





"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text






def 					A__				(			UpperCamelCase       ):
							A		=  []
							for line in lines:
														A		=  re.sub(r"#.*"					,  ""					,  UpperCamelCase       )  # remove comments
														if line:
																					filtered_lines.append(UpperCamelCase       )
							A		=  "\n".join(UpperCamelCase       )

							# Make a hash from all this code
							A		=  full_str.encode("utf-8"       )
							return shaaaa(UpperCamelCase       ).hexdigest()


# get importable module names and hash for caching
_snake_case     :		List[str]       						= {
    'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_snake_case     :		int       						= {
    '.csv': ('csv', {}),
    '.tsv': ('csv', {'sep': '\t'}),
    '.json': ('json', {}),
    '.jsonl': ('json', {}),
    '.parquet': ('parquet', {}),
    '.arrow': ('arrow', {}),
    '.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_snake_case     :		Any       						= {'imagefolder', 'audiofolder'}

# Used to filter data files based on extensions given a module name
_snake_case     :		Dict[str, List[str]]       						= {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
		_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')









"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def 					A__				(			UpperCamelCase       ):
							A		=  [
							    "encoder.version",
							    "decoder.version",
							    "model.encoder.version",
							    "model.decoder.version",
							    "decoder.output_projection.weight",
							    "_float_tensor",
							    "encoder.embed_positions._float_tensor",
							    "decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase					,  UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A		=  list(s_dict.keys()       )
							for key in keys:
														if "transformer_layers" in key:
																					A		=  s_dict.pop(UpperCamelCase       )
														elif "subsample" in key:
																					A		=  s_dict.pop(UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A,	A		=  emb.weight.shape
							A		=  nn.Linear(UpperCamelCase					,  UpperCamelCase					,  bias=UpperCamelCase       )
							A		=  emb.weight.data
							return lin_layer






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  torch.load(UpperCamelCase					,  map_location="cpu"       )
							A		=  mam_aaa["args"]
							A		=  mam_aaa["model"]
							A		=  state_dict["decoder.output_projection.weight"]

							remove_ignore_keys_(UpperCamelCase       )
							rename_keys(UpperCamelCase       )

							A		=  state_dict["decoder.embed_tokens.weight"].shape[0]

							A		=  args.share_decoder_input_output_embed

							A		=  [int(UpperCamelCase       ) for i in args.conv_kernel_sizes.split(","       )]
							A		=  SpeechaTextConfig(
							    vocab_size=UpperCamelCase					,  max_source_positions=args.max_source_positions					,  max_target_positions=args.max_target_positions					,  encoder_layers=args.encoder_layers					,  decoder_layers=args.decoder_layers					,  encoder_attention_heads=args.encoder_attention_heads					,  decoder_attention_heads=args.decoder_attention_heads					,  encoder_ffn_dim=args.encoder_ffn_embed_dim					,  decoder_ffn_dim=args.decoder_ffn_embed_dim					,  d_model=args.encoder_embed_dim					,  dropout=args.dropout					,  attention_dropout=args.attention_dropout					,  activation_dropout=args.activation_dropout					,  activation_function="relu"					,  num_conv_layers=len(UpperCamelCase       )					,  conv_channels=args.conv_channels					,  conv_kernel_sizes=UpperCamelCase					,  input_feat_per_channel=args.input_feat_per_channel					,  input_channels=args.input_channels					,  tie_word_embeddings=UpperCamelCase					,  num_beams=5					,  max_length=200					,  use_cache=UpperCamelCase					,  decoder_start_token_id=2					,  early_stopping=UpperCamelCase					,  )

							A		=  SpeechaTextForConditionalGeneration(UpperCamelCase       )
							A,	A		=  model.model.load_state_dict(UpperCamelCase					,  strict=UpperCamelCase       )
							if len(UpperCamelCase       ) > 0 and not set(UpperCamelCase       ) <= {
							    "encoder.embed_positions.weights",
							    "decoder.embed_positions.weights",
							}:
														raise ValueError(
														    "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
														    F" but all the following weights are missing {missing}"       )

							if tie_embeds:
														A		=  make_linear_from_emb(model.model.decoder.embed_tokens       )
							else:
														A		=  lm_head_weights

							model.save_pretrained(UpperCamelCase       )


if __name__ == "__main__":
		_snake_case     :		Optional[int]       						= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		_snake_case     :		str       						= parser.parse_args()
		convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)


