



'''simple docstring'''




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

lowerCamelCase							:Tuple				    =					{
    '''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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''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''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
lowerCamelCase							:Union[str, Any]				    =					[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			for attribute in key.split("""."""    ):
						A_	:						Optional[Any]	=     getattr(lowerCamelCase__						,      lowerCamelCase__    )

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

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



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						int	=     []
			A_	:						str	=     fairseq_model.state_dict()

			A_	:						Dict	=     hf_model.unispeech_sat.feature_extractor

			for name, value in fairseq_dict.items():
						A_	:						Optional[Any]	=     False
						if "conv_layers" in name:
									load_conv_layer(
									    lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      hf_model.config.feat_extract_norm == """group"""						,      )
									A_	:						Optional[int]	=     True
						else:
									for key, mapped_key in MAPPING.items():
												A_	:						str	=     """unispeech_sat.""" + 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]:
															if "layer_norm_for_extract" in name and (".".join(name.split("""."""    )[:-1]    ) != key):
																		# special case since naming is very similar
																		continue
															A_	:						Optional[int]	=     True
															if "*" in mapped_key:
																		A_	:						Optional[Any]	=     name.split(lowerCamelCase__    )[0].split("""."""    )[-2]
																		A_	:						Dict	=     mapped_key.replace("""*"""						,      lowerCamelCase__    )
															if "weight_g" in name:
																		A_	:						Tuple	=     """weight_g"""
															elif "weight_v" in name:
																		A_	:						int	=     """weight_v"""
															elif "bias" in name:
																		A_	:						Union[str, Any]	=     """bias"""
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		A_	:						Optional[Any]	=     """weight"""
															else:
																		A_	:						Any	=     None
															set_recursively(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )
												continue
						if not is_used:
									unused_weights.append(lowerCamelCase__    )

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



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Union[str, Any]	=     full_name.split("""conv_layers."""    )[-1]
			A_	:						Optional[int]	=     name.split("""."""    )
			A_	:						Any	=     int(items[0]    )
			A_	:						List[str]	=     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_	:						Union[str, Any]	=     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_	:						str	=     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[layer_id].layer_norm.bias.data.shape} was found.'    )
									A_	:						Dict	=     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[layer_id].layer_norm.weight.data.shape} was found.'    )
									A_	:						Dict	=     value
									logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
			else:
						unused_weights.append(lowerCamelCase__    )



@torch.no_grad()
def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      lowerCamelCase__=True    ):
			'''simple docstring'''





			if config_path is not None:
						A_	:						Optional[int]	=     UniSpeechSatConfig.from_pretrained(lowerCamelCase__    )
			else:
						A_	:						Dict	=     UniSpeechSatConfig()

			A_	:						List[str]	=     """"""

			if is_finetuned:
						A_	:						Any	=     UniSpeechSatForCTC(lowerCamelCase__    )
			else:
						A_	:						Optional[int]	=     UniSpeechSatForPreTraining(lowerCamelCase__    )

			A_,    A_,    A_	:						Optional[int]	=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]						,      arg_overrides={"""data""": """/""".join(dict_path.split("""/"""    )[:-1]    )}    )
			A_	:						List[Any]	=     model[0].eval()

			recursively_load_weights(lowerCamelCase__						,      lowerCamelCase__    )

			hf_wavavec.save_pretrained(lowerCamelCase__    )


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(
						    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
						)
						lowerCamelCase							:str				    =					parser.parse_args()
						convert_unispeech_sat_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)




'''simple docstring'''




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		Any					=					['image_processor', 'tokenizer']
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					'BlipImageProcessor'
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					('BertTokenizer', 'BertTokenizerFast')


	def __init__(self       ,      lowercase       ,      lowercase							):
				A_	:						List[Any]	=     False
				super().__init__(lowercase       ,      lowercase							)
				A_	:						Tuple	=     self.image_processor


	def __call__(self       ,      lowercase = None       ,      lowercase = None       ,      lowercase = True       ,      lowercase = False       ,      lowercase = None       ,      lowercase = None       ,      lowercase = 0       ,      lowercase = None       ,      lowercase = None       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = True       ,      lowercase = None       ,      **lowercase       ,      ):
				if images is None and text is None:
							raise ValueError("""You have to specify either images or text."""							)

				# Get only text
				if images is None:
							A_	:						Optional[Any]	=     self.tokenizer
							A_	:						Tuple	=     self.tokenizer(
							    text=lowercase       ,      add_special_tokens=lowercase       ,      padding=lowercase       ,      truncation=lowercase       ,      max_length=lowercase       ,      stride=lowercase       ,      pad_to_multiple_of=lowercase       ,      return_attention_mask=lowercase       ,      return_overflowing_tokens=lowercase       ,      return_special_tokens_mask=lowercase       ,      return_offsets_mapping=lowercase       ,      return_token_type_ids=lowercase       ,      return_length=lowercase       ,      verbose=lowercase       ,      return_tensors=lowercase       ,      **lowercase       ,      )
							return text_encoding

				# add pixel_values
				A_	:						int	=     self.image_processor(lowercase       ,      return_tensors=lowercase							)

				if text is not None:
							A_	:						Optional[Any]	=     self.tokenizer(
							    text=lowercase       ,      add_special_tokens=lowercase       ,      padding=lowercase       ,      truncation=lowercase       ,      max_length=lowercase       ,      stride=lowercase       ,      pad_to_multiple_of=lowercase       ,      return_attention_mask=lowercase       ,      return_overflowing_tokens=lowercase       ,      return_special_tokens_mask=lowercase       ,      return_offsets_mapping=lowercase       ,      return_token_type_ids=lowercase       ,      return_length=lowercase       ,      verbose=lowercase       ,      return_tensors=lowercase       ,      **lowercase       ,      )
				else:
							A_	:						List[str]	=     None

				if text_encoding is not None:
							encoding_image_processor.update(lowercase							)

				return encoding_image_processor


	def     _a	(self       ,      *lowercase       ,      **lowercase							):
				return self.tokenizer.batch_decode(*lowercase       ,      **lowercase							)


	def     _a	(self       ,      *lowercase       ,      **lowercase							):
				return self.tokenizer.decode(*lowercase       ,      **lowercase							)


	@property
	def     _a	(self							):
				A_	:						int	=     self.tokenizer.model_input_names
				A_	:						Any	=     self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)