




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()
UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    '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 				_A  (		lowerCAmelCase_	:   List[Any]  ,							lowerCAmelCase_	:   Dict  ,							lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   str     ):
     """simple docstring"""

     for attribute in key.split("."     ):
          lowerCAmelCase__            =					getattr(lowerCAmelCase_  ,							lowerCAmelCase_     )

     if weight_type is not None:
          lowerCAmelCase__            =					getattr(lowerCAmelCase_  ,							lowerCAmelCase_     ).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 				_A  (		lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   Any  ,							lowerCAmelCase_	:   Union[str, Any]     ):
     """simple docstring"""

     lowerCAmelCase__            =					[]
     lowerCAmelCase__            =					fairseq_model.state_dict()

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

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

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



def 				_A  (		lowerCAmelCase_	:   Any  ,							lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   List[Any]  ,							lowerCAmelCase_	:   Optional[int]  ,							lowerCAmelCase_	:   str     ):
     """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(lowerCAmelCase_     )



def 				_A  (		lowerCAmelCase_	:   Optional[int]  ,							lowerCAmelCase_	:   Tuple     ):
     """simple docstring"""

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

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

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

     lowerCAmelCase__            =					"Wav2Vec2FeatureExtractor"
     lowerCAmelCase__            =					"Wav2Vec2CTCTokenizer"

     return config



@torch.no_grad()
def 				_A  (		lowerCAmelCase_	:   List[Any]  ,							lowerCAmelCase_	:   List[Any]  ,							lowerCAmelCase_	:   Union[str, Any]=None  ,							lowerCAmelCase_	:   Dict=None  ,							lowerCAmelCase_	:   Optional[int]=True     ):
     """simple docstring"""

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

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

     lowerCAmelCase__            =					True if config.feat_extract_norm == "layer" else False
     lowerCAmelCase__            =					WavaVecaFeatureExtractor(
         feature_size=1  ,							sampling_rate=1_6000  ,							padding_value=0  ,							do_normalize=lowerCAmelCase_  ,							return_attention_mask=lowerCAmelCase_  ,							)

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

               # 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.pad_index
               lowerCAmelCase__            =					target_dict.bos_index
               lowerCAmelCase__            =					target_dict.eos_index
               lowerCAmelCase__            =					len(target_dict.symbols     )
               lowerCAmelCase__            =					os.path.join(lowerCAmelCase_  ,							"vocab.json"     )
               if not os.path.isdir(lowerCAmelCase_     ):
                    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowerCAmelCase_     )     )
                    return
               os.makedirs(lowerCAmelCase_  ,							exist_ok=lowerCAmelCase_     )
               with open(lowerCAmelCase_  ,							"w"  ,							encoding="utf-8"     ) as vocab_handle:
                    json.dump(target_dict.indices  ,							lowerCAmelCase_     )
               lowerCAmelCase__            =					WavaVecaCTCTokenizer(
                   lowerCAmelCase_  ,							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=lowerCAmelCase_  ,							)
               lowerCAmelCase__            =					WavaVecaProcessor(feature_extractor=lowerCAmelCase_  ,							tokenizer=lowerCAmelCase_     )
               processor.save_pretrained(lowerCAmelCase_     )

          lowerCAmelCase__            =					SEWForCTC(lowerCAmelCase_     )
     else:
          lowerCAmelCase__            =					SEWModel(lowerCAmelCase_     )
          feature_extractor.save_pretrained(lowerCAmelCase_     )

     recursively_load_weights(lowerCAmelCase_  ,							lowerCAmelCase_  ,							lowerCAmelCase_     )

     hf_model.save_pretrained(lowerCAmelCase_     )


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




'''simple docstring'''
import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.distilbert.modeling_flax_distilbert import (
					    FlaxDistilBertForMaskedLM,
					    FlaxDistilBertForMultipleChoice,
					    FlaxDistilBertForQuestionAnswering,
					    FlaxDistilBertForSequenceClassification,
					    FlaxDistilBertForTokenClassification,
					    FlaxDistilBertModel,
					)




class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_3							,      a_=7							,      a_=True							,      a_=True							,      a_=True							,      a_=True							,      a_=9_9							,      a_=3_2							,      a_=5							,      a_=4							,      a_=3_7							,      a_="gelu"							,      a_=0.1							,      a_=0.1							,      a_=5_1_2							,      a_=1_6							,      a_=2							,      a_=0.02							,      a_=4							,      )    ->  Tuple:
					lowercase						:		Optional[Any]   					=		parent
					lowercase						:		int   					=		batch_size
					lowercase						:		int   					=		seq_length
					lowercase						:		List[str]   					=		is_training
					lowercase						:		str   					=		use_attention_mask
					lowercase						:		List[str]   					=		use_token_type_ids
					lowercase						:		Optional[Any]   					=		use_labels
					lowercase						:		Dict   					=		vocab_size
					lowercase						:		Union[str, Any]   					=		hidden_size
					lowercase						:		int   					=		num_hidden_layers
					lowercase						:		Optional[Any]   					=		num_attention_heads
					lowercase						:		Optional[Any]   					=		intermediate_size
					lowercase						:		int   					=		hidden_act
					lowercase						:		Dict   					=		hidden_dropout_prob
					lowercase						:		Optional[Any]   					=		attention_probs_dropout_prob
					lowercase						:		Dict   					=		max_position_embeddings
					lowercase						:		Tuple   					=		type_vocab_size
					lowercase						:		int   					=		type_sequence_label_size
					lowercase						:		Optional[Any]   					=		initializer_range
					lowercase						:		Union[str, Any]   					=		num_choices

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		List[Any]   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size							)

					lowercase						:		Any   					=		None
					if self.use_attention_mask:
						lowercase						:		str   					=		random_attention_mask([self.batch_size, self.seq_length]							)

					lowercase						:		Optional[Any]   					=		DistilBertConfig(
					    vocab_size=self.vocab_size							,      dim=self.hidden_size							,      n_layers=self.num_hidden_layers							,      n_heads=self.num_attention_heads							,      hidden_dim=self.intermediate_size							,      hidden_act=self.hidden_act							,      dropout=self.hidden_dropout_prob							,      attention_dropout=self.attention_probs_dropout_prob							,      max_position_embeddings=self.max_position_embeddings							,      initializer_range=self.initializer_range							,      tie_weights_=a_							,      )

					return config, input_ids, attention_mask


				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Dict   					=		self.prepare_config_and_inputs()
					lowercase					,		lowercase					,		lowercase						:		Dict   					=		config_and_inputs
					lowercase						:		Any   					=		{"input_ids": input_ids, "attention_mask": attention_mask}
					return config, inputs_dict




@require_flax
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(
				    (
				        FlaxDistilBertModel,
				        FlaxDistilBertForMaskedLM,
				        FlaxDistilBertForMultipleChoice,
				        FlaxDistilBertForQuestionAnswering,
				        FlaxDistilBertForSequenceClassification,
				        FlaxDistilBertForTokenClassification,
				        FlaxDistilBertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Optional[int]   					=		FlaxDistilBertModelTester(self							)


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					for model_class_name in self.all_model_classes:
						lowercase						:		int   					=		model_class_name.from_pretrained("distilbert-base-uncased"							)
						lowercase						:		Tuple   					=		model(np.ones((1, 1)							)							)
						self.assertIsNotNone(a_							)




@require_flax
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		str   					=		FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"							)
					lowercase						:		int   					=		np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
					lowercase						:		int   					=		np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
					lowercase						:		List[str]   					=		model(a_							,      attention_mask=a_							)[0]
					lowercase						:		int   					=		(1, 1_1, 7_6_8)
					self.assertEqual(output.shape							,      a_							)
					lowercase						:		List[str]   					=		np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]							)

					self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]							,      a_							,      atol=1e-4							)							)


