



"""simple docstring"""

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()
a_			   =					logging.get_logger(__name__)

a_			   =					{
    '''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''',
}
a_			   =					[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Tuple			):



					"""simple docstring"""





					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
																				snake_case_					:		List[str] 					=					"""lm_head"""

										snake_case_					:		Tuple 					=					getattr(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)

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

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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					[]
					snake_case_					:		Optional[Any] 					=					fairseq_model.state_dict()

					snake_case_					:		str 					=					hf_model.unispeech.feature_extractor

					for name, value in fairseq_dict.items():
										snake_case_					:		Tuple 					=					False
										if "conv_layers" in name:
															load_conv_layer(
															    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    hf_model.config.feat_extract_norm == """group"""				,    )
															snake_case_					:		Optional[Any] 					=					True
										else:
															for key, mapped_key in MAPPING.items():
																				snake_case_					:		Dict 					=					"""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]:
																									snake_case_					:		Dict 					=					True
																									if "*" in mapped_key:
																														snake_case_					:		Optional[Any] 					=					name.split(SCREAMING_SNAKE_CASE__			)[0].split("""."""			)[-2]
																														snake_case_					:		Union[str, Any] 					=					mapped_key.replace("""*"""				,    SCREAMING_SNAKE_CASE__			)
																									if "weight_g" in name:
																														snake_case_					:		Optional[int] 					=					"""weight_g"""
																									elif "weight_v" in name:
																														snake_case_					:		Tuple 					=					"""weight_v"""
																									elif "bias" in name:
																														snake_case_					:		Any 					=					"""bias"""
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														snake_case_					:		str 					=					"""weight"""
																									else:
																														snake_case_					:		List[str] 					=					None
																									set_recursively(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
																				continue
										if not is_used:
															unused_weights.append(SCREAMING_SNAKE_CASE__			)

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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					:		Any 					=					full_name.split("""conv_layers."""			)[-1]
					snake_case_					:		int 					=					name.split("""."""			)
					snake_case_					:		List[Any] 					=					int(items[0]			)
					snake_case_					:		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.'
															)
															snake_case_					:		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.'
															)
															snake_case_					:		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."
															)
															snake_case_					:		str 					=					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.'
															)
															snake_case_					:		int 					=					value
															logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
					else:
										unused_weights.append(SCREAMING_SNAKE_CASE__			)







@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  Optional[int]				,    SCREAMING_SNAKE_CASE__ :  Tuple=None				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]=None				,    SCREAMING_SNAKE_CASE__ :  List[str]=True			):



					"""simple docstring"""





					if config_path is not None:
										snake_case_					:		Union[str, Any] 					=					UniSpeechConfig.from_pretrained(SCREAMING_SNAKE_CASE__			)
					else:
										snake_case_					:		Optional[int] 					=					UniSpeechConfig()

					if is_finetuned:
										if dict_path:
															snake_case_					:		Optional[int] 					=					Dictionary.load_from_json(SCREAMING_SNAKE_CASE__			)

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															snake_case_					:		Union[str, Any] 					=					target_dict.pad_index
															snake_case_					:		List[Any] 					=					target_dict.bos_index
															snake_case_					:		str 					=					target_dict.eos_index
															snake_case_					:		str 					=					len(target_dict.symbols			)
															snake_case_					:		Dict 					=					os.path.join(SCREAMING_SNAKE_CASE__				,    """vocab.json"""			)
															if not os.path.isdir(SCREAMING_SNAKE_CASE__			):
																				logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE__			)			)
																				return
															os.makedirs(SCREAMING_SNAKE_CASE__				,    exist_ok=SCREAMING_SNAKE_CASE__			)
															snake_case_					:		Optional[Any] 					=					target_dict.indices

															# fairseq has the <pad> and <s> switched
															snake_case_					:		int 					=					4_2
															snake_case_					:		Optional[int] 					=					4_3
															with open(SCREAMING_SNAKE_CASE__				,    """w"""				,    encoding="""utf-8"""			) as vocab_handle:
																				json.dump(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
															snake_case_					:		int 					=					WavaVecaPhonemeCTCTokenizer(
															    SCREAMING_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=SCREAMING_SNAKE_CASE__				,    )
															snake_case_					:		str 					=					True if config.feat_extract_norm == """layer""" else False
															snake_case_					:		Union[str, Any] 					=					WavaVecaFeatureExtractor(
															    feature_size=1				,    sampling_rate=1_6_0_0_0				,    padding_value=0				,    do_normalize=SCREAMING_SNAKE_CASE__				,    return_attention_mask=SCREAMING_SNAKE_CASE__				,    )
															snake_case_					:		Union[str, Any] 					=					WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__				,    tokenizer=SCREAMING_SNAKE_CASE__			)
															processor.save_pretrained(SCREAMING_SNAKE_CASE__			)

										snake_case_					:		List[str] 					=					UniSpeechForCTC(SCREAMING_SNAKE_CASE__			)
					else:
										snake_case_					:		Dict 					=					UniSpeechForPreTraining(SCREAMING_SNAKE_CASE__			)

					if is_finetuned:
										snake_case_					:		Tuple 					=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path]				,    arg_overrides={"""data""": """/""".join(dict_path.split("""/"""			)[:-1]			), """w2v_path""": checkpoint_path}			)
					else:
										snake_case_					:		Optional[int] 					=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

					snake_case_					:		Any 					=					model[0].eval()

					recursively_load_weights(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)

					hf_unispeech.save_pretrained(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
			a_			   =					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'''
			)
			a_			   =					parser.parse_args()
			convert_unispeech_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)





"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Union[str, Any]							       =		["""image_processor""", """tokenizer"""]
						_A		:							str							       =		"""ChineseCLIPImageProcessor"""
						_A		:							Tuple							       =		("""BertTokenizer""", """BertTokenizerFast""")







						def __init__(self   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):
											snake_case_					:		Any 					=					None
											if "feature_extractor" in kwargs:
																warnings.warn(
																    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																    """ instead."""   ,		lowercase__   ,		)
																snake_case_					:		Optional[Any] 					=					kwargs.pop("""feature_extractor"""       )

											snake_case_					:		str 					=					image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																raise ValueError("""You need to specify an `image_processor`."""       )
											if tokenizer is None:
																raise ValueError("""You need to specify a `tokenizer`."""       )

											super().__init__(lowercase__   ,		lowercase__       )
											snake_case_					:		Union[str, Any] 					=					self.image_processor







						def __call__(self   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):

											if text is None and images is None:
																raise ValueError("""You have to specify either text or images. Both cannot be none."""       )

											if text is not None:
																snake_case_					:		Any 					=					self.tokenizer(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if images is not None:
																snake_case_					:		Tuple 					=					self.image_processor(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if text is not None and images is not None:
																snake_case_					:		List[Any] 					=					image_features.pixel_values
																return encoding
											elif text is not None:
																return encoding
											else:
																return BatchEncoding(data=dict(**lowercase__       )   ,		tensor_type=lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.batch_decode(*lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.decode(*lowercase__   ,		**lowercase__       )







						@property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					self.tokenizer.model_input_names
											snake_case_					:		Union[str, Any] 					=					self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )







						@property
						def 						__UpperCamelCase							(self       ):
											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,		lowercase__   ,		)
											return self.image_processor_class




