





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()
UpperCamelCase_									=logging.get_logger(__name__)

UpperCamelCase_									={
    """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 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						for attribute in key.split('''.'''       ):
												_UpperCamelCase      : Dict         =   getattr(_lowercase	,						_lowercase       )

						if weight_type is not None:
												_UpperCamelCase      : str         =   getattr(_lowercase	,						_lowercase       ).shape
						else:
												_UpperCamelCase      : List[str]         =   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      : Dict         =   value
						elif weight_type == "weight_g":
												_UpperCamelCase      : int         =   value
						elif weight_type == "weight_v":
												_UpperCamelCase      : List[str]         =   value
						elif weight_type == "bias":
												_UpperCamelCase      : List[str]         =   value
						else:
												_UpperCamelCase      : Union[str, Any]         =   value

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





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : str         =   []
						_UpperCamelCase      : List[str]         =   fairseq_model.state_dict()

						_UpperCamelCase      : Optional[int]         =   hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCamelCase      : Dict         =   False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						hf_model.config.feat_extract_norm == '''group'''	,						)
																		_UpperCamelCase      : Optional[Any]         =   True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCamelCase      : str         =   '''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]:
																														_UpperCamelCase      : Tuple         =   True
																														if "*" in mapped_key:
																																				_UpperCamelCase      : str         =   name.split(_lowercase       )[0].split('''.'''       )[-2]
																																				_UpperCamelCase      : Optional[Any]         =   mapped_key.replace('''*'''	,						_lowercase       )
																														if "weight_g" in name:
																																				_UpperCamelCase      : Tuple         =   '''weight_g'''
																														elif "weight_v" in name:
																																				_UpperCamelCase      : List[str]         =   '''weight_v'''
																														elif "weight" in name:
																																				_UpperCamelCase      : int         =   '''weight'''
																														elif "bias" in name:
																																				_UpperCamelCase      : List[Any]         =   '''bias'''
																														else:
																																				_UpperCamelCase      : Optional[int]         =   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 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : int         =   full_name.split('''conv_layers.'''       )[-1]
						_UpperCamelCase      : Optional[int]         =   name.split('''.'''       )
						_UpperCamelCase      : str         =   int(items[0]       )
						_UpperCamelCase      : Any         =   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      : List[Any]         =   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      : Optional[int]         =   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      : Optional[Any]         =   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      : List[str]         =   value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
						else:
												unused_weights.append(_lowercase       )





def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Optional[int]         =   SEWConfig()
						if is_finetuned:
												_UpperCamelCase      : Optional[int]         =   model.wav_encoder.wav_model.cfg
						else:
												_UpperCamelCase      : Union[str, Any]         =   model.cfg

						_UpperCamelCase      : int         =   fs_config.conv_bias
						_UpperCamelCase      : int         =   eval(fs_config.conv_feature_layers       )
						_UpperCamelCase      : Any         =   [x[0] for x in conv_layers]
						_UpperCamelCase      : List[Any]         =   [x[1] for x in conv_layers]
						_UpperCamelCase      : Union[str, Any]         =   [x[2] for x in conv_layers]
						_UpperCamelCase      : List[Any]         =   '''gelu'''
						_UpperCamelCase      : Optional[Any]         =   '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
						_UpperCamelCase      : List[str]         =   0.0
						_UpperCamelCase      : Optional[Any]         =   fs_config.activation_fn.name
						_UpperCamelCase      : Union[str, Any]         =   fs_config.encoder_embed_dim
						_UpperCamelCase      : Dict         =   0.02
						_UpperCamelCase      : Optional[Any]         =   fs_config.encoder_ffn_embed_dim
						_UpperCamelCase      : Any         =   1E-5
						_UpperCamelCase      : List[Any]         =   fs_config.encoder_layerdrop
						_UpperCamelCase      : Optional[Any]         =   fs_config.encoder_attention_heads
						_UpperCamelCase      : List[str]         =   fs_config.conv_pos_groups
						_UpperCamelCase      : Union[str, Any]         =   fs_config.conv_pos
						_UpperCamelCase      : Union[str, Any]         =   len(_lowercase       )
						_UpperCamelCase      : Any         =   fs_config.encoder_layers
						_UpperCamelCase      : Tuple         =   fs_config.squeeze_factor

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												_UpperCamelCase      : str         =   model.cfg
												_UpperCamelCase      : Optional[int]         =   fs_config.final_dropout
												_UpperCamelCase      : Any         =   fs_config.layerdrop
						_UpperCamelCase      : List[str]         =   fs_config.activation_dropout
						_UpperCamelCase      : Optional[Any]         =   fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						_UpperCamelCase      : Union[str, Any]         =   fs_config.attention_dropout
						_UpperCamelCase      : Any         =   fs_config.dropout_input
						_UpperCamelCase      : Optional[int]         =   fs_config.dropout
						_UpperCamelCase      : Tuple         =   fs_config.mask_channel_length
						_UpperCamelCase      : Union[str, Any]         =   fs_config.mask_channel_prob
						_UpperCamelCase      : List[str]         =   fs_config.mask_length
						_UpperCamelCase      : Optional[Any]         =   fs_config.mask_prob

						_UpperCamelCase      : Dict         =   '''Wav2Vec2FeatureExtractor'''
						_UpperCamelCase      : Any         =   '''Wav2Vec2CTCTokenizer'''

						return config





@torch.no_grad()
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase=None	,						_lowercase=None	,						_lowercase=True       ):

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

						if config_path is not None:
												_UpperCamelCase      : Union[str, Any]         =   SEWConfig.from_pretrained(_lowercase       )
						else:
												_UpperCamelCase      : int         =   convert_config(model[0]	,						_lowercase       )
						_UpperCamelCase      : int         =   model[0].eval()

						_UpperCamelCase      : Any         =   True if config.feat_extract_norm == '''layer''' else False
						_UpperCamelCase      : str         =   WavaVecaFeatureExtractor(
						    feature_size=1	,						sampling_rate=1_6000	,						padding_value=0	,						do_normalize=_lowercase	,						return_attention_mask=_lowercase	,						)

						if is_finetuned:
												if dict_path:
																		_UpperCamelCase      : Optional[int]         =   Dictionary.load(_lowercase       )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		_UpperCamelCase      : Dict         =   target_dict.pad_index
																		_UpperCamelCase      : Optional[int]         =   target_dict.bos_index
																		_UpperCamelCase      : int         =   target_dict.pad_index
																		_UpperCamelCase      : Union[str, Any]         =   target_dict.bos_index
																		_UpperCamelCase      : Tuple         =   target_dict.eos_index
																		_UpperCamelCase      : Union[str, Any]         =   len(target_dict.symbols       )
																		_UpperCamelCase      : Dict         =   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       )
																		_UpperCamelCase      : Optional[Any]         =   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	,						)
																		_UpperCamelCase      : Union[str, Any]         =   WavaVecaProcessor(feature_extractor=_lowercase	,						tokenizer=_lowercase       )
																		processor.save_pretrained(_lowercase       )

												_UpperCamelCase      : Union[str, Any]         =   SEWForCTC(_lowercase       )
						else:
												_UpperCamelCase      : Any         =   SEWModel(_lowercase       )
												feature_extractor.save_pretrained(_lowercase       )

						recursively_load_weights(_lowercase	,						_lowercase	,						_lowercase       )

						hf_model.save_pretrained(_lowercase       )


if __name__ == "__main__":
						UpperCamelCase_									=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"""
						)
						UpperCamelCase_									=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"""




import argparse
import os
import re

import packaging.version


UpperCamelCase_									="""examples/"""
UpperCamelCase_									={
    """examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
    """init""": (re.compile(r"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
    """setup""": (re.compile(r"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), r"""\1version=\"VERSION\","""),
    """doc""": (re.compile(r"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
UpperCamelCase_									={
    """init""": """src/transformers/__init__.py""",
    """setup""": """setup.py""",
}
UpperCamelCase_									="""README.md"""





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						with open(_lowercase	,						'''r'''	,						encoding='''utf-8'''	,						newline='''\n'''       ) as f:
												_UpperCamelCase      : Tuple         =   f.read()
						_UpperCamelCase       ,							_UpperCamelCase      : List[Any]         =   REPLACE_PATTERNS[pattern]
						_UpperCamelCase      : Optional[Any]         =   replace.replace('''VERSION'''	,						_lowercase       )
						_UpperCamelCase      : List[Any]         =   re_pattern.sub(_lowercase	,						_lowercase       )
						with open(_lowercase	,						'''w'''	,						encoding='''utf-8'''	,						newline='''\n'''       ) as f:
												f.write(_lowercase       )





def 	a_    (							_lowercase       ):
						for folder, directories, fnames in os.walk(_lowercase       ):
												# Removing some of the folders with non-actively maintained examples from the walk
												if "research_projects" in directories:
																		directories.remove('''research_projects'''       )
												if "legacy" in directories:
																		directories.remove('''legacy'''       )
												for fname in fnames:
																		if fname.endswith('''.py'''       ):
																								update_version_in_file(os.path.join(_lowercase	,						_lowercase       )	,						_lowercase	,						pattern='''examples'''       )





def 	a_    (							_lowercase	,						_lowercase=False       ):
						for pattern, fname in REPLACE_FILES.items():
												update_version_in_file(_lowercase	,						_lowercase	,						_lowercase       )
						if not patch:
												update_version_in_examples(_lowercase       )





def 	a_    (							):
						_UpperCamelCase      : Any         =   '''🤗 Transformers currently provides the following architectures'''
						_UpperCamelCase      : List[str]         =   '''1. Want to contribute a new model?'''
						with open(_lowercase	,						'''r'''	,						encoding='''utf-8'''	,						newline='''\n'''       ) as f:
												_UpperCamelCase      : List[Any]         =   f.readlines()

						# Find the start of the list.
						_UpperCamelCase      : Optional[Any]         =   0
						while not lines[start_index].startswith(_start_prompt       ):
												start_index += 1
						start_index += 1

						_UpperCamelCase      : Any         =   start_index
						# Update the lines in the model list.
						while not lines[index].startswith(_end_prompt       ):
												if lines[index].startswith('''1.'''       ):
																		_UpperCamelCase      : Tuple         =   lines[index].replace(
																		    '''https://huggingface.co/docs/transformers/main/model_doc'''	,						'''https://huggingface.co/docs/transformers/model_doc'''	,						)
												index += 1

						with open(_lowercase	,						'''w'''	,						encoding='''utf-8'''	,						newline='''\n'''       ) as f:
												f.writelines(_lowercase       )





def 	a_    (							):
						with open(REPLACE_FILES['''init''']	,						'''r'''       ) as f:
												_UpperCamelCase      : List[Any]         =   f.read()
						_UpperCamelCase      : List[Any]         =   REPLACE_PATTERNS['''init'''][0].search(_lowercase       ).groups()[0]
						return packaging.version.parse(_lowercase       )





def 	a_    (							_lowercase=False       ):
						_UpperCamelCase      : Union[str, Any]         =   get_version()
						if patch and default_version.is_devrelease:
												raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''       )
						if default_version.is_devrelease:
												_UpperCamelCase      : List[str]         =   default_version.base_version
						elif patch:
												_UpperCamelCase      : Union[str, Any]         =   F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
						else:
												_UpperCamelCase      : str         =   F"""{default_version.major}.{default_version.minor + 1}.0"""

						# Now let's ask nicely if that's the right one.
						_UpperCamelCase      : Optional[int]         =   input(F"""Which version are you releasing? [{default_version}]"""       )
						if len(_lowercase       ) == 0:
												_UpperCamelCase      : str         =   default_version

						print(F"""Updating version to {version}."""       )
						global_version_update(_lowercase	,						patch=_lowercase       )
						if not patch:
												print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''       )
												clean_main_ref_in_model_list()





def 	a_    (							):
						_UpperCamelCase      : Any         =   get_version()
						_UpperCamelCase      : Dict         =   F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
						_UpperCamelCase      : Union[str, Any]         =   current_version.base_version

						# Check with the user we got that right.
						_UpperCamelCase      : int         =   input(F"""Which version are we developing now? [{dev_version}]"""       )
						if len(_lowercase       ) == 0:
												_UpperCamelCase      : List[str]         =   dev_version

						print(F"""Updating version to {version}."""       )
						global_version_update(_lowercase       )
						print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''       )
						clean_main_ref_in_model_list()


if __name__ == "__main__":
						UpperCamelCase_									=argparse.ArgumentParser()
						parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
						parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
						UpperCamelCase_									=parser.parse_args()
						if not args.post_release:
												pre_release_work(patch=args.patch)
						elif args.patch:
												print("""Nothing to do after a patch :-)""")
						else:
												post_release_work()



