
"""simple docstring"""



import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Tuple     							=					AutoConfig.from_pretrained(_snake_case )
    UpperCAmelCase__	:      Any     							=					FlaxAutoModelForSeqaSeqLM.from_config(config=_snake_case )
    UpperCAmelCase__	:      str     							=					checkpoints.load_tax_checkpoint(_snake_case )

    UpperCAmelCase__	:      List[Any]     							=					'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

    if config.model_type == "t5":
        UpperCAmelCase__	:      Union[str, Any]     							=					'SelfAttention'
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        UpperCAmelCase__	:      List[Any]     							=					'LocalSelfAttention'
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        UpperCAmelCase__	:      Optional[int]     							=					'TransientGlobalSelfAttention'
    else:
        raise ValueError(
            'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
            ' attribute with a value from [\'local\', \'transient-global].' )

    # Encoder
    for layer_index in range(config.num_layers ):
        UpperCAmelCase__	:      Union[str, Any]     							=					F'''layers_{str(_snake_case )}'''

        # Self-Attention
        UpperCAmelCase__	:      List[str]     							=					tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
        UpperCAmelCase__	:      Tuple     							=					tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
        UpperCAmelCase__	:      Dict     							=					tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
        UpperCAmelCase__	:      Tuple     							=					tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            UpperCAmelCase__	:      List[str]     							=					tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

        # Layer Normalization
        UpperCAmelCase__	:      List[Any]     							=					tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

        if split_mlp_wi:
            UpperCAmelCase__	:      Optional[Any]     							=					tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
            UpperCAmelCase__	:      Tuple     							=					tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
        else:
            UpperCAmelCase__	:      List[Any]     							=					tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

        UpperCAmelCase__	:      Dict     							=					tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

        # Layer Normalization
        UpperCAmelCase__	:      int     							=					tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

        # Assigning
        UpperCAmelCase__	:      Union[str, Any]     							=					flax_model.params['encoder']['block'][str(_snake_case )]['layer']
        UpperCAmelCase__	:      Optional[Any]     							=					tax_attention_key
        UpperCAmelCase__	:      Any     							=					tax_attention_out
        UpperCAmelCase__	:      str     							=					tax_attention_query
        UpperCAmelCase__	:      str     							=					tax_attention_value

        UpperCAmelCase__	:      List[Any]     							=					tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            UpperCAmelCase__	:      List[str]     							=					tax_global_layer_norm

        if split_mlp_wi:
            UpperCAmelCase__	:      Union[str, Any]     							=					tax_mlp_wi_a
            UpperCAmelCase__	:      int     							=					tax_mlp_wi_a
        else:
            UpperCAmelCase__	:      str     							=					tax_mlp_wi

        UpperCAmelCase__	:      str     							=					tax_mlp_wo
        UpperCAmelCase__	:      Optional[Any]     							=					tax_mlp_layer_norm

        UpperCAmelCase__	:      List[str]     							=					flax_model_encoder_layer_block

    # Only for layer 0:
    UpperCAmelCase__	:      List[Any]     							=					tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
    UpperCAmelCase__	:      Union[str, Any]     							=					tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        UpperCAmelCase__	:      List[str]     							=					tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
        UpperCAmelCase__	:      int     							=					tax_encoder_global_rel_embedding

    # Assigning
    UpperCAmelCase__	:      List[str]     							=					tax_model['target']['encoder']['encoder_norm']['scale']
    UpperCAmelCase__	:      str     							=					tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers ):
        UpperCAmelCase__	:      Optional[int]     							=					F'''layers_{str(_snake_case )}'''

        # Self-Attention
        UpperCAmelCase__	:      Union[str, Any]     							=					tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
        UpperCAmelCase__	:      int     							=					tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
        UpperCAmelCase__	:      Dict     							=					tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
        UpperCAmelCase__	:      List[str]     							=					tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

        # Layer Normalization
        UpperCAmelCase__	:      str     							=					tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
            'scale'
        ]

        # Encoder-Decoder-Attention
        UpperCAmelCase__	:      Optional[Any]     							=					tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
        UpperCAmelCase__	:      Optional[int]     							=					tax_enc_dec_attention_module['key']['kernel']
        UpperCAmelCase__	:      List[str]     							=					tax_enc_dec_attention_module['out']['kernel']
        UpperCAmelCase__	:      Optional[int]     							=					tax_enc_dec_attention_module['query']['kernel']
        UpperCAmelCase__	:      Dict     							=					tax_enc_dec_attention_module['value']['kernel']

        # Layer Normalization
        UpperCAmelCase__	:      List[str]     							=					tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

        # MLP
        if split_mlp_wi:
            UpperCAmelCase__	:      Any     							=					tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
            UpperCAmelCase__	:      Optional[int]     							=					tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
        else:
            UpperCAmelCase__	:      int     							=					tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

        UpperCAmelCase__	:      Tuple     							=					tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

        # Layer Normalization
        UpperCAmelCase__	:      Optional[int]     							=					tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

        # Assigning
        UpperCAmelCase__	:      Union[str, Any]     							=					flax_model.params['decoder']['block'][str(_snake_case )]['layer']
        UpperCAmelCase__	:      List[Any]     							=					tax_attention_key
        UpperCAmelCase__	:      int     							=					tax_attention_out
        UpperCAmelCase__	:      List[str]     							=					tax_attention_query
        UpperCAmelCase__	:      Dict     							=					tax_attention_value

        UpperCAmelCase__	:      Any     							=					tax_pre_attention_layer_norm

        UpperCAmelCase__	:      str     							=					tax_enc_dec_attention_key
        UpperCAmelCase__	:      List[Any]     							=					tax_enc_dec_attention_out
        UpperCAmelCase__	:      Optional[Any]     							=					tax_enc_dec_attention_query
        UpperCAmelCase__	:      int     							=					tax_enc_dec_attention_value

        UpperCAmelCase__	:      str     							=					tax_cross_layer_norm

        if split_mlp_wi:
            UpperCAmelCase__	:      Optional[Any]     							=					tax_mlp_wi_a
            UpperCAmelCase__	:      Union[str, Any]     							=					tax_mlp_wi_a
        else:
            UpperCAmelCase__	:      int     							=					tax_mlp_wi

        UpperCAmelCase__	:      Optional[int]     							=					tax_mlp_wo

        UpperCAmelCase__	:      List[str]     							=					txa_mlp_layer_norm

        UpperCAmelCase__	:      Tuple     							=					flax_model_decoder_layer_block

    # Decoder Normalization
    UpperCAmelCase__	:      Tuple     							=					tax_model['target']['decoder']['decoder_norm']['scale']
    UpperCAmelCase__	:      Union[str, Any]     							=					txa_decoder_norm

    # Only for layer 0:
    UpperCAmelCase__	:      List[Any]     							=					tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
    UpperCAmelCase__	:      List[Any]     							=					tax_decoder_rel_embedding

    # Token Embeddings
    UpperCAmelCase__	:      Tuple     							=					tax_model['target']['token_embedder']['embedding']
    UpperCAmelCase__	:      Optional[int]     							=					txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        UpperCAmelCase__	:      str     							=					tax_model['target']['decoder']['logits_dense']['kernel']

    flax_model.save_pretrained(_snake_case )
    print('T5X Model was sucessfully converted!' )


if __name__ == "__main__":
    UpperCamelCase__          =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
    )
    parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
    parser.add_argument(
        '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
    )
    UpperCamelCase__          =	parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


"""simple docstring"""


from __future__ import annotations

import pandas as pd






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   [0] * no_of_processes
  _UpperCAmelCase    			=   [0] * no_of_processes
  # Copy the burst time into remaining_time[]
  for i in range(lowercase		):
    _UpperCAmelCase    			=   burst_time[i]

  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   9_99_99_99_99
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   False

  # Process until all processes are completed
  while complete != no_of_processes:
    for j in range(lowercase		):
      if arrival_time[j] <= increment_time and remaining_time[j] > 0:
        if remaining_time[j] < minm:
          _UpperCAmelCase    			=   remaining_time[j]
          _UpperCAmelCase    			=   j
          _UpperCAmelCase    			=   True

    if not check:
      increment_time += 1
      continue
    remaining_time[short] -= 1

    _UpperCAmelCase    			=   remaining_time[short]
    if minm == 0:
      _UpperCAmelCase    			=   9_99_99_99_99

    if remaining_time[short] == 0:
      complete += 1
      _UpperCAmelCase    			=   False

      # Find finish time of current process
      _UpperCAmelCase    			=   increment_time + 1

      # Calculate waiting time
      _UpperCAmelCase    			=   finish_time - arrival_time[short]
      _UpperCAmelCase    			=   finar - burst_time[short]

      if waiting_time[short] < 0:
        _UpperCAmelCase    			=   0

        # Increment time
    increment_time += 1
  return waiting_time






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   [0] * no_of_processes
  for i in range(lowercase		):
    _UpperCAmelCase    			=   burst_time[i] + waiting_time[i]
  return turn_around_time






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   0
  for i in range(lowercase		):
    _UpperCAmelCase    			=   total_waiting_time + waiting_time[i]
    _UpperCAmelCase    			=   total_turn_around_time + turn_around_time[i]
  print(f'''Average waiting time = {total_waiting_time / no_of_processes:.5f}'''		)
  print("""Average turn around time =""" ,total_turn_around_time / no_of_processes		)


if __name__ == "__main__":
    print("""Enter how many process you want to analyze""")
    UpperCAmelCase__			=    int(input())
    UpperCAmelCase__			=    [0] * no_of_processes
    UpperCAmelCase__			=    [0] * no_of_processes
    UpperCAmelCase__			=    list(range(1, no_of_processes + 1))

    for i in range(no_of_processes):
        print("""Enter the arrival time and burst time for process:--""" + str(i + 1))
        UpperCAmelCase__		, UpperCAmelCase__			=    map(int, input().split())

    UpperCAmelCase__			=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)

    UpperCAmelCase__			=    burst_time
    UpperCAmelCase__			=    no_of_processes
    UpperCAmelCase__			=    waiting_time
    UpperCAmelCase__			=    calculate_turnaroundtime(bt, n, wt)

    calculate_average_times(waiting_time, turn_around_time, no_of_processes)

    UpperCAmelCase__			=    pd.DataFrame(
        list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
        columns=[
            """Process""",
            """BurstTime""",
            """ArrivalTime""",
            """WaitingTime""",
            """TurnAroundTime""",
        ],
    )

    # Printing the dataFrame
    pd.set_option("""display.max_rows""", fcfs.shape[0] + 1)
    print(fcfs)
