





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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_      :			Optional[int]		  =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      :			Any		  =      {
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''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    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						List[str]:
		for attribute in key.split('.'       ):
				__lowercase					=    getattr(UpperCamelCase__	,      UpperCamelCase__       )

		if weight_type is not None:
				__lowercase					=    getattr(UpperCamelCase__	,      UpperCamelCase__       ).shape
		else:
				__lowercase					=    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":
				__lowercase					=    value
		elif weight_type == "weight_g":
				__lowercase					=    value
		elif weight_type == "weight_v":
				__lowercase					=    value
		elif weight_type == "bias":
				__lowercase					=    value
		else:
				__lowercase					=    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	,      snake_case       )		->						Dict:
		__lowercase					=    []
		__lowercase					=    fairseq_model.state_dict()

		__lowercase					=    hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						Any:
		__lowercase					=    full_name.split('conv_layers.'       )[-1]
		__lowercase					=    name.split('.'       )
		__lowercase					=    int(items[0]       )
		__lowercase					=    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."
						)
						__lowercase					=    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."
						)
						__lowercase					=    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."
						)
						__lowercase					=    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."
						)
						__lowercase					=    value
						logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
		else:
				unused_weights.append(UpperCamelCase__       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Union[str, Any]:
		__lowercase					=    SEWDConfig()
		if is_finetuned:
				__lowercase					=    model.wav_encoder.wav_model.cfg
		else:
				__lowercase					=    model.cfg

		__lowercase					=    fs_config.conv_bias
		__lowercase					=    eval(fs_config.conv_feature_layers       )
		__lowercase					=    [x[0] for x in conv_layers]
		__lowercase					=    [x[1] for x in conv_layers]
		__lowercase					=    [x[2] for x in conv_layers]
		__lowercase					=    "gelu"
		__lowercase					=    "layer" if fs_config.extractor_mode == "layer_norm" else "group"
		__lowercase					=    0.0
		__lowercase					=    fs_config.activation_fn.name
		__lowercase					=    fs_config.encoder_embed_dim
		__lowercase					=    0.02
		__lowercase					=    fs_config.encoder_ffn_embed_dim
		__lowercase					=    1E-5
		__lowercase					=    fs_config.encoder_layerdrop
		__lowercase					=    fs_config.encoder_attention_heads
		__lowercase					=    fs_config.conv_pos_groups
		__lowercase					=    fs_config.conv_pos
		__lowercase					=    len(UpperCamelCase__       )
		__lowercase					=    fs_config.encoder_layers
		__lowercase					=    fs_config.squeeze_factor
		# DeBERTa-specific parameters:
		__lowercase					=    fs_config.max_position_embeddings
		__lowercase					=    fs_config.position_buckets
		__lowercase					=    fs_config.share_att_key
		__lowercase					=    fs_config.relative_attention
		__lowercase					=    fs_config.position_biased_input
		__lowercase					=    tuple(fs_config.pos_att_type.split('|'       )       )
		__lowercase					=    fs_config.norm_rel_ebd

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

		__lowercase					=    "Wav2Vec2FeatureExtractor"
		__lowercase					=    "Wav2Vec2CTCTokenizer"

		return config



@torch.no_grad()
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case=None	,      snake_case=None	,      snake_case=True       )		->						Any:

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

		if config_path is not None:
				__lowercase					=    SEWDConfig.from_pretrained(UpperCamelCase__       )
		else:
				__lowercase					=    convert_config(model[0]	,      UpperCamelCase__       )
		__lowercase					=    model[0].eval()

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

		if is_finetuned:
				if dict_path:
						__lowercase					=    Dictionary.load(UpperCamelCase__       )

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

				__lowercase					=    SEWDForCTC(UpperCamelCase__       )
		else:
				__lowercase					=    SEWDModel(UpperCamelCase__       )
				feature_extractor.save_pretrained(UpperCamelCase__       )

		recursively_load_weights(UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__       )

		hf_model.save_pretrained(UpperCamelCase__       )


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_      :			Union[str, 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('''--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_      :			Dict		  =      parser.parse_args()
		convert_sew_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
		)












import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		(VQDiffusionScheduler,)




	def       UpperCAmelCase (	self       :    int				,			**__lowerCamelCase       :    List[Any]      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    {
			    'num_vec_classes': 4_097,
			    'num_train_timesteps': 100,
			}

			config.update(**__lowerCamelCase      )
			return config




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    8
			__lowercase					=    8

			__lowercase					=    torch.randint(0				,			__lowerCamelCase				,			(batch_size, height * width)      )

			return sample




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

			'''simple docstring'''




			assert False




	def       UpperCAmelCase (	self       :    Union[str, Any]				,			__lowerCamelCase       :    str      )					->   Dict:

			'''simple docstring'''




			def model(__lowerCamelCase       :    Dict				,			__lowerCamelCase       :    Optional[Any]				,			*__lowerCamelCase       :    List[Any]      ):
					__lowercase	,			__lowercase					=    sample.shape
					__lowercase					=    torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)      )
					__lowercase					=    F.log_softmax(logits.double()				,			dim=1      ).float()
					return return_value

			return model




	def       UpperCAmelCase (	self       :    Dict      )					->   List[Any]:

			'''simple docstring'''




			for timesteps in [2, 5, 100, 1_000]:
					self.check_over_configs(num_train_timesteps=__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   int:

			'''simple docstring'''




			for num_vec_classes in [5, 100, 1_000, 4_000]:
					self.check_over_configs(num_vec_classes=__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Any      )					->   List[Any]:

			'''simple docstring'''




			for t in [0, 50, 99]:
					self.check_over_forward(time_step=__lowerCamelCase      )






	def       UpperCAmelCase (	self       :    str      )					->   Optional[int]:

			'''simple docstring'''




			pass




