





'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase_					:    Union[str, Any]      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    List[str]      	=			{
    'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}






def  _lowerCamelCase				( lowercase						:      Optional[Any]    ,							lowercase						:      int    ,							lowercase						:      Optional[int]    ,							lowercase						:      Optional[int]    ,							lowercase						:      Optional[Any]						)	->	List[Any]:
				for attribute in key.split("."						):
								_a								= getattr(lowercase    ,							lowercase						)

				if weight_type is not None:
								_a								= getattr(lowercase    ,							lowercase						).shape
				else:
								_a								= 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								= value
				elif weight_type == "weight_g":
								_a								= value
				elif weight_type == "weight_v":
								_a								= value
				elif weight_type == "bias":
								_a								= value
				else:
								_a								= value

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






def  _lowerCamelCase				( lowercase						:      Dict    ,							lowercase						:      Optional[int]    ,							lowercase						:      Dict						)	->	Dict:
				_a								= []
				_a								= fairseq_model.state_dict()

				_a								= hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								_a								= False
								if "conv_layers" in name:
												load_conv_layer(
												    lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							hf_model.config.feat_extract_norm == "group"    ,							)
												_a								= True
								else:
												for key, mapped_key in MAPPING.items():
																_a								= "sew." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

																if key in name or key.split("w2v_model."						)[-1] == name.split("."						)[0]:
																				_a								= True
																				if "*" in mapped_key:
																								_a								= name.split(lowercase						)[0].split("."						)[-2]
																								_a								= mapped_key.replace("*"    ,							lowercase						)
																				if "weight_g" in name:
																								_a								= "weight_g"
																				elif "weight_v" in name:
																								_a								= "weight_v"
																				elif "weight" in name:
																								_a								= "weight"
																				elif "bias" in name:
																								_a								= "bias"
																				else:
																								_a								= None
																				set_recursively(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
																continue
								if not is_used:
												unused_weights.append(lowercase						)

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






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






def  _lowerCamelCase				( lowercase						:      str    ,							lowercase						:      int						)	->	List[str]:
				_a								= SEWConfig()
				if is_finetuned:
								_a								= model.wav_encoder.wav_model.cfg
				else:
								_a								= model.cfg

				_a								= fs_config.conv_bias
				_a								= eval(fs_config.conv_feature_layers						)
				_a								= [x[0] for x in conv_layers]
				_a								= [x[1] for x in conv_layers]
				_a								= [x[2] for x in conv_layers]
				_a								= "gelu"
				_a								= "layer" if fs_config.extractor_mode == "layer_norm" else "group"
				_a								= 0.0
				_a								= fs_config.activation_fn.name
				_a								= fs_config.encoder_embed_dim
				_a								= 0.02
				_a								= fs_config.encoder_ffn_embed_dim
				_a								= 1E-5
				_a								= fs_config.encoder_layerdrop
				_a								= fs_config.encoder_attention_heads
				_a								= fs_config.conv_pos_groups
				_a								= fs_config.conv_pos
				_a								= len(lowercase						)
				_a								= fs_config.encoder_layers
				_a								= fs_config.squeeze_factor

				# take care of any params that are overridden by the Wav2VecCtc model
				if is_finetuned:
								_a								= model.cfg
								_a								= fs_config.final_dropout
								_a								= fs_config.layerdrop
				_a								= fs_config.activation_dropout
				_a								= fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
				_a								= fs_config.attention_dropout
				_a								= fs_config.dropout_input
				_a								= fs_config.dropout
				_a								= fs_config.mask_channel_length
				_a								= fs_config.mask_channel_prob
				_a								= fs_config.mask_length
				_a								= fs_config.mask_prob

				_a								= "Wav2Vec2FeatureExtractor"
				_a								= "Wav2Vec2CTCTokenizer"

				return config






@torch.no_grad()
def  _lowerCamelCase				( lowercase						:      Dict    ,							lowercase						:      List[str]    ,							lowercase						:      str=None    ,							lowercase						:      Union[str, Any]=None    ,							lowercase						:      Optional[int]=True						)	->	str:

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

				if config_path is not None:
								_a								= SEWConfig.from_pretrained(lowercase						)
				else:
								_a								= convert_config(model[0]    ,							lowercase						)
				_a								= model[0].eval()

				_a								= True if config.feat_extract_norm == "layer" else False
				_a								= WavaVecaFeatureExtractor(
				    feature_size=1    ,							sampling_rate=1_6000    ,							padding_value=0    ,							do_normalize=lowercase    ,							return_attention_mask=lowercase    ,							)

				if is_finetuned:
								if dict_path:
												_a								= Dictionary.load(lowercase						)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												_a								= target_dict.pad_index
												_a								= target_dict.bos_index
												_a								= target_dict.pad_index
												_a								= target_dict.bos_index
												_a								= target_dict.eos_index
												_a								= len(target_dict.symbols						)
												_a								= os.path.join(lowercase    ,							"vocab.json"						)
												if not os.path.isdir(lowercase						):
																logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowercase						)						)
																return
												os.makedirs(lowercase    ,							exist_ok=lowercase						)
												with open(lowercase    ,							"w"    ,							encoding="utf-8"						) as vocab_handle:
																json.dump(target_dict.indices    ,							lowercase						)
												_a								= WavaVecaCTCTokenizer(
												    lowercase    ,							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=lowercase    ,							)
												_a								= WavaVecaProcessor(feature_extractor=lowercase    ,							tokenizer=lowercase						)
												processor.save_pretrained(lowercase						)

								_a								= SEWForCTC(lowercase						)
				else:
								_a								= SEWModel(lowercase						)
								feature_extractor.save_pretrained(lowercase						)

				recursively_load_weights(lowercase    ,							lowercase    ,							lowercase						)

				hf_model.save_pretrained(lowercase						)


if __name__ == "__main__":
					lowerCAmelCase_					:    Tuple      	=			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(
					    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
					)
					lowerCAmelCase_					:    int      	=			parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)







'''simple docstring'''







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


lowerCAmelCase_					:    Optional[Any]      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    List[str]      	=			{
    '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 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__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     :					Optional[int]					,				__a     :					Any=5_02_65					,				__a     :					Optional[int]=10_24					,				__a     :					List[Any]=12					,				__a     :					str=16					,				__a     :					int=40_96					,				__a     :					Optional[Any]="gelu"					,				__a     :					Union[str, Any]=5_12					,				__a     :					Dict=0.1					,				__a     :					List[str]=0.0					,				__a     :					Union[str, Any]=0.0					,				__a     :					Any=2					,				__a     :					Union[str, Any]=0.02					,				__a     :					Any=0.0					,				__a     :					List[str]=True					,				__a     :					Optional[Any]=False					,				__a     :					Union[str, Any]=True					,				__a     :					Optional[Any]=True					,				__a     :					Any=1					,				__a     :					List[Any]=0					,				__a     :					Any=2					,				**__a     :					Optional[Any]					,				):
								_a								= vocab_size
								_a								= d_model
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= activation_function
								_a								= max_position_embeddings
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= init_std
								_a								= decoder_layerdrop
								_a								= use_cache
								_a								= scale_embedding
								_a								= use_learned_position_embeddings
								_a								= layernorm_embedding

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