




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()
lowercase_	:				str  				= logging.get_logger(__name__)

lowercase_	:				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',
}
lowercase_	:				int  				= [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]


def 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_						):

						'''simple docstring'''


						for attribute in key.split("."						):
												_UpperCAmelCase													=     getattr(snake_case_	,    snake_case_						)

						if weight_type is not None:
												_UpperCAmelCase													=     getattr(snake_case_	,    snake_case_						).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 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=     []
						_UpperCAmelCase													=     fairseq_model.state_dict()

						_UpperCAmelCase													=     hf_model.feature_extractor

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

						for name, value in fairseq_dict.items():
												_UpperCAmelCase													=     False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    hf_model.config.feat_extract_norm == "group"	,    )
																		_UpperCAmelCase													=     True
												elif name.split("."						)[0] == "proj":
																		_UpperCAmelCase													=     fairseq_model.proj
																		_UpperCAmelCase													=     True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split("w2v_model."						)[-1] == name.split("."						)[0]:
																														_UpperCAmelCase													=     True
																														if "*" in mapped_key:
																																				_UpperCAmelCase													=     name.split(snake_case_						)[0].split("."						)[-2]
																																				_UpperCAmelCase													=     mapped_key.replace("*"	,    snake_case_						)
																														if "weight_g" in name:
																																				_UpperCAmelCase													=     "weight_g"
																														elif "weight_v" in name:
																																				_UpperCAmelCase													=     "weight_v"
																														elif "bias" in name:
																																				_UpperCAmelCase													=     "bias"
																														elif "weight" in name:
																																				_UpperCAmelCase													=     "weight"
																														else:
																																				_UpperCAmelCase													=     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 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_						):

						'''simple docstring'''


						_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(snake_case_						)


def 							__SCREAMING_SNAKE_CASE  (			snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase							, _UpperCAmelCase													=     emb.weight.shape
						_UpperCAmelCase													=     nn.Linear(snake_case_	,    snake_case_	,    bias=snake_case_						)
						_UpperCAmelCase													=     emb.weight.data
						return lin_layer


def 							__SCREAMING_SNAKE_CASE  (			snake_case_						):

						'''simple docstring'''


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

						_UpperCAmelCase													=     len(snake_case_						)

						_UpperCAmelCase													=     {
						    "<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 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    ):

						'''simple docstring'''


						_UpperCAmelCase													=     WavaVecaConfig.from_pretrained(snake_case_						)
						_UpperCAmelCase													=     SpeechaTextaConfig.from_pretrained(
						    snake_case_	,    vocab_size=snake_case_	,    decoder_layers=snake_case_	,    do_stable_layer_norm=snake_case_						)

						_UpperCAmelCase													=     WavaVecaFeatureExtractor(
						    feature_size=1	,    sampling_rate=1_6000	,    padding_value=0	,    do_normalize=snake_case_	,    return_attention_mask=snake_case_	,    )

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

						# set weights for wav2vec2 encoder
						_UpperCAmelCase													=     WavaVecaModel(snake_case_						)
						_UpperCAmelCase													=     recursively_load_weights_wavaveca(model.encoder	,    snake_case_						)

						_UpperCAmelCase													=     SpeechaTextaForCausalLM(snake_case_						)
						_UpperCAmelCase							, _UpperCAmelCase													=     hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()	,    strict=snake_case_						)

						# set output linear layer
						unexpected_keys.remove("embed_out"						)
						_UpperCAmelCase													=     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}"""						)

						_UpperCAmelCase													=     SpeechEncoderDecoderModel(encoder=snake_case_	,    decoder=snake_case_						)
						_UpperCAmelCase													=     False

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

						_UpperCAmelCase													=     create_vocab_dict(snake_case_						)

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

						_UpperCAmelCase													=     SpeechaTextaTokenizer(os.path.join(snake_case_	,    "vocab.json"						)						)
						tokenizer.save_pretrained(snake_case_						)

						_UpperCAmelCase													=     hf_wavavec.config.to_dict()
						_UpperCAmelCase													=     tokenizer.pad_token_id
						_UpperCAmelCase													=     tokenizer.bos_token_id
						_UpperCAmelCase													=     tokenizer.eos_token_id
						_UpperCAmelCase													=     "speech_to_text_2"
						_UpperCAmelCase													=     "wav2vec2"

						_UpperCAmelCase													=     SpeechEncoderDecoderConfig.from_dict(snake_case_						)

						hf_wavavec.save_pretrained(snake_case_						)
						feature_extractor.save_pretrained(snake_case_						)


if __name__ == "__main__":
				lowercase_	:				Optional[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(
				    '--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_02_24, type=int, help='Vocab size of decoder')
				parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

				lowercase_	:				List[str]  				= 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,
				)











import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


snake_case__					:							Union[str, Any]													=     500000

snake_case__						,    snake_case__					:							Optional[Any]													=     os.path.split(__file__)
snake_case__					:							List[Any]													=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.map(**lowerCamelCase )



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.filter(**lowerCamelCase )



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				{'''num examples''': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            __A          =				datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
            __A          =				generate_example_dataset(
                os.path.join(lowerCamelCase					,				'''dataset.arrow''' )					,				lowerCamelCase					,				num_examples=lowerCamelCase )

            __A          =				transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''					,				use_fast=lowerCamelCase )

            def tokenize(lowerCamelCase:   List[str] ):
                  return tokenizer(examples['''text'''] )

            __A          =				map(lowerCamelCase )

            __A          =				map(lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''numpy''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''pandas''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''torch'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''tensorflow'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				filter(lowerCamelCase )

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

      with open(lowerCamelCase					,				'''wb''' ) as f:
            f.write(json.dumps(lowerCamelCase ).encode('''utf-8''' ) )


if __name__ == "__main__":  # useful to run the profiler
       benchmark_map_filter()





