

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 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      for attribute in key.split('''.'''						):
            __lowerCAmelCase					:						List[str]					=					getattr(lowercase__   ,	lowercase__						)

      if weight_type is not None:
            __lowerCAmelCase					:						Any					=					getattr(lowercase__   ,	lowercase__						).shape
      else:
            __lowerCAmelCase					:						Optional[Any]					=					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":
            __lowerCAmelCase					:						str					=					value
      elif weight_type == "weight_g":
            __lowerCAmelCase					:						str					=					value
      elif weight_type == "weight_v":
            __lowerCAmelCase					:						List[Any]					=					value
      elif weight_type == "bias":
            __lowerCAmelCase					:						Tuple					=					value
      else:
            __lowerCAmelCase					:						Dict					=					value

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







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Dict					=					[]
      __lowerCAmelCase					:						int					=					fairseq_model.state_dict()

      __lowerCAmelCase					:						Any					=					hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      __lowerCAmelCase					:						Any					=					None

      for name, value in fairseq_dict.items():
            __lowerCAmelCase					:						Union[str, Any]					=					False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	hf_model.config.feat_extract_norm == '''group'''   ,	)
                  __lowerCAmelCase					:						Union[str, Any]					=					True
            elif name.split('''.'''						)[0] == "proj":
                  __lowerCAmelCase					:						List[Any]					=					fairseq_model.proj
                  __lowerCAmelCase					:						Tuple					=					True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
                              __lowerCAmelCase					:						Optional[Any]					=					True
                              if "*" in mapped_key:
                                    __lowerCAmelCase					:						str					=					name.split(lowercase__						)[0].split('''.'''						)[-2]
                                    __lowerCAmelCase					:						Dict					=					mapped_key.replace('''*'''   ,	lowercase__						)
                              if "weight_g" in name:
                                    __lowerCAmelCase					:						Tuple					=					'''weight_g'''
                              elif "weight_v" in name:
                                    __lowerCAmelCase					:						List[Any]					=					'''weight_v'''
                              elif "bias" in name:
                                    __lowerCAmelCase					:						int					=					'''bias'''
                              elif "weight" in name:
                                    __lowerCAmelCase					:						Optional[int]					=					'''weight'''
                              else:
                                    __lowerCAmelCase					:						str					=					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 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Optional[Any]					=					full_name.split('''conv_layers.'''						)[-1]
      __lowerCAmelCase					:						Optional[int]					=					name.split('''.'''						)
      __lowerCAmelCase					:						str					=					int(items[0]						)
      __lowerCAmelCase					:						int					=					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."""
                  )
                  __lowerCAmelCase					:						str					=					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."""
                  )
                  __lowerCAmelCase					:						str					=					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."
                  )
                  __lowerCAmelCase					:						List[str]					=					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."""
                  )
                  __lowerCAmelCase					:						Optional[int]					=					value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
      else:
            unused_weights.append(lowercase__						)







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase,							__lowerCAmelCase					:						Optional[Any]					=					emb.weight.shape
      __lowerCAmelCase					:						Dict					=					nn.Linear(lowercase__   ,	lowercase__   ,	bias=lowercase__						)
      __lowerCAmelCase					:						Optional[Any]					=					emb.weight.data
      return lin_layer







def 		_lowercase (     lowercase__						):
      with open(lowercase__   ,	'''r'''   ,	encoding='''utf-8'''						) as f:
            __lowerCAmelCase					:						Tuple					=					f.readlines()
            __lowerCAmelCase					:						Tuple					=					[line.split(''' '''						)[0] for line in lines]

      __lowerCAmelCase					:						Tuple					=					len(lowercase__						)

      __lowerCAmelCase					:						List[str]					=					{
          '''<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 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	):
      __lowerCAmelCase					:						List[Any]					=					WavaVecaConfig.from_pretrained(lowercase__						)
      __lowerCAmelCase					:						Union[str, Any]					=					SpeechaTextaConfig.from_pretrained(
          lowercase__   ,	vocab_size=lowercase__   ,	decoder_layers=lowercase__   ,	do_stable_layer_norm=lowercase__						)

      __lowerCAmelCase					:						List[Any]					=					WavaVecaFeatureExtractor(
          feature_size=1   ,	sampling_rate=1_6_0_0_0   ,	padding_value=0   ,	do_normalize=lowercase__   ,	return_attention_mask=lowercase__   ,	)

      __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						Optional[int]					=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]   ,	arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
      __lowerCAmelCase					:						List[Any]					=					model[0].eval()

      # set weights for wav2vec2 encoder
      __lowerCAmelCase					:						List[Any]					=					WavaVecaModel(lowercase__						)
      __lowerCAmelCase					:						Any					=					recursively_load_weights_wavaveca(model.encoder   ,	lowercase__						)

      __lowerCAmelCase					:						int					=					SpeechaTextaForCausalLM(lowercase__						)
      __lowerCAmelCase,							__lowerCAmelCase					:						int					=					hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()   ,	strict=lowercase__						)

      # set output linear layer
      unexpected_keys.remove('''embed_out'''						)
      __lowerCAmelCase					:						Optional[Any]					=					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}"""						)

      __lowerCAmelCase					:						Optional[Any]					=					SpeechEncoderDecoderModel(encoder=lowercase__   ,	decoder=lowercase__						)
      __lowerCAmelCase					:						List[str]					=					False

      # add projection layer
      __lowerCAmelCase					:						List[Any]					=					nn.Parameter(projection_layer.weight						)
      __lowerCAmelCase					:						Any					=					nn.Parameter(projection_layer.bias						)

      __lowerCAmelCase					:						Dict					=					create_vocab_dict(lowercase__						)

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

      __lowerCAmelCase					:						str					=					SpeechaTextaTokenizer(os.path.join(lowercase__   ,	'''vocab.json'''						)						)
      tokenizer.save_pretrained(lowercase__						)

      __lowerCAmelCase					:						Any					=					hf_wavavec.config.to_dict()
      __lowerCAmelCase					:						List[Any]					=					tokenizer.pad_token_id
      __lowerCAmelCase					:						int					=					tokenizer.bos_token_id
      __lowerCAmelCase					:						str					=					tokenizer.eos_token_id
      __lowerCAmelCase					:						Optional[Any]					=					'''speech_to_text_2'''
      __lowerCAmelCase					:						Optional[int]					=					'''wav2vec2'''

      __lowerCAmelCase					:						int					=					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_0224, 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,
 )





from ....configuration_utils import PretrainedConfig
from ....utils import logging


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """trajectory_transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__(							self      ,							A_=100      ,							A_=5      ,							A_=1      ,							A_=1      ,							A_=249      ,							A_=6      ,							A_=17      ,							A_=25      ,							A_=4      ,							A_=4      ,							A_=128      ,							A_=0.1      ,							A_=0.1      ,							A_=0.1      ,							A_=0.0_006      ,							A_=512      ,							A_=0.02      ,							A_=1e-12      ,							A_=1      ,							A_=True      ,							A_=1      ,							A_=5_0256      ,							A_=5_0256      ,							**A_      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					vocab_size
       __lowerCAmelCase					:						Tuple					=					action_weight
       __lowerCAmelCase					:						Tuple					=					reward_weight
       __lowerCAmelCase					:						Union[str, Any]					=					value_weight
       __lowerCAmelCase					:						List[str]					=					max_position_embeddings
       __lowerCAmelCase					:						str					=					block_size
       __lowerCAmelCase					:						Optional[Any]					=					action_dim
       __lowerCAmelCase					:						Union[str, Any]					=					observation_dim
       __lowerCAmelCase					:						Union[str, Any]					=					transition_dim
       __lowerCAmelCase					:						Dict					=					learning_rate
       __lowerCAmelCase					:						Any					=					n_layer
       __lowerCAmelCase					:						Any					=					n_head
       __lowerCAmelCase					:						Optional[int]					=					n_embd
       __lowerCAmelCase					:						str					=					embd_pdrop
       __lowerCAmelCase					:						Dict					=					attn_pdrop
       __lowerCAmelCase					:						Optional[int]					=					resid_pdrop
       __lowerCAmelCase					:						Union[str, Any]					=					initializer_range
       __lowerCAmelCase					:						Optional[int]					=					layer_norm_eps
       __lowerCAmelCase					:						Any					=					kaiming_initializer_range
       __lowerCAmelCase					:						List[str]					=					use_cache
       super().__init__(pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_      ,							**A_					)


