            _UpperCAmelCase						 =      mapped_key.replace("""*"""   ,lowercase		)
          if "weight_g" in name:
            _UpperCAmelCase						 =      """weight_g"""
          elif "weight_v" in name:
            _UpperCAmelCase						 =      """weight_v"""
          elif "bias" in name:
            _UpperCAmelCase						 =      """bias"""
          elif "weight" in name:
            _UpperCAmelCase						 =      """weight"""
          else:
            _UpperCAmelCase						 =      None
          set_recursively(lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase		)
        continue
    if not is_used:
      unused_weights.append(lowercase		)

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

  return proj_weight






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      full_name.split("""conv_layers."""		)[-1]
  _UpperCAmelCase						 =      name.split("""."""		)
  _UpperCAmelCase						 =      int(items[0]		)
  _UpperCAmelCase						 =      int(items[1]		)

  if type_id == 0:
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          f'''{full_name} has size {value.shape}, but'''
          f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
      )
      _UpperCAmelCase						 =      value
      logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          f'''{full_name} has size {value.shape}, but'''
          f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
      )
      _UpperCAmelCase						 =      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:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
          " found."
      )
      _UpperCAmelCase						 =      value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          f'''{full_name} has size {value.shape}, but'''
          f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
      )
      _UpperCAmelCase						 =      value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
  else:
    unused_weights.append(lowercase		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase ,				_UpperCAmelCase						 =      emb.weight.shape
  _UpperCAmelCase						 =      nn.Linear(lowercase   ,lowercase   ,bias=lowercase		)
  _UpperCAmelCase						 =      emb.weight.data
  return lin_layer






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  with open(lowercase   ,"""r"""   ,encoding="""utf-8"""		) as f:
    _UpperCAmelCase						 =      f.readlines()
    _UpperCAmelCase						 =      [line.split(""" """		)[0] for line in lines]

  _UpperCAmelCase						 =      len(lowercase		)

  _UpperCAmelCase						 =      {
      """<s>""": 0,
      """<pad>""": 1,
      """</s>""": 2,
      """<unk>""": 3,
  }

  vocab_dict.update(dict(zip(lowercase   ,range(4   ,num_words + 4		)		)		)		)
  return vocab_dict






@torch.no_grad()
def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,):







  """simple docstring"""
  _UpperCAmelCase						 =      WavaVecaConfig.from_pretrained(lowercase		)
  _UpperCAmelCase						 =      SpeechaTextaConfig.from_pretrained(
      lowercase   ,vocab_size=lowercase   ,decoder_layers=lowercase   ,do_stable_layer_norm=lowercase		)

  _UpperCAmelCase						 =      WavaVecaFeatureExtractor(
      feature_size=1   ,sampling_rate=1_60_00   ,padding_value=0   ,do_normalize=lowercase   ,return_attention_mask=lowercase   ,)

  _UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]   ,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
  _UpperCAmelCase						 =      model[0].eval()

  # set weights for wav2vec2 encoder
  _UpperCAmelCase						 =      WavaVecaModel(lowercase		)
  _UpperCAmelCase						 =      recursively_load_weights_wavaveca(model.encoder   ,lowercase		)

  _UpperCAmelCase						 =      SpeechaTextaForCausalLM(lowercase		)
  _UpperCAmelCase ,				_UpperCAmelCase						 =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()   ,strict=lowercase		)

  # set output linear layer
  unexpected_keys.remove("""embed_out"""		)
  _UpperCAmelCase						 =      nn.Parameter(model.decoder.embed_out.detach()		)

  # layer norm is init to identity matrix so leaving it is fine
  logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}'''		)
  logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''		)

  _UpperCAmelCase						 =      SpeechEncoderDecoderModel(encoder=lowercase   ,decoder=lowercase		)
  _UpperCAmelCase						 =      False

  # add projection layer
  _UpperCAmelCase						 =      nn.Parameter(projection_layer.weight		)
  _UpperCAmelCase						 =      nn.Parameter(projection_layer.bias		)

  _UpperCAmelCase						 =      create_vocab_dict(lowercase		)

  with open(os.path.join(lowercase   ,"""vocab.json"""		)   ,"""w"""		) as fp:
    json.dump(lowercase   ,lowercase		)

  _UpperCAmelCase						 =      SpeechaTextaTokenizer(os.path.join(lowercase   ,"""vocab.json"""		)		)
  tokenizer.save_pretrained(lowercase		)

  _UpperCAmelCase						 =      hf_wavavec.config.to_dict()
  _UpperCAmelCase						 =      tokenizer.pad_token_id
  _UpperCAmelCase						 =      tokenizer.bos_token_id
  _UpperCAmelCase						 =      tokenizer.eos_token_id
  _UpperCAmelCase						 =      """speech_to_text_2"""
  _UpperCAmelCase						 =      """wav2vec2"""

  _UpperCAmelCase						 =      SpeechEncoderDecoderConfig.from_dict(lowercase		)

  hf_wavavec.save_pretrained(lowercase		)
  feature_extractor.save_pretrained(lowercase		)


if __name__ == "__main__":
    UpperCAmelCase__  		=						argparse.ArgumentParser()
    parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
    parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
    parser.add_argument(
        """--encoder_config_path""",
        default="""facebook/wav2vec2-large-lv60""",
        type=str,
        help="""Path to hf encoder wav2vec2 checkpoint config""",
    )
    parser.add_argument(
        """--decoder_config_path""",
        default="""facebook/s2t-small-mustc-en-fr-st""",
        type=str,
        help="""Path to hf decoder s2t checkpoint config""",
    )
    parser.add_argument("""--vocab_size""", default=1_0_2_2_4, type=int, help="""Vocab size of decoder""")
    parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

    UpperCAmelCase__  		=						parser.parse_args()
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.dict_path,
        encoder_config_path=args.encoder_config_path,
        decoder_config_path=args.decoder_config_path,
        vocab_size=args.vocab_size,
        num_decoder_layers=args.num_decoder_layers,
    )







"""simple docstring"""


import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
    import tensorflow as tf

if is_torch_available():
    import torch


UpperCAmelCase__  		=						logging.get_logger(__name__)


class        a				:


    def __init__(					self	:  Tuple   ,				__lowerCAmelCase	:  str = None   ,				__lowerCAmelCase	:  uuid.UUID = None   ,				__lowerCAmelCase	:  Any=None   ,				__lowerCAmelCase	:  Tuple=None					):
      if not conversation_id:
        _UpperCAmelCase						 =      uuid.uuida()
      if past_user_inputs is None:
        _UpperCAmelCase						 =      []
      if generated_responses is None:
        _UpperCAmelCase						 =      []

      _UpperCAmelCase						 =      conversation_id
      _UpperCAmelCase						 =      past_user_inputs
      _UpperCAmelCase						 =      generated_responses
      _UpperCAmelCase						 =      text


    def __eq__(					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Union[str, Any]					):
      if not isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					):
        return False
      if self.uuid == other.uuid:
        return True
      return (
          self.new_user_input == other.new_user_input
          and self.past_user_inputs == other.past_user_inputs
          and self.generated_responses == other.generated_responses
      )


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  bool = False					):
      if self.new_user_input:
        if overwrite:
          logger.warning(
              f'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
              f'''with: "{text}".'''					)
          _UpperCAmelCase						 =      text
        else:
          logger.warning(
              f'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
              f'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'''					)
      else:
        _UpperCAmelCase						 =      text


    def      lowerCAmelCase_  (					self	:  int					):
      if self.new_user_input:
        self.past_user_inputs.append(self.new_user_input					)
      _UpperCAmelCase						 =      None


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  str					):
      self.generated_responses.append(__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      for user_input, generated_response in zip(self.past_user_inputs   ,				self.generated_responses					):
        yield True, user_input
        yield False, generated_response
      if self.new_user_input:
        yield True, self.new_user_input


    def __repr__(					self	:  Optional[int]					):
      _UpperCAmelCase						 =      f'''Conversation id: {self.uuid} \n'''
      for is_user, text in self.iter_texts():
        _UpperCAmelCase						 =      """user""" if is_user else """bot"""
        output += f'''{name} >> {text} \n'''
      return output



@add_end_docstrings(
    lowerCAmelCase_	,							R'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '	,							)
class        a				(		lowerCAmelCase_   ):


    def __init__(					self	:  List[Any]   ,				*__lowerCAmelCase	:  List[Any]   ,				**__lowerCAmelCase	:  Tuple					):
      super().__init__(*__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if self.tokenizer.pad_token_id is None:
        _UpperCAmelCase						 =      self.tokenizer.eos_token


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Dict=None   ,				__lowerCAmelCase	:  List[Any]=None   ,				__lowerCAmelCase	:  Optional[int]=None   ,				**__lowerCAmelCase	:  str					):
      _UpperCAmelCase						 =      {}
      _UpperCAmelCase						 =      {}
      _UpperCAmelCase						 =      {}

      if min_length_for_response is not None:
        _UpperCAmelCase						 =      min_length_for_response
      if minimum_tokens is not None:
        _UpperCAmelCase						 =      minimum_tokens

      if "max_length" in generate_kwargs:
        _UpperCAmelCase						 =      generate_kwargs["""max_length"""]
        # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
      if clean_up_tokenization_spaces is not None:
        _UpperCAmelCase						 =      clean_up_tokenization_spaces

      if generate_kwargs:
        forward_params.update(__lowerCAmelCase					)
      return preprocess_params, forward_params, postprocess_params


    def __call__(					self	:  Tuple   ,				__lowerCAmelCase	:  Union[Conversation, List[Conversation]]   ,				__lowerCAmelCase	:  int=0   ,				**__lowerCAmelCase	:  List[str]					):
      _UpperCAmelCase						 =      super().__call__(__lowerCAmelCase   ,				num_workers=__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					) and len(__lowerCAmelCase					) == 1:
        return outputs[0]
      return outputs


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  Conversation   ,				__lowerCAmelCase	:  Optional[int]=32					):
      if not isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					):
        raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""					)
      if conversation.new_user_input is None:
        raise ValueError(
            f'''Conversation with UUID {type(conversation.uuid					)} does not contain new user input to process. '''
            """Add user inputs with the conversation's `add_user_input` method"""					)
      if hasattr(self.tokenizer   ,				"""_build_conversation_input_ids"""					):
        _UpperCAmelCase						 =      self.tokenizer._build_conversation_input_ids(__lowerCAmelCase					)
      else:
        # If the tokenizer cannot handle conversations, we default to only the old version
        _UpperCAmelCase						 =      self._legacy_parse_and_tokenize(__lowerCAmelCase					)

      if self.framework == "pt":
        _UpperCAmelCase						 =      torch.LongTensor([input_ids]					)
      elif self.framework == "tf":
        _UpperCAmelCase						 =      tf.constant([input_ids]					)
      return {"input_ids": input_ids, "conversation": conversation}


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[str]=10   ,				**__lowerCAmelCase	:  Union[str, Any]					):
      _UpperCAmelCase						 =      generate_kwargs.get("""max_length"""   ,				self.model.config.max_length					)

      _UpperCAmelCase						 =      model_inputs["""input_ids"""].shape[1]
      if max_length - minimum_tokens < n:
        logger.warning(f'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''					)
        _UpperCAmelCase						 =      max_length - minimum_tokens
        _UpperCAmelCase						 =      model_inputs["""input_ids"""][:, -trim:]
        if "attention_mask" in model_inputs:
          _UpperCAmelCase						 =      model_inputs["""attention_mask"""][:, -trim:]
      _UpperCAmelCase						 =      model_inputs.pop("""conversation"""					)
      _UpperCAmelCase						 =      max_length
      _UpperCAmelCase						 =      self.model.generate(**__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if self.model.config.is_encoder_decoder:
        _UpperCAmelCase						 =      1
      else:
        _UpperCAmelCase						 =      n
      return {"output_ids": output_ids[:, start_position:], "conversation": conversation}


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[Any]=True					):
      _UpperCAmelCase						 =      model_outputs["""output_ids"""]
      _UpperCAmelCase						 =      self.tokenizer.decode(
          output_ids[0]   ,				skip_special_tokens=__lowerCAmelCase   ,				clean_up_tokenization_spaces=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      model_outputs["""conversation"""]
      conversation.mark_processed()
      conversation.append_response(__lowerCAmelCase					)
      return conversation


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  Conversation					):
      _UpperCAmelCase						 =      self.tokenizer.eos_token_id
      _UpperCAmelCase						 =      []
      for is_user, text in conversation.iter_texts():
        if eos_token_id is not None:
          input_ids.extend(self.tokenizer.encode(__lowerCAmelCase   ,				add_special_tokens=__lowerCAmelCase					) + [eos_token_id]					)
        else:
          input_ids.extend(self.tokenizer.encode(__lowerCAmelCase   ,				add_special_tokens=__lowerCAmelCase					)					)

      if len(__lowerCAmelCase					) > self.tokenizer.model_max_length:
        _UpperCAmelCase						 =      input_ids[-self.tokenizer.model_max_length :]
      return input_ids
