


"""simple docstring"""
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()
__SCREAMING_SNAKE_CASE							:    Any		=    logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:    str		=    {
    '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				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Tuple:
							for attribute in key.split("""."""				):
														snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

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

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







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				List[str]:
							snake_case_           =		[]
							snake_case_           =		fairseq_model.state_dict()

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

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

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







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:
							snake_case_           =		full_name.split("""conv_layers."""				)[-1]
							snake_case_           =		name.split("""."""				)
							snake_case_           =		int(items[0]				)
							snake_case_           =		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."""
																					)
																					snake_case_           =		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."""
																					)
																					snake_case_           =		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."
																					)
																					snake_case_           =		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."""
																					)
																					snake_case_           =		value
																					logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
							else:
														unused_weights.append(_SCREAMING_SNAKE_CASE				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:
							snake_case_           =		SEWConfig()
							if is_finetuned:
														snake_case_           =		model.wav_encoder.wav_model.cfg
							else:
														snake_case_           =		model.cfg

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

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

							snake_case_           =		"""Wav2Vec2FeatureExtractor"""
							snake_case_           =		"""Wav2Vec2CTCTokenizer"""

							return config







@torch.no_grad()
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE=True				)    ->				List[Any]:

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

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

							snake_case_           =		True if config.feat_extract_norm == """layer""" else False
							snake_case_           =		WavaVecaFeatureExtractor(
							    feature_size=1				, sampling_rate=16_000				, padding_value=0				, do_normalize=_SCREAMING_SNAKE_CASE				, return_attention_mask=_SCREAMING_SNAKE_CASE				, )

							if is_finetuned:
														if dict_path:
																					snake_case_           =		Dictionary.load(_SCREAMING_SNAKE_CASE				)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					snake_case_           =		target_dict.pad_index
																					snake_case_           =		target_dict.bos_index
																					snake_case_           =		target_dict.pad_index
																					snake_case_           =		target_dict.bos_index
																					snake_case_           =		target_dict.eos_index
																					snake_case_           =		len(target_dict.symbols				)
																					snake_case_           =		os.path.join(_SCREAMING_SNAKE_CASE				, """vocab.json"""				)
																					if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
																												logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_SCREAMING_SNAKE_CASE				)				)
																												return
																					os.makedirs(_SCREAMING_SNAKE_CASE				, exist_ok=_SCREAMING_SNAKE_CASE				)
																					with open(_SCREAMING_SNAKE_CASE				, """w"""				, encoding="""utf-8"""				) as vocab_handle:
																												json.dump(target_dict.indices				, _SCREAMING_SNAKE_CASE				)
																					snake_case_           =		WavaVecaCTCTokenizer(
																					    _SCREAMING_SNAKE_CASE				, 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=_SCREAMING_SNAKE_CASE				, )
																					snake_case_           =		WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE				, tokenizer=_SCREAMING_SNAKE_CASE				)
																					processor.save_pretrained(_SCREAMING_SNAKE_CASE				)

														snake_case_           =		SEWForCTC(_SCREAMING_SNAKE_CASE				)
							else:
														snake_case_           =		SEWModel(_SCREAMING_SNAKE_CASE				)
														feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE				)

							recursively_load_weights(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

							hf_model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    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('--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'
				)
				__SCREAMING_SNAKE_CASE							:    Any		=    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 torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()





class      __A     (snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		str							      =    VQModel
							__lowercase:		Union[str, Any]							      =    """sample"""



							@property
							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      List[str]=(32, 32)       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		4
														snake_case_           =		3

														snake_case_           =		floats_tensor((batch_size, num_channels) + sizes       ).to(UpperCAmelCase_       )

														return {"sample": image}



							@property
							def 		lowerCAmelCase       (				self							:      Tuple       )					->str:



														"""simple docstring"""



														return (3, 32, 32)



							@property
							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Any:



														"""simple docstring"""



														return (3, 32, 32)



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		{
														    """block_out_channels""": [32, 64],
														    """in_channels""": 3,
														    """out_channels""": 3,
														    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
														    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
														    """latent_channels""": 3,
														}
														snake_case_           =		self.dummy_input
														return init_dict, inputs_dict



							def 		lowerCAmelCase       (				self							:      List[str]       )					->Dict:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[str]:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Any       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		VQModel.from_pretrained("""fusing/vqgan-dummy"""		,       output_loading_info=UpperCAmelCase_       )
														self.assertIsNotNone(UpperCAmelCase_       )
														self.assertEqual(len(loading_info["""missing_keys"""]       )		,       0       )

														model.to(UpperCAmelCase_       )
														snake_case_           =		model(**self.dummy_input       )

														assert image is not None, "Make sure output is not None"



							def 		lowerCAmelCase       (				self							:      Tuple       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		VQModel.from_pretrained("""fusing/vqgan-dummy"""       )
														model.to(UpperCAmelCase_       ).eval()

														torch.manual_seed(0       )
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0       )

														snake_case_           =		torch.randn(1		,       model.config.in_channels		,       model.config.sample_size		,       model.config.sample_size       )
														snake_case_           =		image.to(UpperCAmelCase_       )
														with torch.no_grad():
																					snake_case_           =		model(UpperCAmelCase_       ).sample

														snake_case_           =		output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														snake_case_           =		torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143]       )
														# fmt: on
														self.assertTrue(torch.allclose(UpperCAmelCase_		,       UpperCAmelCase_		,       atol=1E-3       )       )



