'''simple docstring'''




import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 		__A							(__magic_name__			,       unittest.TestCase ):
       snake_case							:str					     =       XLMTokenizer
       snake_case							:Optional[Any]					     =       False






       def       _snake_case    (   self   ):
              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              __UpperCAmelCase	:					Dict												=		[
                  "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>",
              ]
              __UpperCAmelCase	:					Union[str, Any]												=		dict(zip(a_					,  range(len(a_   )   )   )   )
              __UpperCAmelCase	:					Optional[int]												=		["l o 123", "lo w 1456", "e r</w> 1789", ""]

              __UpperCAmelCase	:					Any												=		os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES["vocab_file"]   )
              __UpperCAmelCase	:					Dict												=		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					,  UpperCamelCase_   ):
              __UpperCAmelCase	:					str												=		"lower newer"
              __UpperCAmelCase	:					Any												=		"lower newer"
              return input_text, output_text






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Union[str, Any]												=		XLMTokenizer(self.vocab_file					,  self.merges_file   )

              __UpperCAmelCase	:					Dict												=		"lower"
              __UpperCAmelCase	:					str												=		["low", "er</w>"]
              __UpperCAmelCase	:					Optional[int]												=		tokenizer.tokenize(a_   )
              self.assertListEqual(a_					,  a_   )

              __UpperCAmelCase	:					Dict												=		tokens + ["<unk>"]
              __UpperCAmelCase	:					List[Any]												=		[14, 15, 20]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(a_   )					,  a_   )







       @slow
       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Tuple												=		XLMTokenizer.from_pretrained("xlm-mlm-en-2048"   )

              __UpperCAmelCase	:					Dict												=		tokenizer.encode("sequence builders"					,  add_special_tokens=a_   )
              __UpperCAmelCase	:					List[Any]												=		tokenizer.encode("multi-sequence build"					,  add_special_tokens=a_   )

              __UpperCAmelCase	:					Optional[int]												=		tokenizer.build_inputs_with_special_tokens(a_   )
              __UpperCAmelCase	:					Optional[int]												=		tokenizer.build_inputs_with_special_tokens(a_					,  a_   )

              assert encoded_sentence == [0] + text + [1]
              assert encoded_pair == [0] + text + [1] + text_a + [1]











import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

_lowerCAmelCase	:   str    =   {
    "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": "lm_head",
    "mask_emb": "masked_spec_embed",
}
_lowerCAmelCase	:   List[str]    =   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     Any:



				"""simple docstring"""





				for attribute in key.split('.'					):
								lowerCAmelCase__		  =  getattr(snake_case__    ,			snake_case__					)

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

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







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     List[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  fairseq_model.state_dict()

				lowerCAmelCase__		  =  hf_model.feature_extractor

				# if encoder has different dim to decoder -> use proj_weight
				lowerCAmelCase__		  =  None

				for name, value in fairseq_dict.items():
								lowerCAmelCase__		  =  False
								if "conv_layers" in name:
												load_conv_layer(
												    snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			hf_model.config.feat_extract_norm == 'group'    ,			)
												lowerCAmelCase__		  =  True
								elif name.split('.'					)[0] == "proj":
												lowerCAmelCase__		  =  fairseq_model.proj
												lowerCAmelCase__		  =  True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split('w2v_model.'					)[-1] == name.split('.'					)[0]:
																				lowerCAmelCase__		  =  True
																				if "*" in mapped_key:
																								lowerCAmelCase__		  =  name.split(snake_case__					)[0].split('.'					)[-2]
																								lowerCAmelCase__		  =  mapped_key.replace('*'    ,			snake_case__					)
																				if "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:
																								lowerCAmelCase__		  =  'weight'
																				else:
																								lowerCAmelCase__		  =  None
																				set_recursively(snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)
																continue
								if not is_used:
												unused_weights.append(snake_case__					)

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

				return proj_weight







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     List[Any]:



				"""simple docstring"""





				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:
												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.'
												)
												lowerCAmelCase__		  =  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.'
												)
												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:
												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."
												)
												lowerCAmelCase__		  =  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.'
												)
												lowerCAmelCase__		  =  value
												logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
				else:
								unused_weights.append(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__  ,   lowerCAmelCase__		  =  emb.weight.shape
				lowerCAmelCase__		  =  nn.Linear(snake_case__    ,			snake_case__    ,			bias=snake_case__					)
				lowerCAmelCase__		  =  emb.weight.data
				return lin_layer







def     UpperCAmelCase_		(					snake_case__					)	->     Any:



				"""simple docstring"""





				with open(snake_case__    ,			'r'    ,			encoding='utf-8'					) as f:
								lowerCAmelCase__		  =  f.readlines()
								lowerCAmelCase__		  =  [line.split(' '					)[0] for line in lines]

				lowerCAmelCase__		  =  len(snake_case__					)

				lowerCAmelCase__		  =  {
				    '<s>': 0,
				    '<pad>': 1,
				    '</s>': 2,
				    '<unk>': 3,
				}

				vocab_dict.update(dict(zip(snake_case__    ,			range(4    ,			num_words + 4					)					)					)					)
				return vocab_dict







@torch.no_grad()
def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			)	->     Any:



				"""simple docstring"""





				lowerCAmelCase__		  =  WavaVecaConfig.from_pretrained(snake_case__					)
				lowerCAmelCase__		  =  SpeechaTextaConfig.from_pretrained(
				    snake_case__    ,			vocab_size=snake_case__    ,			decoder_layers=snake_case__    ,			do_stable_layer_norm=snake_case__					)

				lowerCAmelCase__		  =  WavaVecaFeatureExtractor(
				    feature_size=1    ,			sampling_rate=16000    ,			padding_value=0    ,			do_normalize=snake_case__    ,			return_attention_mask=snake_case__    ,			)

				lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]    ,			arg_overrides={'data': '/'.join(dict_path.split('/'					)[:-1]					)}					)
				lowerCAmelCase__		  =  model[0].eval()

				# set weights for wav2vec2 encoder
				lowerCAmelCase__		  =  WavaVecaModel(snake_case__					)
				lowerCAmelCase__		  =  recursively_load_weights_wavaveca(model.encoder    ,			snake_case__					)

				lowerCAmelCase__		  =  SpeechaTextaForCausalLM(snake_case__					)
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()    ,			strict=snake_case__					)

				# set output linear layer
				unexpected_keys.remove('embed_out'					)
				lowerCAmelCase__		  =  nn.Parameter(model.decoder.embed_out.detach()					)

				# layer norm is init to identity matrix so leaving it is fine
				logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}'					)
				logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'					)

				lowerCAmelCase__		  =  SpeechEncoderDecoderModel(encoder=snake_case__    ,			decoder=snake_case__					)
				lowerCAmelCase__		  =  False

				# add projection layer
				lowerCAmelCase__		  =  nn.Parameter(projection_layer.weight					)
				lowerCAmelCase__		  =  nn.Parameter(projection_layer.bias					)

				lowerCAmelCase__		  =  create_vocab_dict(snake_case__					)

				with open(os.path.join(snake_case__    ,			'vocab.json'					)    ,			'w'					) as fp:
								json.dump(snake_case__    ,			snake_case__					)

				lowerCAmelCase__		  =  SpeechaTextaTokenizer(os.path.join(snake_case__    ,			'vocab.json'					)					)
				tokenizer.save_pretrained(snake_case__					)

				lowerCAmelCase__		  =  hf_wavavec.config.to_dict()
				lowerCAmelCase__		  =  tokenizer.pad_token_id
				lowerCAmelCase__		  =  tokenizer.bos_token_id
				lowerCAmelCase__		  =  tokenizer.eos_token_id
				lowerCAmelCase__		  =  'speech_to_text_2'
				lowerCAmelCase__		  =  'wav2vec2'

				lowerCAmelCase__		  =  SpeechEncoderDecoderConfig.from_dict(snake_case__					)

				hf_wavavec.save_pretrained(snake_case__					)
				feature_extractor.save_pretrained(snake_case__					)


if __name__ == "__main__":
	_lowerCAmelCase	:   int    =   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(
	    "--encoder_config_path",
	    default="facebook/wav2vec2-large-lv60",
	    type=str,
	    help="Path to hf encoder wav2vec2 checkpoint config",
	)
	parser.add_argument(
	    "--decoder_config_path",
	    default="facebook/s2t-small-mustc-en-fr-st",
	    type=str,
	    help="Path to hf decoder s2t checkpoint config",
	)
	parser.add_argument("--vocab_size", default=1_0_2_2_4, type=int, help="Vocab size of decoder")
	parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

	_lowerCAmelCase	:   Optional[int]    =   parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.dict_path,
	    encoder_config_path=args.encoder_config_path,
	    decoder_config_path=args.decoder_config_path,
	    vocab_size=args.vocab_size,
	    num_decoder_layers=args.num_decoder_layers,
	)
