


'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__UpperCAmelCase       							=				{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''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''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__UpperCAmelCase       							=				[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       Optional[int]:
							for attribute in key.split('''.'''			):
														lowerCAmelCase__			  =      getattr(A					,       A			)

							if weight_type is not None:
														lowerCAmelCase__			  =      getattr(A					,       A			).shape
							else:
														lowerCAmelCase__			  =      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":
														lowerCAmelCase__			  =      value
							elif weight_type == "weight_g":
														lowerCAmelCase__			  =      value
							elif weight_type == "weight_v":
														lowerCAmelCase__			  =      value
							elif weight_type == "bias":
														lowerCAmelCase__			  =      value
							elif weight_type == "running_mean":
														lowerCAmelCase__			  =      value
							elif weight_type == "running_var":
														lowerCAmelCase__			  =      value
							elif weight_type == "num_batches_tracked":
														lowerCAmelCase__			  =      value
							elif weight_type == "inv_freq":
														lowerCAmelCase__			  =      value
							else:
														lowerCAmelCase__			  =      value

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


def    _snake_case  (	A					,       A					,       A			)		->       int:
							lowerCAmelCase__			  =      []
							lowerCAmelCase__			  =      fairseq_model.state_dict()

							lowerCAmelCase__			  =      hf_model.wavaveca_conformer.feature_extractor

							for name, value in fairseq_dict.items():
														lowerCAmelCase__			  =      False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A					,       A					,       A					,       A					,       hf_model.config.feat_extract_norm == '''group'''					,       )
																					lowerCAmelCase__			  =      True
														else:
																					for key, mapped_key in MAPPING.items():
																												lowerCAmelCase__			  =      '''wav2vec2_conformer.''' + 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]:
																																			lowerCAmelCase__			  =      True
																																			if "*" in mapped_key:
																																										lowerCAmelCase__			  =      name.split(A			)[0].split('''.'''			)[-2]
																																										lowerCAmelCase__			  =      mapped_key.replace('''*'''					,       A			)
																																			if "pos_bias_u" in name:
																																										lowerCAmelCase__			  =      None
																																			elif "pos_bias_v" in name:
																																										lowerCAmelCase__			  =      None
																																			elif "weight_g" in name:
																																										lowerCAmelCase__			  =      '''weight_g'''
																																			elif "weight_v" in name:
																																										lowerCAmelCase__			  =      '''weight_v'''
																																			elif "bias" in name:
																																										lowerCAmelCase__			  =      '''bias'''
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										lowerCAmelCase__			  =      '''weight'''
																																			elif "running_mean" in name:
																																										lowerCAmelCase__			  =      '''running_mean'''
																																			elif "inv_freq" in name:
																																										lowerCAmelCase__			  =      '''inv_freq'''
																																			elif "running_var" in name:
																																										lowerCAmelCase__			  =      '''running_var'''
																																			elif "num_batches_tracked" in name:
																																										lowerCAmelCase__			  =      '''num_batches_tracked'''
																																			else:
																																										lowerCAmelCase__			  =      None
																																			set_recursively(A					,       A					,       A					,       A					,       A			)
																												continue
														if not is_used:
																					unused_weights.append(A			)

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


def    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       Optional[Any]:
							lowerCAmelCase__			  =      full_name.split('''conv_layers.'''			)[-1]
							lowerCAmelCase__			  =      name.split('''.'''			)
							lowerCAmelCase__			  =      int(items[0]			)
							lowerCAmelCase__			  =      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."""			)
																					lowerCAmelCase__			  =      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."""			)
																					lowerCAmelCase__			  =      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."""			)
																					lowerCAmelCase__			  =      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."""			)
																					lowerCAmelCase__			  =      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    _snake_case  (	A					,       A					,       A=None					,       A=None					,       A=True			)		->       str:
							if config_path is not None:
														lowerCAmelCase__			  =      WavaVecaConformerConfig.from_pretrained(A					,       hidden_act='''swish'''			)
							else:
														lowerCAmelCase__			  =      WavaVecaConformerConfig()

							if "rope" in checkpoint_path:
														lowerCAmelCase__			  =      '''rotary'''

							if is_finetuned:
														if dict_path:
																					lowerCAmelCase__			  =      Dictionary.load(A			)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					lowerCAmelCase__			  =      target_dict.pad_index
																					lowerCAmelCase__			  =      target_dict.bos_index
																					lowerCAmelCase__			  =      target_dict.eos_index
																					lowerCAmelCase__			  =      len(target_dict.symbols			)
																					lowerCAmelCase__			  =      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			)
																					lowerCAmelCase__			  =      target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					lowerCAmelCase__			  =      0
																					lowerCAmelCase__			  =      1
																					with open(A					,       '''w'''					,       encoding='''utf-8'''			) as vocab_handle:
																												json.dump(A					,       A			)
																					lowerCAmelCase__			  =      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					,       )
																					lowerCAmelCase__			  =      True if config.feat_extract_norm == '''layer''' else False
																					lowerCAmelCase__			  =      WavaVecaFeatureExtractor(
																					    feature_size=1					,       sampling_rate=16000					,       padding_value=0					,       do_normalize=A					,       return_attention_mask=A					,       )
																					lowerCAmelCase__			  =      WavaVecaProcessor(feature_extractor=A					,       tokenizer=A			)
																					processor.save_pretrained(A			)

														lowerCAmelCase__			  =      WavaVecaConformerForCTC(A			)
							else:
														lowerCAmelCase__			  =      WavaVecaConformerForPreTraining(A			)

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

														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,       task=A			)

							lowerCAmelCase__			  =      model[0].eval()

							recursively_load_weights(A					,       A					,       not is_finetuned			)

							hf_wavavec.save_pretrained(A			)


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






import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   OpenAIGPTTokenizer
      UpperCAmelCase__							=   OpenAIGPTTokenizerFast
      UpperCAmelCase__							=   True
      UpperCAmelCase__							=   False


      def 					__snake_case      (		self):



             """simple docstring"""





             super().setUp()

             # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
             _lowerCamelCase				:  Tuple						=		[
                 '''l''',
                 '''o''',
                 '''w''',
                 '''e''',
                 '''r''',
                 '''s''',
                 '''t''',
                 '''i''',
                 '''d''',
                 '''n''',
                 '''w</w>''',
                 '''r</w>''',
                 '''t</w>''',
                 '''lo''',
                 '''low''',
                 '''er</w>''',
                 '''low</w>''',
                 '''lowest</w>''',
                 '''newer</w>''',
                 '''wider</w>''',
                 '''<unk>''',
             ]
             _lowerCamelCase				:  Any						=		dict(zip(a__   ,				range(len(a__))))
             _lowerCamelCase				:  List[str]						=		['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

             _lowerCamelCase				:  str						=		os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file'''])
             _lowerCamelCase				:  int						=		os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''merges_file'''])
             with open(self.vocab_file   ,				'''w''') as fp:
                    fp.write(json.dumps(a__))
             with open(self.merges_file   ,				'''w''') as fp:
                    fp.write('''\n'''.join(a__))


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return "lower newer", "lower newer"


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		OpenAIGPTTokenizer(self.vocab_file   ,				self.merges_file)

             _lowerCamelCase				:  Dict						=		'''lower'''
             _lowerCamelCase				:  Optional[Any]						=		['''low''', '''er</w>''']
             _lowerCamelCase				:  List[Any]						=		tokenizer.tokenize(a__)
             self.assertListEqual(a__   ,				a__)

             _lowerCamelCase				:  Any						=		tokens + ['''<unk>''']
             _lowerCamelCase				:  Optional[int]						=		[14, 15, 20]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(a__)   ,				a__)


      def 					__snake_case      (		self   ,				a__=15):



             """simple docstring"""





             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                           _lowerCamelCase				:  Dict						=		self.rust_tokenizer_class.from_pretrained(a__   ,				**a__)

                           # Simple input
                           _lowerCamelCase				:  Optional[int]						=		'''This is a simple input'''
                           _lowerCamelCase				:  Optional[Any]						=		['''This is a simple input 1''', '''This is a simple input 2''']
                           _lowerCamelCase				:  Optional[int]						=		('''This is a simple input''', '''This is a pair''')
                           _lowerCamelCase				:  List[Any]						=		[
                               ('''This is a simple input 1''', '''This is a simple input 2'''),
                               ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                           ]

                           # Simple input tests
                           self.assertRaises(a__   ,				tokenizer_r.encode   ,				a__   ,				max_length=a__   ,				padding='''max_length''')

                           # Simple input
                           self.assertRaises(a__   ,				tokenizer_r.encode_plus   ,				a__   ,				max_length=a__   ,				padding='''max_length''')

                           # Simple input
                           self.assertRaises(
                               a__   ,				tokenizer_r.batch_encode_plus   ,				a__   ,				max_length=a__   ,				padding='''max_length'''   ,				)

                           # Pair input
                           self.assertRaises(a__   ,				tokenizer_r.encode   ,				a__   ,				max_length=a__   ,				padding='''max_length''')

                           # Pair input
                           self.assertRaises(a__   ,				tokenizer_r.encode_plus   ,				a__   ,				max_length=a__   ,				padding='''max_length''')

                           # Pair input
                           self.assertRaises(
                               a__   ,				tokenizer_r.batch_encode_plus   ,				a__   ,				max_length=a__   ,				padding='''max_length'''   ,				)



      def 					__snake_case      (		self):



             """simple docstring"""





             pass







@require_ftfy
@require_spacy
@require_tokenizers
class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      pass

