



"""simple docstring"""


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
snake_case_								=			logging.get_logger(__name__)

snake_case_								=			{
    """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""",
    """mask_emb""": """masked_spec_embed""",
}

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_						,      lowercase_		):
						for attribute in key.split('.'		):
												UpperCAmelCase			 =					getattr(lowercase_						,      lowercase_		)

						if weight_type is not None:
												UpperCAmelCase			 =					getattr(lowercase_						,      lowercase_		).shape
						else:
												UpperCAmelCase			 =					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			 =					value
						elif weight_type == "weight_g":
												UpperCAmelCase			 =					value
						elif weight_type == "weight_v":
												UpperCAmelCase			 =					value
						elif weight_type == "bias":
												UpperCAmelCase			 =					value
						else:
												UpperCAmelCase			 =					value

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

def 					_lowerCAmelCase (  lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					[]
						UpperCAmelCase			 =					fairseq_model.state_dict()

						UpperCAmelCase			 =					hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												UpperCAmelCase			 =					False
												if "conv_layers" in name:
																		load_conv_layer(
																		    lowercase_						,      lowercase_						,      lowercase_						,      lowercase_						,      hf_model.config.feat_extract_norm == 'group'						,      )
																		UpperCAmelCase			 =					True
												else:
																		for key, mapped_key in MAPPING.items():
																								UpperCAmelCase			 =					mapped_key

																								if key in name:
																														UpperCAmelCase			 =					True
																														if "*" in mapped_key:
																																				UpperCAmelCase			 =					name.split(lowercase_		)[0].split('.'		)[-2]
																																				UpperCAmelCase			 =					mapped_key.replace('*'						,      lowercase_		)
																														if "weight_g" in name:
																																				UpperCAmelCase			 =					'weight_g'
																														elif "weight_v" in name:
																																				UpperCAmelCase			 =					'weight_v'
																														elif "weight" in name:
																																				UpperCAmelCase			 =					'weight'
																														elif "bias" in name:
																																				UpperCAmelCase			 =					'bias'
																														else:
																																				UpperCAmelCase			 =					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 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					full_name.split('conv_layers.'		)[-1]
						UpperCAmelCase			 =					name.split('.'		)
						UpperCAmelCase			 =					int(items[0]		)
						UpperCAmelCase			 =					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			 =					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			 =					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			 =					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			 =					value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
						else:
												unused_weights.append(lowercase_		)

def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					HubertConfig()
						UpperCAmelCase			 =					model.config

						UpperCAmelCase			 =					fs_config.activation_dropout
						UpperCAmelCase			 =					False
						UpperCAmelCase			 =					fs_config.attention_dropout
						UpperCAmelCase			 =					False
						UpperCAmelCase			 =					eval(fs_config.extractor_conv_feature_layers		)
						UpperCAmelCase			 =					[x[0] for x in conv_layers]
						UpperCAmelCase			 =					[x[1] for x in conv_layers]
						UpperCAmelCase			 =					[x[2] for x in conv_layers]
						UpperCAmelCase			 =					'gelu'
						UpperCAmelCase			 =					'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
						UpperCAmelCase			 =					False
						UpperCAmelCase			 =					0.0
						UpperCAmelCase			 =					0.0
						UpperCAmelCase			 =					fs_config.activation_fn
						UpperCAmelCase			 =					fs_config.dropout
						UpperCAmelCase			 =					fs_config.encoder_embed_dim
						UpperCAmelCase			 =					0.0_2
						UpperCAmelCase			 =					fs_config.encoder_ffn_embed_dim
						UpperCAmelCase			 =					1e-5
						UpperCAmelCase			 =					0.0
						UpperCAmelCase			 =					fs_config.encoder_attention_heads
						UpperCAmelCase			 =					fs_config.conv_pos_groups
						UpperCAmelCase			 =					fs_config.conv_pos
						UpperCAmelCase			 =					len(lowercase_		)
						UpperCAmelCase			 =					fs_config.encoder_layers

						return config

@torch.no_grad()
def 					_lowerCAmelCase (  lowercase_						,      lowercase_=None		):
						UpperCAmelCase			 =					distilhubert().model.model

						if config_path is not None:
												UpperCAmelCase			 =					HubertConfig.from_pretrained(lowercase_		)
						else:
												UpperCAmelCase			 =					convert_config(lowercase_		)
						UpperCAmelCase			 =					model.eval()

						UpperCAmelCase			 =					WavaVecaFeatureExtractor(
						    feature_size=1						,      sampling_rate=16000						,      padding_value=0						,      do_normalize=lowercase_						,      return_attention_mask=lowercase_						,      )
						UpperCAmelCase			 =					HubertModel(lowercase_		)

						recursively_load_weights(lowercase_						,      lowercase_		)

						feature_extractor.save_pretrained(lowercase_		)
						hf_model.save_pretrained(lowercase_		)


if __name__ == "__main__":
				snake_case_								=			argparse.ArgumentParser()
				parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
				parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
				snake_case_								=			parser.parse_args()
				convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)










"""simple docstring"""


from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case_								=			{
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case_								=			{
    """openai/clip-vit-base-patch32""": 77,
}







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				VOCAB_FILES_NAMES
						__UpperCamelCase													=				PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase													=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase													=				["""input_ids""", """attention_mask"""]
						__UpperCamelCase													=				CLIPTokenizer






						def __init__(   self					:int  ,    lowercase_					:Optional[int]=None  ,    lowercase_					:int=None  ,    lowercase_					:Optional[Any]=None  ,    lowercase_					:Dict="<|endoftext|>"  ,    lowercase_					:List[Any]="<|startoftext|>"  ,    lowercase_					:Any="<|endoftext|>"  ,    lowercase_					:List[str]="<|endoftext|>"  ,    **lowercase_					:Tuple  ,    )							->     List[Any]:
												super().__init__(
												    lowercase_  ,    lowercase_  ,    tokenizer_file=lowercase_  ,    unk_token=lowercase_  ,    bos_token=lowercase_  ,    eos_token=lowercase_  ,    pad_token=lowercase_  ,    **lowercase_  ,    )

												if not isinstance(self.backend_tokenizer.pre_tokenizer  ,    pre_tokenizers.Sequence   ):
																		raise ValueError(
																		    'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
																		    ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
																		    ' to be compatible with this version.The easiest way to do so is'
																		    ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
																		    ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
																		    ' transformers.'   )

												self._wrap_decode_method_backend_tokenizer()






						def       UpperCAmelCase__	(   self					:Dict   )							->     List[Any]:
												UpperCAmelCase			 =					self.backend_tokenizer.decode

												def new_decode_method(*lowercase_					:List[Any]  ,    **lowercase_					:Optional[Any]   ):
																		UpperCAmelCase			 =					orig_decode_method(*lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					text.replace(self.backend_tokenizer.model.end_of_word_suffix  ,    ' '   ).strip()
																		return text

												UpperCAmelCase			 =					new_decode_method






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.bos_token_id]
												UpperCAmelCase			 =					[self.eos_token_id]

												if token_ids_a is None:
																		return bos_token + token_ids_a + eos_token
												return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.bos_token_id]
												UpperCAmelCase			 =					[self.eos_token_id]

												if token_ids_a is None:
																		return len(bos_token + token_ids_a + eos_token   ) * [0]
												return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token   ) * [0]






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:str  ,    lowercase_					:Optional[str] = None   )							->     Tuple[str]:
												UpperCAmelCase			 =					self._tokenizer.model.save(lowercase_  ,    name=lowercase_   )
												return tuple(lowercase_   )





