
import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
lowerCAmelCase__:    Tuple      = logging.get_logger("transformers.models.speecht5")

lowerCAmelCase__:    Tuple      = {
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
lowerCAmelCase__:    Tuple      = {
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
lowerCAmelCase__:    Optional[int]      = {
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
lowerCAmelCase__:    Optional[int]      = {
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
lowerCAmelCase__:    Any      = {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
lowerCAmelCase__:    Dict      = {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
lowerCAmelCase__:    List[str]      = {
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
lowerCAmelCase__:    Dict      = {
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
lowerCAmelCase__:    str      = {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowerCAmelCase__:    int      = {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase__:    Dict      = {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase__:    List[Any]      = []
lowerCAmelCase__:    str      = [
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
lowerCAmelCase__:    int      = IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
lowerCAmelCase__:    Dict      = IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
lowerCAmelCase__:    int      = IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> Union[str, Any]:






		for attribute in key.split('.' ):
				SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			getattr(UpperCamelCase__ ,    UpperCamelCase__ )

		if weight_type is not None:
				SCREAMING_SNAKE_CASE_					:	Dict		  =			getattr(UpperCamelCase__ ,    UpperCamelCase__ ).shape
		else:
				SCREAMING_SNAKE_CASE_					:	str		  =			hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    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":
				SCREAMING_SNAKE_CASE_					:	int		  =			value
		elif weight_type == "weight_g":
				SCREAMING_SNAKE_CASE_					:	List[Any]		  =			value
		elif weight_type == "weight_v":
				SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			value
		elif weight_type == "bias":
				SCREAMING_SNAKE_CASE_					:	List[Any]		  =			value
		elif weight_type == "running_mean":
				SCREAMING_SNAKE_CASE_					:	str		  =			value
		elif weight_type == "running_var":
				SCREAMING_SNAKE_CASE_					:	str		  =			value
		elif weight_type == "num_batches_tracked":
				SCREAMING_SNAKE_CASE_					:	Dict		  =			value
		else:
				SCREAMING_SNAKE_CASE_					:	Any		  =			value

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



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> Any:






		for key in ignore_keys:
				if key.endswith('.*' ):
						if name.startswith(key[:-1] ):
								return True
				elif ".*." in key:
						SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	Tuple		  =			key.split('.*.' )
						if prefix in name and suffix in name:
								return True
				elif key in name:
						return True
		return False



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> Any:
		SCREAMING_SNAKE_CASE_					:	Any		  =			[]

		if task == "s2t":
				SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			hf_model.speechta.encoder.prenet.feature_encoder
				SCREAMING_SNAKE_CASE_					:	Dict		  =			MAPPING_S2T
				SCREAMING_SNAKE_CASE_					:	int		  =			IGNORE_KEYS_S2T
		elif task == "t2s":
				SCREAMING_SNAKE_CASE_					:	List[Any]		  =			None
				SCREAMING_SNAKE_CASE_					:	List[Any]		  =			MAPPING_T2S
				SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			IGNORE_KEYS_T2S
		elif task == "s2s":
				SCREAMING_SNAKE_CASE_					:	int		  =			hf_model.speechta.encoder.prenet.feature_encoder
				SCREAMING_SNAKE_CASE_					:	Tuple		  =			MAPPING_S2S
				SCREAMING_SNAKE_CASE_					:	str		  =			IGNORE_KEYS_S2S
		else:
				raise ValueError(f'Unsupported task: {task}' )

		for name, value in fairseq_dict.items():
				if should_ignore(UpperCamelCase__ ,    UpperCamelCase__ ):
						logger.info(f'{name} was ignored' )
						continue

				SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			False
				if "conv_layers" in name:
						load_conv_layer(
						    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    hf_model.config.feat_extract_norm == 'group' ,    )
						SCREAMING_SNAKE_CASE_					:	List[Any]		  =			True
				else:
						for key, mapped_key in MAPPING.items():
								# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

								if "*" in key:
										SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	Dict		  =			key.split('.*.' )
										if prefix in name and suffix in name:
												SCREAMING_SNAKE_CASE_					:	Tuple		  =			suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
								if key in name:
										SCREAMING_SNAKE_CASE_					:	int		  =			True
										if "*" in mapped_key:
												SCREAMING_SNAKE_CASE_					:	Tuple		  =			name.split(UpperCamelCase__ )[0].split('.' )[-2]
												SCREAMING_SNAKE_CASE_					:	List[Any]		  =			mapped_key.replace('*' ,    UpperCamelCase__ )
										if "weight_g" in name:
												SCREAMING_SNAKE_CASE_					:	Dict		  =			'weight_g'
										elif "weight_v" in name:
												SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			'weight_v'
										elif "bias" in name:
												SCREAMING_SNAKE_CASE_					:	List[str]		  =			'bias'
										elif "weight" in name:
												SCREAMING_SNAKE_CASE_					:	Dict		  =			'weight'
										elif "running_mean" in name:
												SCREAMING_SNAKE_CASE_					:	Tuple		  =			'running_mean'
										elif "running_var" in name:
												SCREAMING_SNAKE_CASE_					:	str		  =			'running_var'
										elif "num_batches_tracked" in name:
												SCREAMING_SNAKE_CASE_					:	Tuple		  =			'num_batches_tracked'
										else:
												SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			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 (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> Dict:
		SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			full_name.split('conv_layers.' )[-1]
		SCREAMING_SNAKE_CASE_					:	str		  =			name.split('.' )
		SCREAMING_SNAKE_CASE_					:	int		  =			int(items[0] )
		SCREAMING_SNAKE_CASE_					:	List[Any]		  =			int(items[1] )

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    f'{full_name} has size {value.shape}, but'
								    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
						SCREAMING_SNAKE_CASE_					:	Tuple		  =			value
						logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    f'{full_name} has size {value.shape}, but'
								    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
						SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			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:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    f'{full_name} has size {value.shape}, but'
								    f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' )
						SCREAMING_SNAKE_CASE_					:	Tuple		  =			value
						logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    f'{full_name} has size {value.shape}, but'
								    f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' )
						SCREAMING_SNAKE_CASE_					:	Tuple		  =			value
						logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
		else:
				unused_weights.append(UpperCamelCase__ )



@torch.no_grad()
def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE=None ,    SCREAMING_SNAKE_CASE=None ,    SCREAMING_SNAKE_CASE=None ,    )		-> Optional[Any]:






		if config_path is not None:
				SCREAMING_SNAKE_CASE_					:	Tuple		  =			SpeechTaConfig.from_pretrained(UpperCamelCase__ )
		else:
				SCREAMING_SNAKE_CASE_					:	int		  =			SpeechTaConfig()

		if task == "s2t":
				SCREAMING_SNAKE_CASE_					:	List[str]		  =			config.max_text_positions
				SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			SpeechTaForSpeechToText(UpperCamelCase__ )
		elif task == "t2s":
				SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			1876
				SCREAMING_SNAKE_CASE_					:	Any		  =			600
				SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			config.max_speech_positions
				SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			SpeechTaForTextToSpeech(UpperCamelCase__ )
		elif task == "s2s":
				SCREAMING_SNAKE_CASE_					:	str		  =			1876
				SCREAMING_SNAKE_CASE_					:	Any		  =			config.max_speech_positions
				SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			SpeechTaForSpeechToSpeech(UpperCamelCase__ )
		else:
				raise ValueError(f'Unknown task name: {task}' )

		if vocab_path:
				SCREAMING_SNAKE_CASE_					:	Dict		  =			SpeechTaTokenizer(UpperCamelCase__ ,    model_max_length=config.max_text_positions )

				# Mask token behaves like a normal word, i.e. include the space before it
				SCREAMING_SNAKE_CASE_					:	str		  =			AddedToken('<mask>' ,    lstrip=UpperCamelCase__ ,    rstrip=UpperCamelCase__ )
				SCREAMING_SNAKE_CASE_					:	Any		  =			mask_token
				tokenizer.add_special_tokens({'mask_token': mask_token} )
				tokenizer.add_tokens(['<ctc_blank>'] )

		SCREAMING_SNAKE_CASE_					:	List[Any]		  =			SpeechTaFeatureExtractor()
		SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			SpeechTaProcessor(tokenizer=UpperCamelCase__ ,    feature_extractor=UpperCamelCase__ )
		processor.save_pretrained(UpperCamelCase__ )

		SCREAMING_SNAKE_CASE_					:	Dict		  =			torch.load(UpperCamelCase__ )
		recursively_load_weights(fairseq_checkpoint['model'] ,    UpperCamelCase__ ,    UpperCamelCase__ )

		model.save_pretrained(UpperCamelCase__ )

		if repo_id:
				print('Pushing to the hub...' )
				processor.push_to_hub(UpperCamelCase__ )
				model.push_to_hub(UpperCamelCase__ )


if __name__ == "__main__":
		lowerCAmelCase__:    str      = argparse.ArgumentParser()
		parser.add_argument(
		    "--task",
		    default="s2t",
		    type=str,
		    help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
		)
		parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
		parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
		parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
		parser.add_argument(
		    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
		)

		lowerCAmelCase__:    Optional[Any]      = parser.parse_args()
		convert_speechta_checkpoint(
		    args.task,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.vocab_path,
		    args.push_to_hub,
		)









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_UpperCAmelCase						:  Union[str, Any]									=			logging.get_logger(__name__)

_UpperCAmelCase						:  Dict									=			{
    """microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}







class 					lowercase							(			lowercase_      ,    lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     '''resnet'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     ['''basic''', '''bottleneck''']




				def __init__(   self ,  snake_case=3 ,  snake_case=64 ,  snake_case=[256, 512, 1024, 2048] ,  snake_case=[3, 4, 6, 3] ,  snake_case="bottleneck" ,  snake_case="relu" ,  snake_case=False ,  snake_case=None ,  snake_case=None ,  **snake_case ,  ):
											super().__init__(**snake_case			)
											if layer_type not in self.layer_types:
																		raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types			)}'''			)
											snake_case_     							=       num_channels
											snake_case_     							=       embedding_size
											snake_case_     							=       hidden_sizes
											snake_case_     							=       depths
											snake_case_     							=       layer_type
											snake_case_     							=       hidden_act
											snake_case_     							=       downsample_in_first_stage
											snake_case_     							=       ['stem'] + [F'''stage{idx}''' for idx in range(1 ,  len(snake_case			) + 1			)]
											snake_case_							,       snake_case_     							=       get_aligned_output_features_output_indices(
											    out_features=snake_case ,  out_indices=snake_case ,  stage_names=self.stage_names			)







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     version.parse('''1.11'''     )




				@property
				def        a				(   self			):
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]			)




				@property
				def        a				(   self			):
											return 1e-3




