

"""simple docstring"""


import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__A						   =			logging.get_logger(__name__)

__A						   =			{
    "post_extract_proj": "feature_projection.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.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
__A						   =			[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Optional[int]:
		for attribute in key.split("." ):
				__lowerCAmelCase:       Union[str, Any]          =			getattr(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__ )

		if weight_type is not None:
				__lowerCAmelCase:       List[Any]          =			getattr(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__ ).shape
		else:
				__lowerCAmelCase:       List[Any]          =			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:       Optional[Any]          =			value
		elif weight_type == "weight_g":
				__lowerCAmelCase:       Optional[Any]          =			value
		elif weight_type == "weight_v":
				__lowerCAmelCase:       Tuple          =			value
		elif weight_type == "bias":
				__lowerCAmelCase:       List[str]          =			value
		else:
				__lowerCAmelCase:       Dict          =			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 )	->	Any:
		__lowerCAmelCase:       str          =			[]
		__lowerCAmelCase:       Union[str, Any]          =			fairseq_model.state_dict()

		__lowerCAmelCase:       Dict          =			hf_model.feature_extractor
		__lowerCAmelCase:       Dict          =			hf_model.adapter

		for name, value in fairseq_dict.items():
				__lowerCAmelCase:       List[str]          =			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"					, )
						__lowerCAmelCase:       Optional[int]          =			True
				elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."] ):
						load_adapter(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__ )
						__lowerCAmelCase:       Dict          =			True
				else:
						for key, mapped_key in MAPPING.items():
								if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
										__lowerCAmelCase:       Any          =			True
										if "*" in mapped_key:
												__lowerCAmelCase:       Any          =			name.split(SCREAMING_SNAKE_CASE__ )[0].split("." )[-2]
												__lowerCAmelCase:       List[str]          =			mapped_key.replace("*"					, SCREAMING_SNAKE_CASE__ )
										if "weight_g" in name:
												__lowerCAmelCase:       Optional[int]          =			"""weight_g"""
										elif "weight_v" in name:
												__lowerCAmelCase:       Tuple          =			"""weight_v"""
										elif "bias" in name:
												__lowerCAmelCase:       Optional[Any]          =			"""bias"""
										elif "weight" in name:
												__lowerCAmelCase:       Optional[int]          =			"""weight"""
										else:
												__lowerCAmelCase:       Dict          =			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 )	->	int:
		__lowerCAmelCase:       Dict          =			full_name.split("conv_layers." )[-1]
		__lowerCAmelCase:       List[Any]          =			name.split("." )
		__lowerCAmelCase:       List[str]          =			int(items[0] )
		__lowerCAmelCase:       Optional[Any]          =			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:       Union[str, Any]          =			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:       Any          =			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:       Tuple          =			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:       Tuple          =			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					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Optional[Any]:
		__lowerCAmelCase:       Optional[Any]          =			full_name.split("adaptor." )[-1]
		__lowerCAmelCase:       Tuple          =			name.split("." )

		if items[1].isdigit():
				__lowerCAmelCase:       Tuple          =			int(items[1] )
		else:
				__lowerCAmelCase:       Tuple          =			None

		if "adaptor" not in full_name:
				if "proj_ln" in full_name:
						# has to be layer norm
						if "bias" in name:
								assert (
								    value.shape == adapter.proj_layer_norm.bias.data.shape
								), F"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."
								__lowerCAmelCase:       int          =			value
								logger.info(F"Adapter proj layer norm bias was initialized from {full_name}." )
						if "weight" in name:
								assert (
								    value.shape == adapter.proj_layer_norm.weight.data.shape
								), F"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."
								__lowerCAmelCase:       str          =			value
				else:
						# has to be projection layer
						if "bias" in name:
								assert (
								    value.shape == adapter.proj.bias.data.shape
								), F"{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."
								__lowerCAmelCase:       Union[str, Any]          =			value
								logger.info(F"Adapter proj layer bias was initialized from {full_name}." )
						if "weight" in name:
								assert (
								    value.shape == adapter.proj.weight.data.shape
								), F"{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."
								__lowerCAmelCase:       Union[str, Any]          =			value
								logger.info(F"Adapter proj layer weight was initialized from {full_name}." )
		elif isinstance(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__ ):
				if "bias" in name:
						assert (
						    value.shape == adapter.layers[layer_id].conv.bias.data.shape
						), F"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."
						__lowerCAmelCase:       Optional[Any]          =			value
						logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}." )
				elif "weight" in name:
						assert (
						    value.shape == adapter.layers[layer_id].conv.weight.data.shape
						), F"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."
						__lowerCAmelCase:       int          =			value
						logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}." )
		else:
				unused_weights.append(SCREAMING_SNAKE_CASE__ )






def     a__    (    __SCREAMING_SNAKE_CASE )	->	Optional[Any]:
		__lowerCAmelCase:       Optional[Any]          =			emb.weight.shape
		__lowerCAmelCase:       List[str]          =			nn.Linear(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, bias=SCREAMING_SNAKE_CASE__ )
		__lowerCAmelCase:       str          =			emb.weight.data
		return lin_layer






@torch.no_grad()
def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, )	->	Dict:
		__lowerCAmelCase:       Tuple          =			WavaVecaConfig.from_pretrained(
		    SCREAMING_SNAKE_CASE__					, add_adapter=SCREAMING_SNAKE_CASE__					, adapter_stride=SCREAMING_SNAKE_CASE__					, adapter_kernel_size=SCREAMING_SNAKE_CASE__					, use_auth_token=SCREAMING_SNAKE_CASE__					, output_hidden_size=SCREAMING_SNAKE_CASE__					, )
		__lowerCAmelCase:       int          =			MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )

		# load model
		__lowerCAmelCase:       List[str]          =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]					, arg_overrides={
		        "config_yaml": config_yaml_path,
		        "data": "/".join(dict_path.split("/" )[:-1] ),
		        "w2v_path": checkpoint_path,
		        "load_pretrained_decoder_from": None,
		    }					, )
		__lowerCAmelCase:       List[Any]          =			model[0].eval()

		# load feature extractor
		__lowerCAmelCase:       str          =			WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__					, use_auth_token=SCREAMING_SNAKE_CASE__ )

		# set weights for wav2vec2 encoder
		__lowerCAmelCase:       Dict          =			WavaVecaModel(SCREAMING_SNAKE_CASE__ )

		recursively_load_weights_wavaveca(model.encoder					, SCREAMING_SNAKE_CASE__ )

		# load decoder weights
		__lowerCAmelCase:       Union[str, Any]          =			MBartForCausalLM(SCREAMING_SNAKE_CASE__ )
		__lowerCAmelCase:       Optional[int]          =			hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					, strict=SCREAMING_SNAKE_CASE__ )
		logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}" )
		logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}" )

		__lowerCAmelCase:       Dict          =			SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__					, decoder=SCREAMING_SNAKE_CASE__ )
		__lowerCAmelCase:       Optional[int]          =			False

		__lowerCAmelCase:       Union[str, Any]          =			MBartaaTokenizer(SCREAMING_SNAKE_CASE__ )
		tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )

		__lowerCAmelCase:       Optional[int]          =			hf_wavavec.config.to_dict()
		__lowerCAmelCase:       int          =			tokenizer.pad_token_id
		__lowerCAmelCase:       Union[str, Any]          =			tokenizer.bos_token_id
		__lowerCAmelCase:       Tuple          =			tokenizer.eos_token_id
		__lowerCAmelCase:       List[Any]          =			"""mbart50"""
		__lowerCAmelCase:       Optional[int]          =			"""wav2vec2"""

		__lowerCAmelCase:       Union[str, Any]          =			tokenizer.eos_token_id
		__lowerCAmelCase:       str          =			2_5_0_0_0_4
		__lowerCAmelCase:       Optional[Any]          =			tokenizer.eos_token_id

		__lowerCAmelCase:       List[Any]          =			SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE__ )

		hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__ )
		feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
			__A						   =			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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
			parser.add_argument(
			    "--encoder_config_path",
			    default="facebook/wav2vec2-xls-r-1b",
			    type=str,
			    help="Path to hf encoder wav2vec2 checkpoint config",
			)
			parser.add_argument(
			    "--decoder_config_path",
			    default="facebook/mbart-large-50-one-to-many-mmt",
			    type=str,
			    help="Path to hf decoder checkpoint config",
			)
			parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
			parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
			parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
			parser.add_argument("--encoder_output_dim", default=1024, type=int, help="encoder output dim")
			parser.add_argument("--start_token_id", default=250004, type=int, help="`decoder_start_token_id` of model config")

			__A						   =			parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.dict_path,
			    args.config_yaml_path,
			    encoder_config_path=args.encoder_config_path,
			    decoder_config_path=args.decoder_config_path,
			    add_adapter=args.add_adapter,
			    adapter_kernel_size=args.adapter_kernel_size,
			    adapter_stride=args.adapter_stride,
			    decoder_start_token_id=args.start_token_id,
			    encoder_output_dim=args.encoder_output_dim,
			)









from statistics import mean

import numpy as np







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list:
  __snake_case:						List[Any]   =						0
  # Number of processes finished
  __snake_case:						Union[str, Any]   =						0
  # Displays the finished process.
  # If it is 0, the performance is completed if it is 1, before the performance.
  __snake_case:						Dict   =						[0] * no_of_process
  # List to include calculation results
  __snake_case:						Tuple   =						[0] * no_of_process

  # Sort by arrival time.
  __snake_case:						int   =						[burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  __snake_case:						Any   =						[process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  arrival_time.sort()

  while no_of_process > finished_process_count:
    __snake_case:						Tuple   =						0
    while finished_process[i] == 1:
      i += 1
    if current_time < arrival_time[i]:
      __snake_case:						Any   =						arrival_time[i]

    __snake_case:						List[Any]   =						0
    # Index showing the location of the process being performed
    __snake_case:						Union[str, Any]   =						0
    # Saves the current response ratio.
    __snake_case:						Optional[Any]   =						0
    for i in range(0					,					SCREAMING_SNAKE_CASE__):
      if finished_process[i] == 0 and arrival_time[i] <= current_time:
        __snake_case:						Tuple   =						(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
            i
        ]
      if response_ratio < temp:
        __snake_case:						Union[str, Any]   =						temp
        __snake_case:						Optional[int]   =						i

        # Calculate the turn around time
    __snake_case:						Optional[Any]   =						current_time + burst_time[loc] - arrival_time[loc]
    current_time += burst_time[loc]
    # Indicates that the process has been performed.
    __snake_case:						Optional[int]   =						1
    # Increase finished_process_count by 1
    finished_process_count += 1

  return turn_around_time







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list:
  __snake_case:						Union[str, Any]   =						[0] * no_of_process
  for i in range(0					,					SCREAMING_SNAKE_CASE__):
    __snake_case:						Optional[int]   =						turn_around_time[i] - burst_time[i]
  return waiting_time


if __name__ == "__main__":
      __UpperCAmelCase       :   Optional[Any]				       =							5
      __UpperCAmelCase       :   Tuple				       =							["A", "B", "C", "D", "E"]
      __UpperCAmelCase       :   str				       =							[1, 2, 3, 4, 5]
      __UpperCAmelCase       :   Dict				       =							[1, 2, 3, 4, 5]

      __UpperCAmelCase       :   List[str]				       =							calculate_turn_around_time(
          process_name, arrival_time, burst_time, no_of_process
      )
      __UpperCAmelCase       :   List[str]				       =							calculate_waiting_time(
          process_name, turn_around_time, burst_time, no_of_process
      )

      print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
      for i in range(0, no_of_process):
            print(
                f'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
                f'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
            )

      print(f'average waiting time : {mean(waiting_time):.5f}')
      print(f'average turn around time : {mean(turn_around_time):.5f}')






