


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
_lowerCAmelCase:						int   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Union[str, Any]   				=       {
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
_lowerCAmelCase:						Tuple   				=       [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]






def   _lowercase(					__a				:						Optional[Any]     ):
							a__      ={}
							with open(__a    ,     'r'     ) as file:
														for line_number, line in enumerate(__a     ):
																					a__      =line.strip()
																					if line:
																												a__      =line.split()
																												a__      =line_number
																												a__      =words[0]
																												a__      =value
							return result






def   _lowercase(					__a				:						Dict    ,     __a				:						Optional[Any]    ,     __a				:						List[str]    ,     __a				:						Dict    ,     __a				:						str     ):
							for attribute in key.split('.'     ):
														a__      =getattr(__a    ,     __a     )

							a__      =None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(__a     ):
																					a__      =PARAM_MAPPING[full_name.split('.'     )[-1]]
																					a__      ='param'

							if weight_type is not None and weight_type != "param":
														a__      =getattr(__a    ,     __a     ).shape
							elif weight_type is not None and weight_type == "param":
														a__      =hf_pointer
														for attribute in hf_param_name.split('.'     ):
																					a__      =getattr(__a    ,     __a     )
														a__      =shape_pointer.shape

														# let's reduce dimension
														a__      =value[0]
							else:
														a__      =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__      =value
							elif weight_type == "weight_g":
														a__      =value
							elif weight_type == "weight_v":
														a__      =value
							elif weight_type == "bias":
														a__      =value
							elif weight_type == "param":
														for attribute in hf_param_name.split('.'     ):
																					a__      =getattr(__a    ,     __a     )
														a__      =value
							else:
														a__      =value

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






def   _lowercase(					__a				:						Optional[int]    ,     __a				:						int    ,     __a				:						Optional[int]    ,     __a				:						Optional[Any]    ,     __a				:						List[Any]     ):
							a__      =None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(__a     ):
																					a__      =PARAM_MAPPING[full_name.split('.'     )[-1]]
																					a__      ='param'

							if weight_type is not None and weight_type != "param":
														a__      ='.'.join([key, weight_type]     )
							elif weight_type is not None and weight_type == "param":
														a__      ='.'.join([key, hf_param_name]     )
							else:
														a__      =key

							a__      =value if 'lm_head' in full_key else value[0]


_lowerCAmelCase:						Dict   				=       {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}






def   _lowercase(					__a				:						Dict    ,     __a				:						int    ,     __a				:						int=None    ,     __a				:						List[str]=None     ):
							a__      =False
							for key, mapped_key in MAPPING.items():
														a__      ='wav2vec2.' + 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]:
																					a__      =True
																					if "*" in mapped_key:
																												a__      =name.split(__a     )[0].split('.'     )[-2]
																												a__      =mapped_key.replace('*'    ,     __a     )
																					if "weight_g" in name:
																												a__      ='weight_g'
																					elif "weight_v" in name:
																												a__      ='weight_v'
																					elif "bias" in name:
																												a__      ='bias'
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												a__      ='weight'
																					else:
																												a__      =None
																					if hf_dict is not None:
																												rename_dict(__a    ,     __a    ,     __a    ,     __a    ,     __a     )
																					else:
																												set_recursively(__a    ,     __a    ,     __a    ,     __a    ,     __a     )
																					return is_used
							return is_used






def   _lowercase(					__a				:						Union[str, Any]    ,     __a				:						List[str]    ,     __a				:						Dict     ):
							a__      =[]
							a__      =fairseq_model.state_dict()

							a__      =hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														a__      =False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __a    ,     __a    ,     __a    ,     __a    ,     hf_model.config.feat_extract_norm == 'group'    ,     )
																					a__      =True
														else:
																					a__      =load_wavaveca_layer(__a    ,     __a    ,     __a     )
														if not is_used:
																					unused_weights.append(__a     )

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






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






@torch.no_grad()
def   _lowercase(					__a				:						str    ,     __a				:						str    ,     __a				:						Any=None    ,     __a				:						str=None    ,     __a				:						Any=True    ,     __a				:						Union[str, Any]=False     ):
							if config_path is not None:
														a__      =WavaVecaConfig.from_pretrained(__a     )
							else:
														a__      =WavaVecaConfig()

							if is_seq_class:
														a__      =read_txt_into_dict(__a     )
														a__      =idalabel
														a__      =WavaVecaForSequenceClassification(__a     )
														a__      =WavaVecaFeatureExtractor(
														    feature_size=1    ,     sampling_rate=1_6000    ,     padding_value=0    ,     do_normalize=__a    ,     return_attention_mask=__a    ,     )
														feature_extractor.save_pretrained(__a     )

							elif is_finetuned:
														if dict_path:
																					a__      =Dictionary.load(__a     )

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					a__      =target_dict.pad_index
																					a__      =target_dict.bos_index
																					a__      =target_dict.eos_index
																					a__      =len(target_dict.symbols     )
																					a__      =os.path.join(__a    ,     'vocab.json'     )
																					if not os.path.isdir(__a     ):
																												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__a     )     )
																												return
																					os.makedirs(__a    ,     exist_ok=__a     )
																					a__      =target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					a__      =0
																					a__      =1
																					with open(__a    ,     'w'    ,     encoding='utf-8'     ) as vocab_handle:
																												json.dump(__a    ,     __a     )
																					a__      =WavaVecaCTCTokenizer(
																					    __a    ,     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=__a    ,     )
																					a__      =True if config.feat_extract_norm == 'layer' else False
																					a__      =WavaVecaFeatureExtractor(
																					    feature_size=1    ,     sampling_rate=1_6000    ,     padding_value=0    ,     do_normalize=__a    ,     return_attention_mask=__a    ,     )
																					a__      =WavaVecaProcessor(feature_extractor=__a    ,     tokenizer=__a     )
																					processor.save_pretrained(__a     )

														a__      =WavaVecaForCTC(__a     )
							else:
														a__      =WavaVecaForPreTraining(__a     )

							if is_finetuned or is_seq_class:
														a__    ,     a__    ,     a__      =fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]    ,     arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
							else:
														a__      =argparse.Namespace(task='audio_pretraining'     )
														a__      =fairseq.tasks.setup_task(__a     )

														a__    ,     a__    ,     a__      =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,     task=__a     )

							a__      =model[0].eval()

							recursively_load_weights(__a    ,     __a    ,     not is_finetuned     )

							hf_wavavec.save_pretrained(__a     )


if __name__ == "__main__":
	_lowerCAmelCase:						Any   				=       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'
	)
	parser.add_argument(
	    '--is_seq_class',
	    action='store_true',
	    help='Whether the model to convert is a fine-tuned sequence classification model or not',
	)
	_lowerCAmelCase:						Tuple   				=       parser.parse_args()

	_lowerCAmelCase:						Tuple   				=       not args.not_finetuned and not args.is_seq_class
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.dict_path,
	    is_finetuned,
	    args.is_seq_class,
	)





'''simple docstring'''







import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
       if is_torch_available():
              import torch
       if is_tf_available():
              import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


lowerCAmelCase     :  Optional[int]											=    logging.get_logger(__name__)

lowerCAmelCase     :  Union[str, Any]											=    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase     :  Optional[Any]											=    {
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """Salesforce/codegen-350M-mono""": (
            """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCAmelCase     :  List[str]											=    {
    """Salesforce/codegen-350M-mono""": 20_48,
}
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     VOCAB_FILES_NAMES
     __magic_name__           =     PRETRAINED_VOCAB_FILES_MAP
     __magic_name__           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__           =     ["input_ids", "attention_mask"]
     __magic_name__           =     CodeGenTokenizer







     def __init__(			self   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__=False   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(
                snake_case__   ,						snake_case__   ,						tokenizer_file=snake_case__   ,						unk_token=snake_case__   ,						bos_token=snake_case__   ,						eos_token=snake_case__   ,						add_prefix_space=snake_case__   ,						**snake_case__   ,						)

            if kwargs.pop('add_bos_token'   ,						snake_case__ ):
                   _lowerCAmelCase     :				Tuple       =							kwargs.pop('name_or_path'   ,						'' )
                   raise ValueError(
                       'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
                       'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
                       F'`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'
                       F'`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'
                       'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
                       ' so that the fast tokenizer works correctly.' )

            _lowerCAmelCase     :				List[str]       =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
            if pre_tok_state.get('add_prefix_space'   ,						snake_case__ ) != add_prefix_space:
                   _lowerCAmelCase     :				List[Any]       =							getattr(snake_case__   ,						pre_tok_state.pop('type' ) )
                   _lowerCAmelCase     :				Dict       =							add_prefix_space
                   _lowerCAmelCase     :				List[Any]       =							pre_tok_class(**snake_case__ )

            _lowerCAmelCase     :				Tuple       =							add_prefix_space







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							kwargs.get('is_split_into_words'   ,						snake_case__ )
            assert self.add_prefix_space or not is_split_into_words, (
                F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

            return super()._batch_encode_plus(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							kwargs.get('is_split_into_words'   ,						snake_case__ )

            assert self.add_prefix_space or not is_split_into_words, (
                F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

            return super()._encode_plus(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self._tokenizer.model.save(snake_case__   ,						name=snake_case__ )
            return tuple(snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = False   ,						snake_case__ = None   ,						snake_case__ = None   ,						**snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							super().decode(
                token_ids=snake_case__   ,						skip_special_tokens=snake_case__   ,						clean_up_tokenization_spaces=snake_case__   ,						**snake_case__   ,						)

            if truncate_before_pattern is not None and len(snake_case__ ) > 0:
                   _lowerCAmelCase     :				Optional[int]       =							self.truncate(snake_case__   ,						snake_case__ )

            return decoded_text







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            def find_re(snake_case__   ,						snake_case__   ,						snake_case__ ):
                   _lowerCAmelCase     :				List[Any]       =							pattern.search(snake_case__   ,						snake_case__ )
                   return m.start() if m else -1

            _lowerCAmelCase     :				Any       =							[re.compile(snake_case__   ,						re.MULTILINE ) for pattern in truncate_before_pattern]

            _lowerCAmelCase     :				Dict       =							list(re.finditer('^print'   ,						snake_case__   ,						re.MULTILINE ) )

            if len(snake_case__ ) > 1:
                   _lowerCAmelCase     :				Optional[int]       =							completion[: prints[1].start()]

            _lowerCAmelCase     :				Optional[int]       =							list(re.finditer('^def'   ,						snake_case__   ,						re.MULTILINE ) )

            if len(snake_case__ ) > 1:
                   _lowerCAmelCase     :				Optional[Any]       =							completion[: defs[1].start()]

            _lowerCAmelCase     :				int       =							0

            _lowerCAmelCase     :				Tuple       =							[
                pos for pos in [find_re(snake_case__   ,						snake_case__   ,						snake_case__ ) for terminal in terminals] if pos != -1
            ]

            if len(snake_case__ ) > 0:
                   return completion[: min(snake_case__ )]
            else:
                   return completion

