


'''simple docstring'''



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


__lowercase	:						int       =	logging.get_logger(__name__)

__lowercase	:						Optional[Any]       =	{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "trocr"
	A_				=    ["past_key_values"]
	A_				=    {
	    "num_attention_heads": "decoder_attention_heads",
	    "hidden_size": "d_model",
	    "num_hidden_layers": "decoder_layers",
	}




	def __init__(   self						,				__a=5_0265						,				__a=1024						,				__a=12						,				__a=16						,				__a=4096						,				__a="gelu"						,				__a=512						,				__a=0.1						,				__a=0.0						,				__a=0.0						,				__a=2						,				__a=0.02						,				__a=0.0						,				__a=True						,				__a=False						,				__a=True						,				__a=True						,				__a=1						,				__a=0						,				__a=2						,				**__a						,				):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  vocab_size
						__a			:       Dict					    =  d_model
						__a			:       Optional[Any]					    =  decoder_layers
						__a			:       Tuple					    =  decoder_attention_heads
						__a			:       int					    =  decoder_ffn_dim
						__a			:       Union[str, Any]					    =  activation_function
						__a			:       List[str]					    =  max_position_embeddings
						__a			:       List[Any]					    =  dropout
						__a			:       Union[str, Any]					    =  attention_dropout
						__a			:       Optional[int]					    =  activation_dropout
						__a			:       Optional[Any]					    =  init_std
						__a			:       List[Any]					    =  decoder_layerdrop
						__a			:       int					    =  use_cache
						__a			:       List[Any]					    =  scale_embedding
						__a			:       Any					    =  use_learned_position_embeddings
						__a			:       List[str]					    =  layernorm_embedding

						super().__init__(
						    pad_token_id=__a						,				bos_token_id=__a						,				eos_token_id=__a						,				decoder_start_token_id=__a						,				**__a						,				)







'''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()
__lowercase	:						Union[str, Any]       =	logging.get_logger(__name__)

__lowercase	:						Optional[int]       =	{
    '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',
}
__lowercase	:						Optional[Any]       =	[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   Dict   ):
					for attribute in key.split('.'   ):
										__a			:       Any					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

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

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       int					    =  []
					__a			:       List[str]					    =  fairseq_model.state_dict()

					__a			:       Tuple					    =  hf_model.feature_extractor

					# if encoder has different dim to decoder -> use proj_weight
					__a			:       int					    =  None

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

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

					return proj_weight






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       List[str]					    =  full_name.split('conv_layers.'   )[-1]
					__a			:       Any					    =  name.split('.'   )
					__a			:       List[str]					    =  int(items[0]   )
					__a			:       List[Any]					    =  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."""
															)
															__a			:       List[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."""
															)
															__a			:       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."
															)
															__a			:       Tuple					    =  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."""
															)
															__a			:       List[Any]					    =  value
															logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
					else:
										unused_weights.append(_SCREAMING_SNAKE_CASE   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a    ,				__a			:       List[str]					    =  emb.weight.shape
					__a			:       str					    =  nn.Linear(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      bias=_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  emb.weight.data
					return lin_layer






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any   ):
					with open(_SCREAMING_SNAKE_CASE						,      'r'						,      encoding='utf-8'   ) as f:
										__a			:       Union[str, Any]					    =  f.readlines()
										__a			:       Tuple					    =  [line.split(' '   )[0] for line in lines]

					__a			:       int					    =  len(_SCREAMING_SNAKE_CASE   )

					__a			:       List[Any]					    =  {
					    '<s>': 0,
					    '<pad>': 1,
					    '</s>': 2,
					    '<unk>': 3,
					}

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






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      ):
					__a			:       Optional[int]					    =  WavaVecaConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
					__a			:       Any					    =  SpeechaTextaConfig.from_pretrained(
					    _SCREAMING_SNAKE_CASE						,      vocab_size=_SCREAMING_SNAKE_CASE						,      decoder_layers=_SCREAMING_SNAKE_CASE						,      do_stable_layer_norm=_SCREAMING_SNAKE_CASE   )

					__a			:       Dict					    =  WavaVecaFeatureExtractor(
					    feature_size=1						,      sampling_rate=16_000						,      padding_value=0						,      do_normalize=_SCREAMING_SNAKE_CASE						,      return_attention_mask=_SCREAMING_SNAKE_CASE						,      )

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

					# set weights for wav2vec2 encoder
					__a			:       Tuple					    =  WavaVecaModel(_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  recursively_load_weights_wavaveca(model.encoder						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Dict					    =  SpeechaTextaForCausalLM(_SCREAMING_SNAKE_CASE   )
					__a    ,				__a			:       Optional[int]					    =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,      strict=_SCREAMING_SNAKE_CASE   )

					# set output linear layer
					unexpected_keys.remove('embed_out'   )
					__a			:       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}"""   )

					__a			:       Tuple					    =  SpeechEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE						,      decoder=_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  False

					# add projection layer
					__a			:       str					    =  nn.Parameter(projection_layer.weight   )
					__a			:       Any					    =  nn.Parameter(projection_layer.bias   )

					__a			:       str					    =  create_vocab_dict(_SCREAMING_SNAKE_CASE   )

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

					__a			:       Optional[Any]					    =  SpeechaTextaTokenizer(os.path.join(_SCREAMING_SNAKE_CASE						,      'vocab.json'   )   )
					tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE   )

					__a			:       Union[str, Any]					    =  hf_wavavec.config.to_dict()
					__a			:       Tuple					    =  tokenizer.pad_token_id
					__a			:       Optional[int]					    =  tokenizer.bos_token_id
					__a			:       Union[str, Any]					    =  tokenizer.eos_token_id
					__a			:       Tuple					    =  'speech_to_text_2'
					__a			:       Tuple					    =  'wav2vec2'

					__a			:       List[str]					    =  SpeechEncoderDecoderConfig.from_dict(_SCREAMING_SNAKE_CASE   )

					hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE   )
					feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	__lowercase	:						Dict       =	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_02_24, type=int, help='Vocab size of decoder')
	parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

	__lowercase	:						Tuple       =	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,
	)



