import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

_lowerCAmelCase       =		{
    "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",
}
_lowerCAmelCase       =		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						):
					for attribute in key.split('''.'''						):
										_UpperCamelCase			     =     getattr(__snake_case  ,     __snake_case						)

					if weight_type is not None:
										_UpperCamelCase			     =     getattr(__snake_case  ,     __snake_case						).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 					_snake_case	(  __snake_case  ,     __snake_case						):
					_UpperCamelCase			     =     []
					_UpperCamelCase			     =     fairseq_model.state_dict()

					_UpperCamelCase			     =     hf_model.feature_extractor
					_UpperCamelCase			     =     hf_model.adapter

					for name, value in fairseq_dict.items():
										_UpperCamelCase			     =     False
										if "conv_layers" in name:
															load_conv_layer(
															    __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     hf_model.config.feat_extract_norm == '''group'''  ,     )
															_UpperCamelCase			     =     True
										elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']						):
															load_adapter(__snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						)
															_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(__snake_case						)[0].split('''.'''						)[-2]
																														_UpperCamelCase			     =     mapped_key.replace('''*'''  ,     __snake_case						)
																									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(__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 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						):
					_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(__snake_case						)
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						):
					_UpperCamelCase			     =     full_name.split('''adaptor.'''						)[-1]
					_UpperCamelCase			     =     name.split('''.'''						)

					if items[1].isdigit():
										_UpperCamelCase			     =     int(items[1]						)
					else:
										_UpperCamelCase			     =     None

					if "adaptor" not in full_name:
										if "proj_ln" in full_name:
															# has to be layer norm
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.bias.data.shape
																				), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
																				_UpperCamelCase			     =     value
																				logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""						)
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.weight.data.shape
																				), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
																				_UpperCamelCase			     =     value
										else:
															# has to be projection layer
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj.bias.data.shape
																				), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
																				_UpperCamelCase			     =     value
																				logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""						)
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj.weight.data.shape
																				), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
																				_UpperCamelCase			     =     value
																				logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""						)
					elif isinstance(__snake_case  ,     __snake_case						):
										if "bias" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.bias.data.shape
															), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
															_UpperCamelCase			     =     value
															logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""						)
										elif "weight" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.weight.data.shape
															), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
															_UpperCamelCase			     =     value
															logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""						)
					else:
										unused_weights.append(__snake_case						)
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase     ,    _UpperCamelCase			     =     emb.weight.shape
					_UpperCamelCase			     =     nn.Linear(__snake_case  ,     __snake_case  ,     bias=__snake_case						)
					_UpperCamelCase			     =     emb.weight.data
					return lin_layer
@torch.no_grad()
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     ):
					_UpperCamelCase			     =     WavaVecaConfig.from_pretrained(
					    __snake_case  ,     add_adapter=__snake_case  ,     adapter_stride=__snake_case  ,     adapter_kernel_size=__snake_case  ,     use_auth_token=__snake_case  ,     output_hidden_size=__snake_case  ,     )
					_UpperCamelCase			     =     MBartConfig.from_pretrained(__snake_case						)

					# load model
					_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path]  ,     arg_overrides={
					        '''config_yaml''': config_yaml_path,
					        '''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						),
					        '''w2v_path''': checkpoint_path,
					        '''load_pretrained_decoder_from''': None,
					    }  ,     )
					_UpperCamelCase			     =     model[0].eval()

					# load feature extractor
					_UpperCamelCase			     =     WavaVecaFeatureExtractor.from_pretrained(__snake_case  ,     use_auth_token=__snake_case						)

					# set weights for wav2vec2 encoder
					_UpperCamelCase			     =     WavaVecaModel(__snake_case						)

					recursively_load_weights_wavaveca(model.encoder  ,     __snake_case						)

					# load decoder weights
					_UpperCamelCase			     =     MBartForCausalLM(__snake_case						)
					_UpperCamelCase     ,    _UpperCamelCase			     =     hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()  ,     strict=__snake_case						)
					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=__snake_case  ,     decoder=__snake_case						)
					_UpperCamelCase			     =     False

					_UpperCamelCase			     =     MBartaaTokenizer(__snake_case						)
					tokenizer.save_pretrained(__snake_case						)

					_UpperCamelCase			     =     hf_wavavec.config.to_dict()
					_UpperCamelCase			     =     tokenizer.pad_token_id
					_UpperCamelCase			     =     tokenizer.bos_token_id
					_UpperCamelCase			     =     tokenizer.eos_token_id
					_UpperCamelCase			     =     '''mbart50'''
					_UpperCamelCase			     =     '''wav2vec2'''

					_UpperCamelCase			     =     tokenizer.eos_token_id
					_UpperCamelCase			     =     250004
					_UpperCamelCase			     =     tokenizer.eos_token_id

					_UpperCamelCase			     =     SpeechEncoderDecoderConfig.from_dict(__snake_case						)

					hf_wavavec.save_pretrained(__snake_case						)
					feature_extractor.save_pretrained(__snake_case						)


if __name__ == "__main__":
					_lowerCAmelCase       =		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("--config_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
					parser.add_argument(
					    "--encoder_config_path",
					    default="facebook/wav2vec2-xls-r-1b",
					    type=str,
					    help="Path to hf encoder wav2vec2 checkpoint config",
					)
					parser.add_argument(
					    "--decoder_config_path",
					    default="facebook/mbart-large-50-one-to-many-mmt",
					    type=str,
					    help="Path to hf decoder checkpoint config",
					)
					parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
					parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
					parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
					parser.add_argument("--encoder_output_dim", default=1_024, type=int, help="encoder output dim")
					parser.add_argument("--start_token_id", default=250_004, type=int, help="`decoder_start_token_id` of model config")

					_lowerCAmelCase       =		parser.parse_args()
					convert_wavaveca_checkpoint(
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.dict_path,
					    args.config_yaml_path,
					    encoder_config_path=args.encoder_config_path,
					    decoder_config_path=args.decoder_config_path,
					    add_adapter=args.add_adapter,
					    adapter_kernel_size=args.adapter_kernel_size,
					    adapter_stride=args.adapter_stride,
					    decoder_start_token_id=args.start_token_id,
					    encoder_output_dim=args.encoder_output_dim,
					)

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


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "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     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "trocr"
							UpperCAmelCase				   =     ["past_key_values"]
							UpperCAmelCase				   =     {
							    "num_attention_heads": "decoder_attention_heads",
							    "hidden_size": "d_model",
							    "num_hidden_layers": "decoder_layers",
							}







							def __init__(   self      :     List[str] ,			_A      :     Optional[Any]=5_0265 ,			_A      :     Optional[Any]=1024 ,			_A      :     Optional[Any]=12 ,			_A      :     Any=16 ,			_A      :     Any=4096 ,			_A      :     Optional[Any]="gelu" ,			_A      :     Union[str, Any]=512 ,			_A      :     Dict=0.1 ,			_A      :     List[str]=0.0 ,			_A      :     Optional[Any]=0.0 ,			_A      :     Union[str, Any]=2 ,			_A      :     Any=0.02 ,			_A      :     List[str]=0.0 ,			_A      :     List[str]=True ,			_A      :     str=False ,			_A      :     List[str]=True ,			_A      :     Optional[Any]=True ,			_A      :     Optional[int]=1 ,			_A      :     int=0 ,			_A      :     Any=2 ,			**_A      :     Optional[int] ,			):
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     d_model
												_UpperCamelCase			     =     decoder_layers
												_UpperCamelCase			     =     decoder_attention_heads
												_UpperCamelCase			     =     decoder_ffn_dim
												_UpperCamelCase			     =     activation_function
												_UpperCamelCase			     =     max_position_embeddings
												_UpperCamelCase			     =     dropout
												_UpperCamelCase			     =     attention_dropout
												_UpperCamelCase			     =     activation_dropout
												_UpperCamelCase			     =     init_std
												_UpperCamelCase			     =     decoder_layerdrop
												_UpperCamelCase			     =     use_cache
												_UpperCamelCase			     =     scale_embedding
												_UpperCamelCase			     =     use_learned_position_embeddings
												_UpperCamelCase			     =     layernorm_embedding

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