



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
_lowercase: List[str]      			=		logging.get_logger(__name__)

_lowercase: Optional[Any]      			=		{
    "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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
_lowercase: Tuple      			=		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]






def     a(      A       :    Optional[Any]       )		->       Optional[int]:
					"""simple docstring"""





					a					     =   {}
					with open(A ,     "r"       ) as file:
										for line_number, line in enumerate(A       ):
															a					     =   line.strip()
															if line:
																				a					     =   line.split()
																				a					     =   line_number
																				a					     =   words[0]
																				a					     =   value
					return result






def     a(      A       :    List[str] ,     A       :    Dict ,     A       :    Tuple ,     A       :    Any ,     A       :    List[str]       )		->       Tuple:
					"""simple docstring"""





					for attribute in key.split("."       ):
										a					     =   getattr(A ,     A       )

					a					     =   None
					for param_key in PARAM_MAPPING.keys():
										if full_name.endswith(A       ):
															a					     =   PARAM_MAPPING[full_name.split("."       )[-1]]
															a					     =   "param"

					if weight_type is not None and weight_type != "param":
										a					     =   getattr(A ,     A       ).shape
					elif weight_type is not None and weight_type == "param":
										a					     =   hf_pointer
										for attribute in hf_param_name.split("."       ):
															a					     =   getattr(A ,     A       )
										a					     =   shape_pointer.shape

										# let's reduce dimension
										a					     =   value[0]
					else:
										a					     =   hf_pointer.shape

					if hf_shape != value.shape:
										raise ValueError(
										    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
					elif weight_type == "param":
										for attribute in hf_param_name.split("."       ):
															a					     =   getattr(A ,     A       )
										a					     =   value
					else:
										a					     =   value

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






def     a(      A       :    str ,     A       :    int ,     A       :    Dict ,     A       :    List[Any] ,     A       :    str       )		->       Optional[Any]:
					"""simple docstring"""





					a					     =   None
					for param_key in PARAM_MAPPING.keys():
										if full_name.endswith(A       ):
															a					     =   PARAM_MAPPING[full_name.split("."       )[-1]]
															a					     =   "param"

					if weight_type is not None and weight_type != "param":
										a					     =   ".".join([key, weight_type]       )
					elif weight_type is not None and weight_type == "param":
										a					     =   ".".join([key, hf_param_name]       )
					else:
										a					     =   key

					a					     =   value if "lm_head" in full_key else value[0]


_lowercase: List[str]      			=		{
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}






def     a(      A       :    Tuple ,     A       :    Optional[Any] ,     A       :    Optional[int]=None ,     A       :    int=None       )		->       Tuple:
					"""simple docstring"""





					a					     =   False
					for key, mapped_key in MAPPING.items():
										a					     =   "wav2vec2." + 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]:
															a					     =   True
															if "*" in mapped_key:
																				a					     =   name.split(A       )[0].split("."       )[-2]
																				a					     =   mapped_key.replace("*" ,     A       )
															if "weight_g" in name:
																				a					     =   "weight_g"
															elif "weight_v" in name:
																				a					     =   "weight_v"
															elif "bias" in name:
																				a					     =   "bias"
															elif "weight" in name:
																				# TODO: don't match quantizer.weight_proj
																				a					     =   "weight"
															else:
																				a					     =   None
															if hf_dict is not None:
																				rename_dict(A ,     A ,     A ,     A ,     A       )
															else:
																				set_recursively(A ,     A ,     A ,     A ,     A       )
															return is_used
					return is_used






def     a(      A       :    Optional[int] ,     A       :    Any ,     A       :    Union[str, Any]       )		->       Tuple:
					"""simple docstring"""





					a					     =   []
					a					     =   fairseq_model.state_dict()

					a					     =   hf_model.wavaveca.feature_extractor

					for name, value in fairseq_dict.items():
										a					     =   False
										if "conv_layers" in name:
															load_conv_layer(
															    A ,     A ,     A ,     A ,     hf_model.config.feat_extract_norm == "group" ,     )
															a					     =   True
										else:
															a					     =   load_wavaveca_layer(A ,     A ,     A       )
										if not is_used:
															unused_weights.append(A       )

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






def     a(      A       :    Dict ,     A       :    Union[str, Any] ,     A       :    List[Any] ,     A       :    str ,     A       :    List[Any]       )		->       Tuple:
					"""simple docstring"""





					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:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																				raise ValueError(
																				    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:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																				raise ValueError(
																				    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:
															if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																				raise ValueError(
																				    f'''{full_name} has size {value.shape}, but'''
																				    f''' {feature_extractor.conv_layers[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:
															if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																				raise ValueError(
																				    f'''{full_name} has size {value.shape}, but'''
																				    f''' {feature_extractor.conv_layers[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(A       )






@torch.no_grad()
def     a(      A       :    Any ,     A       :    Union[str, Any] ,     A       :    str=None ,     A       :    Optional[int]=None ,     A       :    Union[str, Any]=True ,     A       :    List[str]=False       )		->       str:
					"""simple docstring"""





					if config_path is not None:
										a					     =   WavaVecaConfig.from_pretrained(A       )
					else:
										a					     =   WavaVecaConfig()

					if is_seq_class:
										a					     =   read_txt_into_dict(A       )
										a					     =   idalabel
										a					     =   WavaVecaForSequenceClassification(A       )
										a					     =   WavaVecaFeatureExtractor(
										    feature_size=1 ,     sampling_rate=1_6000 ,     padding_value=0 ,     do_normalize=A ,     return_attention_mask=A ,     )
										feature_extractor.save_pretrained(A       )

					elif is_finetuned:
										if dict_path:
															a					     =   Dictionary.load(A       )

															# 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.eos_index
															a					     =   len(target_dict.symbols       )
															a					     =   os.path.join(A ,     "vocab.json"       )
															if not os.path.isdir(A       ):
																				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(A       )       )
																				return
															os.makedirs(A ,     exist_ok=A       )
															a					     =   target_dict.indices

															# fairseq has the <pad> and <s> switched
															a					     =   0
															a					     =   1
															with open(A ,     "w" ,     encoding="utf-8"       ) as vocab_handle:
																				json.dump(A ,     A       )
															a					     =   WavaVecaCTCTokenizer(
															    A ,     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=A ,     )
															a					     =   True if config.feat_extract_norm == "layer" else False
															a					     =   WavaVecaFeatureExtractor(
															    feature_size=1 ,     sampling_rate=1_6000 ,     padding_value=0 ,     do_normalize=A ,     return_attention_mask=A ,     )
															a					     =   WavaVecaProcessor(feature_extractor=A ,     tokenizer=A       )
															processor.save_pretrained(A       )

										a					     =   WavaVecaForCTC(A       )
					else:
										a					     =   WavaVecaForPreTraining(A       )

					if is_finetuned or is_seq_class:
										a     ,				a     ,				a					     =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path] ,     arg_overrides={"data": "/".join(dict_path.split("/"       )[:-1]       )}       )
					else:
										a					     =   argparse.Namespace(task="audio_pretraining"       )
										a					     =   fairseq.tasks.setup_task(A       )

										a     ,				a     ,				a					     =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,     task=A       )

					a					     =   model[0].eval()

					recursively_load_weights(A ,     A ,     not is_finetuned       )

					hf_wavavec.save_pretrained(A       )


if __name__ == "__main__":
				_lowercase: str      			=		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"
				)
				parser.add_argument(
				    "--is_seq_class",
				    action="store_true",
				    help="Whether the model to convert is a fine-tuned sequence classification model or not",
				)
				_lowercase: List[Any]      			=		parser.parse_args()

				_lowercase: str      			=		not args.not_finetuned and not args.is_seq_class
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.dict_path,
				    is_finetuned,
				    args.is_seq_class,
				)





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


_lowercase: str      			=		logging.get_logger(__name__)

_lowercase: 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 	_lowercase							(   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		,       lowerCamelCase_=50265		,       lowerCamelCase_=1024		,       lowerCamelCase_=12		,       lowerCamelCase_=16		,       lowerCamelCase_=4096		,       lowerCamelCase_="gelu"		,       lowerCamelCase_=512		,       lowerCamelCase_=0.1		,       lowerCamelCase_=0.0		,       lowerCamelCase_=0.0		,       lowerCamelCase_=2		,       lowerCamelCase_=0.02		,       lowerCamelCase_=0.0		,       lowerCamelCase_=True		,       lowerCamelCase_=False		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=1		,       lowerCamelCase_=0		,       lowerCamelCase_=2		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						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=lowerCamelCase_		,       bos_token_id=lowerCamelCase_		,       eos_token_id=lowerCamelCase_		,       decoder_start_token_id=lowerCamelCase_		,       **lowerCamelCase_		,       )
