

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

A__						=							{
    "post_extract_proj": "feature_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.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}




def 					_lowercase     (				a_				:					str					,a_				:					Dict					,a_				:					Any					,a_				:					Union[str, Any]					,a_				:					Any				)				->	Tuple:




					'''simple docstring'''







					for attribute in key.split('.'				):
										__magic_name__							=    getattr(a_					,a_				)

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

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




def 					_lowercase     (				a_				:					Union[str, Any]					,a_				:					Tuple					,a_				:					Union[str, Any]				)				->	Optional[int]:




					'''simple docstring'''







					__magic_name__							=    []
					__magic_name__							=    fairseq_model.state_dict()

					__magic_name__							=    hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										__magic_name__							=    False
										if "conv_layers" in name:
															load_conv_layer(
															    a_					,a_					,a_					,a_					,hf_model.config.feat_extract_norm == 'group'					,)
															__magic_name__							=    True
										else:
															for key, mapped_key in MAPPING.items():
																				__magic_name__							=    'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

																				if key in name or key.split('w2v_model.'				)[-1] == name.split('.'				)[0]:
																									__magic_name__							=    True
																									if "*" in mapped_key:
																														__magic_name__							=    name.split(a_				)[0].split('.'				)[-2]
																														__magic_name__							=    mapped_key.replace('*'					,a_				)
																									if "weight_g" in name:
																														__magic_name__							=    'weight_g'
																									elif "weight_v" in name:
																														__magic_name__							=    'weight_v'
																									elif "weight" in name:
																														__magic_name__							=    'weight'
																									elif "bias" in name:
																														__magic_name__							=    'bias'
																									else:
																														__magic_name__							=    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 					_lowercase     (				a_				:					int					,a_				:					Any					,a_				:					Optional[int]					,a_				:					List[str]					,a_				:					Dict				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    full_name.split('conv_layers.'				)[-1]
					__magic_name__							=    name.split('.'				)
					__magic_name__							=    int(items[0]				)
					__magic_name__							=    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.'
															)
															__magic_name__							=    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.'
															)
															__magic_name__							=    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."
															)
															__magic_name__							=    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.'
															)
															__magic_name__							=    value
															logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
					else:
										unused_weights.append(a_				)




def 					_lowercase     (				a_				:					List[str]					,a_				:					List[str]				)				->	Union[str, Any]:




					'''simple docstring'''







					__magic_name__							=    SEWConfig()
					if is_finetuned:
										__magic_name__							=    model.wav_encoder.wav_model.cfg
					else:
										__magic_name__							=    model.cfg

					__magic_name__							=    fs_config.conv_bias
					__magic_name__							=    eval(fs_config.conv_feature_layers				)
					__magic_name__							=    [x[0] for x in conv_layers]
					__magic_name__							=    [x[1] for x in conv_layers]
					__magic_name__							=    [x[2] for x in conv_layers]
					__magic_name__							=    'gelu'
					__magic_name__							=    'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
					__magic_name__							=    0.0
					__magic_name__							=    fs_config.activation_fn.name
					__magic_name__							=    fs_config.encoder_embed_dim
					__magic_name__							=    0.02
					__magic_name__							=    fs_config.encoder_ffn_embed_dim
					__magic_name__							=    1e-5
					__magic_name__							=    fs_config.encoder_layerdrop
					__magic_name__							=    fs_config.encoder_attention_heads
					__magic_name__							=    fs_config.conv_pos_groups
					__magic_name__							=    fs_config.conv_pos
					__magic_name__							=    len(a_				)
					__magic_name__							=    fs_config.encoder_layers
					__magic_name__							=    fs_config.squeeze_factor

					# take care of any params that are overridden by the Wav2VecCtc model
					if is_finetuned:
										__magic_name__							=    model.cfg
										__magic_name__							=    fs_config.final_dropout
										__magic_name__							=    fs_config.layerdrop
					__magic_name__							=    fs_config.activation_dropout
					__magic_name__							=    fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
					__magic_name__							=    fs_config.attention_dropout
					__magic_name__							=    fs_config.dropout_input
					__magic_name__							=    fs_config.dropout
					__magic_name__							=    fs_config.mask_channel_length
					__magic_name__							=    fs_config.mask_channel_prob
					__magic_name__							=    fs_config.mask_length
					__magic_name__							=    fs_config.mask_prob

					__magic_name__							=    'Wav2Vec2FeatureExtractor'
					__magic_name__							=    'Wav2Vec2CTCTokenizer'

					return config




@torch.no_grad()
def 					_lowercase     (				a_				:					Optional[Any]					,a_				:					Tuple					,a_				:					List[Any]=None					,a_				:					Optional[int]=None					,a_				:					Optional[int]=True				)				->	str:




					'''simple docstring'''







					if is_finetuned:
										__magic_name__,	__magic_name__,	__magic_name__							=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path]					,arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				)}				)
					else:
										__magic_name__,	__magic_name__,	__magic_name__							=    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

					if config_path is not None:
										__magic_name__							=    SEWConfig.from_pretrained(a_				)
					else:
										__magic_name__							=    convert_config(model[0]					,a_				)
					__magic_name__							=    model[0].eval()

					__magic_name__							=    True if config.feat_extract_norm == 'layer' else False
					__magic_name__							=    WavaVecaFeatureExtractor(
					    feature_size=1					,sampling_rate=1_6_0_0_0					,padding_value=0					,do_normalize=a_					,return_attention_mask=a_					,)

					if is_finetuned:
										if dict_path:
															__magic_name__							=    Dictionary.load(a_				)

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															__magic_name__							=    target_dict.pad_index
															__magic_name__							=    target_dict.bos_index
															__magic_name__							=    target_dict.pad_index
															__magic_name__							=    target_dict.bos_index
															__magic_name__							=    target_dict.eos_index
															__magic_name__							=    len(target_dict.symbols				)
															__magic_name__							=    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_				)
															with open(a_					,'w'					,encoding='utf-8'				) as vocab_handle:
																				json.dump(target_dict.indices					,a_				)
															__magic_name__							=    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_					,)
															__magic_name__							=    WavaVecaProcessor(feature_extractor=a_					,tokenizer=a_				)
															processor.save_pretrained(a_				)

										__magic_name__							=    SEWForCTC(a_				)
					else:
										__magic_name__							=    SEWModel(a_				)
										feature_extractor.save_pretrained(a_				)

					recursively_load_weights(a_					,a_					,a_				)

					hf_model.save_pretrained(a_				)


if __name__ == "__main__":
						A__						=							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(
						    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
						)
						A__						=							parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase				(					unittest.TestCase			):







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  , return_dict=__UpperCamelCase      ).to(__UpperCamelCase      )
						__magic_name__							=    AutoTokenizer.from_pretrained('google/mt5-small'      )

						__magic_name__							=    tokenizer('Hello there'  , return_tensors='pt'      ).input_ids
						__magic_name__							=    tokenizer('Hi I am'  , return_tensors='pt'      ).input_ids

						__magic_name__							=    model(input_ids.to(__UpperCamelCase      )  , labels=labels.to(__UpperCamelCase      )      ).loss
						__magic_name__							=    -(labels.shape[-1] * loss.item())

						__magic_name__							=    -84.9127
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1E-4      )




