import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
_lowerCAmelCase       =		[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						):
					for attribute in key.split('''.'''						):
										if is_finetuned:
															if attribute in ["quantizer", "project_q", "project_hid"]:
																				# those layers are only relevant for pretraining and should be dropped
																				return

															if attribute == "ctc_proj":
																				# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																				_UpperCamelCase			     =     '''lm_head'''

										_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  ,     __snake_case						):
					_UpperCamelCase			     =     []
					_UpperCamelCase			     =     fairseq_model.state_dict()

					_UpperCamelCase			     =     hf_model.unispeech.feature_extractor

					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
										else:
															for key, mapped_key in MAPPING.items():
																				_UpperCamelCase			     =     '''unispeech.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																				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:
																														# TODO: don't match quantizer.weight_proj
																														_UpperCamelCase			     =     '''weight'''
																									else:
																														_UpperCamelCase			     =     None
																									set_recursively(__snake_case  ,     __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						)
@torch.no_grad()
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case=None  ,     __snake_case=None  ,     __snake_case=True						):
					if config_path is not None:
										_UpperCamelCase			     =     UniSpeechConfig.from_pretrained(__snake_case						)
					else:
										_UpperCamelCase			     =     UniSpeechConfig()

					if is_finetuned:
										if dict_path:
															_UpperCamelCase			     =     Dictionary.load_from_json(__snake_case						)

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															_UpperCamelCase			     =     target_dict.pad_index
															_UpperCamelCase			     =     target_dict.bos_index
															_UpperCamelCase			     =     target_dict.eos_index
															_UpperCamelCase			     =     len(target_dict.symbols						)
															_UpperCamelCase			     =     os.path.join(__snake_case  ,     '''vocab.json'''						)
															if not os.path.isdir(__snake_case						):
																				logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__snake_case						)						)
																				return
															os.makedirs(__snake_case  ,     exist_ok=__snake_case						)
															_UpperCamelCase			     =     target_dict.indices

															# fairseq has the <pad> and <s> switched
															_UpperCamelCase			     =     42
															_UpperCamelCase			     =     43
															with open(__snake_case  ,     '''w'''  ,     encoding='''utf-8'''						) as vocab_handle:
																				json.dump(__snake_case  ,     __snake_case						)
															_UpperCamelCase			     =     WavaVecaPhonemeCTCTokenizer(
															    __snake_case  ,     unk_token=target_dict.unk_word  ,     pad_token=target_dict.pad_word  ,     bos_token=target_dict.bos_word  ,     eos_token=target_dict.eos_word  ,     word_delimiter_token='''|'''  ,     do_lower_case=__snake_case  ,     )
															_UpperCamelCase			     =     True if config.feat_extract_norm == '''layer''' else False
															_UpperCamelCase			     =     WavaVecaFeatureExtractor(
															    feature_size=1  ,     sampling_rate=16000  ,     padding_value=0  ,     do_normalize=__snake_case  ,     return_attention_mask=__snake_case  ,     )
															_UpperCamelCase			     =     WavaVecaProcessor(feature_extractor=__snake_case  ,     tokenizer=__snake_case						)
															processor.save_pretrained(__snake_case						)

										_UpperCamelCase			     =     UniSpeechForCTC(__snake_case						)
					else:
										_UpperCamelCase			     =     UniSpeechForPreTraining(__snake_case						)

					if is_finetuned:
										_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path]  ,     arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						), '''w2v_path''': checkpoint_path}						)
					else:
										_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

					_UpperCamelCase			     =     model[0].eval()

					recursively_load_weights(__snake_case  ,     __snake_case  ,     __snake_case						)

					hf_unispeech.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_path", default=None, type=str, help="Path to hf config.json of model to convert")
					parser.add_argument(
					    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
					)
					_lowerCAmelCase       =		parser.parse_args()
					convert_unispeech_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)

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 ,			)
