






"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def 							lowerCAmelCase			(   *UpperCamelCase_:				Tuple       )							->				int:







  '''simple docstring'''







  _a					      =						torch.cuda.device_count()
  print(f'''Device count: {num_processes}'''       )
  with patch_environment(
      world_size=UpperCamelCase_  ,			master_addr="127.0.01"  ,			master_port="29500"  ,			mixed_precision=args[1].mixed_precision       ):
    _a					      =						PrepareForLaunch(UpperCamelCase_  ,			distributed_type="MULTI_GPU"       )
    torch.multiprocessing.start_processes(UpperCamelCase_  ,			args=UpperCamelCase_  ,			nprocs=UpperCamelCase_  ,			start_method="spawn"       )


if __name__ == "__main__":
     # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
     # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

     # on-demand GPU
     # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
     UpperCamelCase											=     rh.cluster(name="""rh-cluster""", instance_type="""V100:4""", provider="""cheapest""", use_spot=False)  # multi GPU
     gpu.up_if_not()

     # on-prem GPU
     # gpu = rh.cluster(
     #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
     #       )

     # Set up remote function
     UpperCamelCase											=     [
         """pip:./""",
         """transformers""",
         """datasets""",
         """evaluate""",
         """tqdm""",
         """scipy""",
         """scikit-learn""",
         """tensorboard""",
         """torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117""",
     ]
     UpperCamelCase											=     rh.function(fn=launch_train, system=gpu, reqs=reqs, name="""train_bert_glue""")

     # Define train args/config, run train function
     UpperCamelCase											=     argparse.Namespace(cpu=False, mixed_precision="""fp16""")
     UpperCamelCase											=     {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
     launch_train_gpu(config, train_args, stream_logs=True)

     # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
     # gpu.install_packages(reqs)
     # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])




"""simple docstring"""




import argparse
import collections
import importlib.util
import os
import re


# 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
A__   :						List[str] 							=     """src/diffusers"""
A__   :						List[str] 							=     """docs/source/en"""
A__   :						List[Any] 							=     """."""



def 				a__			(					lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							Any							,     lowerCAmelCase						:							List[Any]		):






     '''simple docstring'''
     with open(lowerCAmelCase							,     "r"							,     encoding="utf-8"							,     newline="\n"		) as f:
          UpperCAmelCase__				:			List[str]							=    f.readlines()
     # Find the start prompt.
     UpperCAmelCase__				:			Union[str, Any]							=    0
     while not lines[start_index].startswith(lowerCAmelCase		):
          start_index += 1
     start_index += 1

     UpperCAmelCase__				:			List[Any]							=    start_index
     while not lines[end_index].startswith(lowerCAmelCase		):
          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 |
A__   :						Tuple 							=     """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
A__   :						Any 							=     re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
A__   :						Tuple 							=     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.
A__   :						List[Any] 							=     re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
A__   :						List[Any] 							=     importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
A__   :						Dict 							=     spec.loader.load_module()



def 				a__			(					lowerCAmelCase						:							List[str]		):






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



def 				a__			(					lowerCAmelCase						:							Tuple							,     lowerCAmelCase						:							Union[str, Any]		):






     '''simple docstring'''
     UpperCAmelCase__				:			List[Any]							=    2 if text == "✅" or text == "❌" else len(lowerCAmelCase		)
     UpperCAmelCase__				:			Any							=    (width - text_length) // 2
     UpperCAmelCase__				:			List[Any]							=    width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     UpperCAmelCase__				:			Optional[int]							=    {
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     UpperCAmelCase__				:			Optional[int]							=    {name: config.replace("ConfigMixin"							,     ""		) 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__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			Optional[int]							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			Union[str, Any]							=    collections.defaultdict(lowerCAmelCase		)

     # Let's lookup through all diffusers object (once).
     for attr_name in dir(lowerCAmelCase		):
          UpperCAmelCase__				:			List[str]							=    None
          if attr_name.endswith("Tokenizer"		):
               UpperCAmelCase__				:			Union[str, Any]							=    slow_tokenizers
               UpperCAmelCase__				:			Optional[Any]							=    attr_name[:-9]
          elif attr_name.endswith("TokenizerFast"		):
               UpperCAmelCase__				:			List[str]							=    fast_tokenizers
               UpperCAmelCase__				:			Union[str, Any]							=    attr_name[:-13]
          elif _re_tf_models.match(lowerCAmelCase		) is not None:
               UpperCAmelCase__				:			Union[str, Any]							=    tf_models
               UpperCAmelCase__				:			Optional[int]							=    _re_tf_models.match(lowerCAmelCase		).groups()[0]
          elif _re_flax_models.match(lowerCAmelCase		) is not None:
               UpperCAmelCase__				:			List[Any]							=    flax_models
               UpperCAmelCase__				:			Any							=    _re_flax_models.match(lowerCAmelCase		).groups()[0]
          elif _re_pt_models.match(lowerCAmelCase		) is not None:
               UpperCAmelCase__				:			str							=    pt_models
               UpperCAmelCase__				:			List[str]							=    _re_pt_models.match(lowerCAmelCase		).groups()[0]

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

    # Let's build that table!
     UpperCAmelCase__				:			Tuple							=    list(model_name_to_config.keys()		)
     model_names.sort(key=str.lower		)
     UpperCAmelCase__				:			Dict							=    ["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__				:			List[str]							=    [len(lowerCAmelCase		) + 2 for c in columns]
     UpperCAmelCase__				:			Union[str, Any]							=    max([len(lowerCAmelCase		) for name in model_names]		) + 2

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

     UpperCAmelCase__				:			List[Any]							=    {True: "✅", False: "❌"}
     for name in model_names:
          UpperCAmelCase__				:			Optional[Any]							=    model_name_to_prefix[name]
          UpperCAmelCase__				:			List[Any]							=    [
              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(lowerCAmelCase							,     lowerCAmelCase		) for l, w in zip(lowerCAmelCase							,     lowerCAmelCase		)]		) + "|\n"
     return table



def 				a__			(					lowerCAmelCase						:							Union[str, Any]=False		):






     '''simple docstring'''
     UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			int							=    _find_text_in_file(
         filename=os.path.join(lowerCAmelCase							,     "index.mdx"		)							,     start_prompt="<!--This table is updated automatically from the auto modules"							,     end_prompt="<!-- End table-->"							,     )
     UpperCAmelCase__				:			Optional[Any]							=    get_model_table_from_auto_modules()

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(lowerCAmelCase							,     "index.mdx"		)							,     "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.mdx` has not been updated. Run `make fix-copies` to fix this."		)


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

    check_model_table(args.fix_and_overwrite)



