






"""simple docstring"""







import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowerCAmelCase_	:    List[str]					     =      '''src/transformers'''
lowerCAmelCase_	:    Any					     =      '''docs/source/en'''
lowerCAmelCase_	:    Union[str, Any]					     =      '''.'''






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''

       with open(lowercase_	,      """r"""	,      encoding="""utf-8"""	,      newline="""\n"""		) as f:
              UpperCAmelCase	    =			f.readlines()
       # Find the start prompt.
       UpperCAmelCase	    =			0
       while not lines[start_index].startswith(lowercase_		):
              start_index += 1
       start_index += 1

       UpperCAmelCase	    =			start_index
       while not lines[end_index].startswith(lowercase_		):
              end_index += 1
       end_index -= 1

       while len(lines[start_index]		) <= 1:
              start_index += 1
       while len(lines[end_index]		) <= 1:
              end_index -= 1
       end_index += 1
       return "".join(lines[start_index:end_index]		), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
lowerCAmelCase_	:    Optional[Any]					     =      '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase_	:    Dict					     =      re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCAmelCase_	:    List[str]					     =      re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase_	:    List[str]					     =      re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase_	:    Union[str, Any]					     =      direct_transformers_import(TRANSFORMERS_PATH)






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""	,      lowercase_		)
       return [m.group(0		) for m in matches]






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			2 if text == "✅" or text == "❌" else len(lowercase_		)
       UpperCAmelCase	    =			(width - text_length) // 2
       UpperCAmelCase	    =			width - text_length - left_indent
       return " " * left_indent + text + " " * right_indent






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       UpperCAmelCase	    =			{
           name: config_maping_names[code]
           for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
           if code in config_maping_names
       }
       UpperCAmelCase	    =			{name: config.replace("""Config"""	,      """"""		) for name, config in model_name_to_config.items()}

       # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
       UpperCAmelCase	    =			collections.defaultdict(lowercase_		)
       UpperCAmelCase	    =			collections.defaultdict(lowercase_		)
       UpperCAmelCase	    =			collections.defaultdict(lowercase_		)
       UpperCAmelCase	    =			collections.defaultdict(lowercase_		)
       UpperCAmelCase	    =			collections.defaultdict(lowercase_		)

       # Let's lookup through all transformers object (once).
       for attr_name in dir(lowercase_		):
              UpperCAmelCase	    =			None
              if attr_name.endswith("""Tokenizer"""		):
                     UpperCAmelCase	    =			slow_tokenizers
                     UpperCAmelCase	    =			attr_name[:-9]
              elif attr_name.endswith("""TokenizerFast"""		):
                     UpperCAmelCase	    =			fast_tokenizers
                     UpperCAmelCase	    =			attr_name[:-13]
              elif _re_tf_models.match(lowercase_		) is not None:
                     UpperCAmelCase	    =			tf_models
                     UpperCAmelCase	    =			_re_tf_models.match(lowercase_		).groups()[0]
              elif _re_flax_models.match(lowercase_		) is not None:
                     UpperCAmelCase	    =			flax_models
                     UpperCAmelCase	    =			_re_flax_models.match(lowercase_		).groups()[0]
              elif _re_pt_models.match(lowercase_		) is not None:
                     UpperCAmelCase	    =			pt_models
                     UpperCAmelCase	    =			_re_pt_models.match(lowercase_		).groups()[0]

              if lookup_dict is not None:
                     while len(lowercase_		) > 0:
                            if attr_name in model_name_to_prefix.values():
                                   UpperCAmelCase	    =			True
                                   break
                            # Try again after removing the last word in the name
                            UpperCAmelCase	    =			"".join(camel_case_split(lowercase_		)[:-1]		)

    # Let's build that table!
       UpperCAmelCase	    =			list(model_name_to_config.keys()		)
       model_names.sort(key=str.lower		)
       UpperCAmelCase	    =			["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
       # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
       UpperCAmelCase	    =			[len(lowercase_		) + 2 for c in columns]
       UpperCAmelCase	    =			max([len(lowercase_		) for name in model_names]		) + 2

       # Build the table per se
       UpperCAmelCase	    =			"|" + "|".join([_center_text(lowercase_	,      lowercase_		) for c, w in zip(lowercase_	,      lowercase_		)]		) + "|\n"
       # Use ":-----:" format to center-aligned table cell texts
       table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]		) + "|\n"

       UpperCAmelCase	    =			{True: "✅", False: "❌"}
       for name in model_names:
              UpperCAmelCase	    =			model_name_to_prefix[name]
              UpperCAmelCase	    =			[
                  name,
                  check[slow_tokenizers[prefix]],
                  check[fast_tokenizers[prefix]],
                  check[pt_models[prefix]],
                  check[tf_models[prefix]],
                  check[flax_models[prefix]],
              ]
              table += "|" + "|".join([_center_text(lowercase_	,      lowercase_		) for l, w in zip(lowercase_	,      lowercase_		)]		) + "|\n"
       return table






def       _lowerCAmelCase       (      lowerCAmelCase=False		):







       '''simple docstring'''
       UpperCAmelCase	    =			_find_text_in_file(
           filename=os.path.join(lowercase_	,      """index.md"""		)	,      start_prompt="""<!--This table is updated automatically from the auto modules"""	,      end_prompt="""<!-- End table-->"""	,      )
       UpperCAmelCase	    =			get_model_table_from_auto_modules()

       if current_table != new_table:
              if overwrite:
                     with open(os.path.join(lowercase_	,      """index.md"""		)	,      """w"""	,      encoding="""utf-8"""	,      newline="""\n"""		) as f:
                            f.writelines(lines[:start_index] + [new_table] + lines[end_index:]		)
              else:
                     raise ValueError(
                         """The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."""		)


if __name__ == "__main__":
 lowerCAmelCase_	:    str					     =      argparse.ArgumentParser()
 parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
 lowerCAmelCase_	:    List[Any]					     =      parser.parse_args()

 check_model_table(args.fix_and_overwrite)








"""simple docstring"""







from __future__ import annotations

import pandas as pd






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[0] * no_of_processes
       UpperCAmelCase	    =			[0] * no_of_processes
       # Copy the burst time into remaining_time[]
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			burst_time[i]

       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			999999999
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			False

       # Process until all processes are completed
       while complete != no_of_processes:
              for j in range(lowerCAmelCase		):
                     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	    =			999999999

              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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[0] * no_of_processes
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			burst_time[i] + waiting_time[i]
       return turn_around_time






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			0
       for i in range(lowerCAmelCase		):
              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''')
 lowerCAmelCase_	:    Optional[Any]            =      int(input())
 lowerCAmelCase_	:    List[Any]            =      [0] * no_of_processes
 lowerCAmelCase_	:    Optional[Any]            =      [0] * no_of_processes
 lowerCAmelCase_	:    int            =      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))
  lowerCAmelCase_     ,					lowerCAmelCase_	:    Optional[Any]            =      map(int, input().split())

 lowerCAmelCase_	:    str            =      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

 lowerCAmelCase_	:    str            =      burst_time
 lowerCAmelCase_	:    List[Any]            =      no_of_processes
 lowerCAmelCase_	:    int            =      waiting_time
 lowerCAmelCase_	:    int            =      calculate_turnaroundtime(bt, n, wt)

 calculate_average_times(waiting_time, turn_around_time, no_of_processes)

 lowerCAmelCase_	:    List[Any]            =      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)
