
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 		_UpperCAmelCase   (	UpperCamelCase:     List[Any]			,							UpperCamelCase:     int			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     List[Any]  ):






	"""simple docstring"""


	for attribute in key.split("."  ):
		__lowerCAmelCase         =							getattr(UpperCamelCase			,							UpperCamelCase  )

	if weight_type is not None:
		__lowerCAmelCase         =							getattr(UpperCamelCase			,							UpperCamelCase  ).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 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     Dict			,							UpperCamelCase:     int  ):






	"""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(
			    UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							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(UpperCamelCase  )[0].split("."  )[-2]
						__lowerCAmelCase         =							mapped_key.replace("*"			,							UpperCamelCase  )
					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(UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase  )
				continue
		if not is_used:
			unused_weights.append(UpperCamelCase  )

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






def 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     str			,							UpperCamelCase:     Dict			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     List[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(UpperCamelCase  )






def 		_UpperCAmelCase   (	UpperCamelCase:     Tuple			,							UpperCamelCase:     Optional[Any]  ):






	"""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(UpperCamelCase  )
	__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 		_UpperCAmelCase   (	UpperCamelCase:     List[Any]			,							UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Any=None			,							UpperCamelCase:     Union[str, Any]=None			,							UpperCamelCase:     Optional[Any]=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(UpperCamelCase  )
	else:
		__lowerCAmelCase         =							convert_config(model[0]			,							UpperCamelCase  )
	__lowerCAmelCase         =							model[0].eval()

	__lowerCAmelCase         =							True if config.feat_extract_norm == "layer" else False
	__lowerCAmelCase         =							WavaVecaFeatureExtractor(
	    feature_size=1			,							sampling_rate=1_6_0_0_0			,							padding_value=0			,							do_normalize=UpperCamelCase			,							return_attention_mask=UpperCamelCase			,							)

	if is_finetuned:
		if dict_path:
			__lowerCAmelCase         =							Dictionary.load(UpperCamelCase  )

			# 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(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  )
			__lowerCAmelCase         =							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			,							)
			__lowerCAmelCase         =							WavaVecaProcessor(feature_extractor=UpperCamelCase			,							tokenizer=UpperCamelCase  )
			processor.save_pretrained(UpperCamelCase  )

		__lowerCAmelCase         =							SEWForCTC(UpperCamelCase  )
	else:
		__lowerCAmelCase         =							SEWModel(UpperCamelCase  )
		feature_extractor.save_pretrained(UpperCamelCase  )

	recursively_load_weights(UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase  )

	hf_model.save_pretrained(UpperCamelCase  )


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
					)








import warnings
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


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  'segformer'







							def __init__(						self  :  List[str]      ,				snake_case__  :  Any=3      ,				snake_case__  :  int=4      ,				snake_case__  :  Tuple=[2, 2, 2, 2]      ,				snake_case__  :  Optional[int]=[8, 4, 2, 1]      ,				snake_case__  :  Union[str, Any]=[32, 64, 160, 256]      ,				snake_case__  :  str=[7, 3, 3, 3]      ,				snake_case__  :  List[Any]=[4, 2, 2, 2]      ,				snake_case__  :  Tuple=[1, 2, 5, 8]      ,				snake_case__  :  List[str]=[4, 4, 4, 4]      ,				snake_case__  :  Optional[Any]="gelu"      ,				snake_case__  :  Optional[Any]=0.0      ,				snake_case__  :  Any=0.0      ,				snake_case__  :  str=0.1      ,				snake_case__  :  List[Any]=0.0_2      ,				snake_case__  :  Any=0.1      ,				snake_case__  :  List[Any]=1E-6      ,				snake_case__  :  Any=256      ,				snake_case__  :  Optional[Any]=255      ,				**snake_case__  :  Union[str, Any]      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__ )

								if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
									warnings.warn(
									    "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
									    " removed, as the behaviour will default to that of reshape_last_stage = True."      ,				snake_case__      ,				)

								__lowerCAmelCase         =							num_channels
								__lowerCAmelCase         =							num_encoder_blocks
								__lowerCAmelCase         =							depths
								__lowerCAmelCase         =							sr_ratios
								__lowerCAmelCase         =							hidden_sizes
								__lowerCAmelCase         =							patch_sizes
								__lowerCAmelCase         =							strides
								__lowerCAmelCase         =							mlp_ratios
								__lowerCAmelCase         =							num_attention_heads
								__lowerCAmelCase         =							hidden_act
								__lowerCAmelCase         =							hidden_dropout_prob
								__lowerCAmelCase         =							attention_probs_dropout_prob
								__lowerCAmelCase         =							classifier_dropout_prob
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							drop_path_rate
								__lowerCAmelCase         =							layer_norm_eps
								__lowerCAmelCase         =							decoder_hidden_size
								__lowerCAmelCase         =							kwargs.get("reshape_last_stage"      ,				snake_case__ )
								__lowerCAmelCase         =							semantic_loss_ignore_index

class       a		(   __UpperCAmelCase					):
							lowercase_		:						List[str]   							=  version.parse('1.11'					)







							@property
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								return OrderedDict(
								    [
								        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
								    ] )







							@property
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								return 1E-4







							@property
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								return 12






