




'''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_     						=				'''src/transformers'''
lowerCAmelCase_     						=				'''docs/source/en'''
lowerCAmelCase_     						=				'''.'''







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







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

   A__       =     start_index
   while not lines[end_index].startswith(_a						):
      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_     						=				'''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase_     						=				re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCAmelCase_     						=				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_     						=				re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase_     						=				direct_transformers_import(TRANSFORMERS_PATH)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







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







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     2 if text == '✅' or text == '❌' else len(_a						)
   A__       =     (width - text_length) // 2
   A__       =     width - text_length - left_indent
   return " " * left_indent + text + " " * right_indent







def 						_A      ( ):






   '''simple docstring'''







   A__       =     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   A__       =     {
       name: config_maping_names[code]
       for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
       if code in config_maping_names
   }
   A__       =     {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.
   A__       =     collections.defaultdict(_a						)
   A__       =     collections.defaultdict(_a						)
   A__       =     collections.defaultdict(_a						)
   A__       =     collections.defaultdict(_a						)
   A__       =     collections.defaultdict(_a						)

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

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

    # Let's build that table!
   A__       =     list(model_name_to_config.keys()						)
   model_names.sort(key=str.lower						)
   A__       =     ['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).
   A__       =     [len(_a						) + 2 for c in columns]
   A__       =     max([len(_a						) for name in model_names]						) + 2

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

   A__       =     {True: '✅', False: '❌'}
   for name in model_names:
      A__       =     model_name_to_prefix[name]
      A__       =     [
          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(_a      ,_a						) for l, w in zip(_a      ,_a						)]						) + "|\n"
   return table







def 						_A      ( UpperCAmelCase=False						):






   '''simple docstring'''







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

   if current_table != new_table:
      if overwrite:
         with open(os.path.join(_a      ,'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_     						=				argparse.ArgumentParser()
    parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
    lowerCAmelCase_     						=				parser.parse_args()

    check_model_table(args.fix_and_overwrite)





"""simple docstring"""


import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int



def   _A					(	_a	:						Optional[Any]     ,   _a	:						Optional[Any]				):



      """simple docstring"""






      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
            # expert layer
            A				  =		flax_key_tuple[:-1] + ("""weight""",)
            A				  =		torch.permute(_a     ,   (0, 2, 1)				)
      elif flax_key_tuple[-1] == "kernel" and ".".join(_a				):
            # linear layer
            A				  =		flax_key_tuple[:-1] + ("""weight""",)
            A				  =		flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
            A				  =		flax_key_tuple[:-1] + ("""weight""",)

      return flax_key_tuple, flax_tensor



def   _A					(	_a	:						Tuple     ,   _a	:						Any     ,   _a	:						Any				):



      """simple docstring"""






      if "metadata" in layer:
            A				  =		layer.split("""metadata"""				)
            A				  =		"""""".join(split_layer[0]				)[:-1]
            A				  =		[tuple(("""metadata""" + split_layer[1]).split("""/"""				)				)]
      elif "kvstore" in layer:
            A				  =		layer.split("""kvstore"""				)
            A				  =		"""""".join(split_layer[0]				)[:-1]
            A				  =		[tuple(("""kvstore""" + split_layer[1]).split("""/"""				)				)]

      else:
            A				  =		layer.split("""/"""				)
            A				  =		"""/""".join(split_layer[:-1]				)
            A				  =		(split_layer[-1],)

      if "kvstore/path" in layer:
            A				  =		f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
      elif "kvstore/driver" in layer:
            A				  =		"""file"""
      else:
            A				  =		checkpoint_info[layer]

      return curr_real_layer_name, split_layer, content



def   _A					(	_a	:						Optional[Any]     ,   _a	:						Optional[int]				):



      """simple docstring"""






      A				  =		rename_keys(_a				)
      A				  =		{}
      for k, v in current_block.items():
            A				  =		v
      A				  =		new_current_block
      torch.save(_a     ,   _a				)



def   _A					(	_a	:						str     ,   _a	:						Optional[Any]     ,   _a	:						int     ,   _a	:						Optional[int]     ,   _a	:						str = WEIGHTS_NAME				):



      """simple docstring"""






      A				  =		convert_file_size_to_int(_a				)
      A				  =		[]
      A				  =		{}
      A				  =		0
      A				  =		0

      os.makedirs(_a     ,   exist_ok=_a				)
      with gfile.GFile(switch_checkpoint_path + """/checkpoint"""     ,   """rb"""				) as fp:
            A				  =		serialization.msgpack_restore(fp.read()				)["""optimizer"""]["""target"""]
            A				  =		flatten_dict(_a     ,   sep="""/"""				)

      A				  =		{}
      for layer in checkpoint_info.keys():
            A  ,			A  ,			A				  =		get_key_and_tensorstore_dict(
                _a     ,   _a     ,   _a				)
            if curr_real_layer_name in all_layers:
                  A				  =		content
            else:
                  A				  =		{split_layer[-1]: content}

      for key in all_layers.keys():
            # open tensorstore file
            A				  =		ts.open(unflatten_dict(all_layers[key]				)				).result().read().result()
            A				  =		torch.tensor(_a				)
            A				  =		raw_weights.numel() * dtype_byte_size(raw_weights.dtype				)

            # use the renaming pattern from the small conversion scripts
            A  ,			A				  =		rename_base_flax_keys(tuple(key.split("""/"""				)				)     ,   _a				)
            A				  =		"""/""".join(_a				)

            # If this weight is going to tip up over the maximal size, we split.
            if current_block_size + weight_size > max_shard_size:
                  A				  =		os.path.join(
                      _a     ,   weights_name.replace(""".bin"""     ,   f'-{len(_a				)+1:05d}-of-???.bin'				)				)
                  rename_and_save_block(_a     ,   _a				)
                  sharded_state_dicts.append(current_block.keys()				)
                  del current_block
                  A				  =		{}
                  A				  =		0

            A				  =		raw_weights.to(getattr(_a     ,   _a				)				)
            current_block_size += weight_size
            total_size += weight_size

      # Add the last block
      A				  =		os.path.join(_a     ,   weights_name.replace(""".bin"""     ,   f'-{len(_a				)+1:05d}-of-???.bin'				)				)
      rename_and_save_block(_a     ,   _a				)
      sharded_state_dicts.append(current_block.keys()				)

      # If we only have one shard, we return it
      if len(_a				) == 1:
            return {weights_name: sharded_state_dicts[0]}, None

      # Otherwise, let's build the index
      A				  =		{}
      A				  =		{}
      for idx, shard in enumerate(_a				):
            A				  =		weights_name.replace(
                """.bin"""     ,   f'-{idx+1:05d}-of-{len(_a				):05d}.bin'				)  # len(sharded_state_dicts):05d}
            A				  =		os.path.join(_a     ,   weights_name.replace(""".bin"""     ,   f'-{idx+1:05d}-of-???.bin'				)				)
            os.rename(_a     ,   os.path.join(_a     ,   _a				)				)
            A				  =		shard
            for key in shard:
                  A				  =		shard_file

    # Add the metadata
      A				  =		{"""total_size""": total_size}
      A				  =		{"""metadata""": metadata, """weight_map""": weight_map}

      with open(os.path.join(_a     ,   _a				)     ,   """w"""     ,   encoding="""utf-8"""				) as f:
            A				  =		json.dumps(_a     ,   indent=2     ,   sort_keys=_a				) + """\n"""
            f.write(_a				)

      return metadata, index


if __name__ == "__main__":
  UpperCAmelCase													=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--switch_t5x_checkpoint_path",
      default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
      type=str,
      required=False,
      help="Path to a directory containing a folder per layer. Follows the original Google format.",
  )
  parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
  parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
      type=str,
      required=False,
      help="Path to the output pytorch model.",
  )
  UpperCAmelCase													=parser.parse_args()
  shard_on_the_fly(
      args.switch_tax_checkpoint_path,
      args.pytorch_dump_folder_path,
      args.max_shard_size,
      args.dtype,
  )





def   _A					(	):



      """simple docstring"""






      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

      A				  =		SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""				)
      config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""				)
      A				  =		SwitchTransformersForConditionalGeneration.from_pretrained(
          """/home/arthur_huggingface_co/transformers/switch_converted"""     ,   device_map="""auto"""				)

      A				  =		TaTokenizer.from_pretrained("""t5-small"""				)
      A				  =		"""A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""

      A				  =		tokenizer(_a     ,   return_tensors="""pt"""				).input_ids
      A				  =		model.generate(_a     ,   decoder_start_token_id=0				)
      print(tokenizer.decode(out[0]				)				)




