




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
SCREAMING_SNAKE_CASE__	:				Dict  =       'src/diffusers'
SCREAMING_SNAKE_CASE__	:				List[str]  =       'docs/source/en'
SCREAMING_SNAKE_CASE__	:				str  =       '.'

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			int:
       with open(_SCREAMING_SNAKE_CASE			,"r"			,encoding="utf-8"			,newline="\n"   ) as f:
              lowerCamelCase       :				List[Any]      			= f.readlines()
       # Find the start prompt.
       lowerCamelCase       :				Any      			= 0
       while not lines[start_index].startswith(_SCREAMING_SNAKE_CASE   ):
              start_index += 1
       start_index += 1

       lowerCamelCase       :				Optional[Any]      			= start_index
       while not lines[end_index].startswith(_SCREAMING_SNAKE_CASE   ):
              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 |
SCREAMING_SNAKE_CASE__	:				List[str]  =       'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE__	:				List[str]  =       re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
SCREAMING_SNAKE_CASE__	:				Union[str, Any]  =       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.
SCREAMING_SNAKE_CASE__	:				List[Any]  =       re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			int:
       lowerCamelCase       :				List[Any]      			= re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"			,_SCREAMING_SNAKE_CASE   )
       return [m.group(0   ) for m in matches]

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			List[Any]:
       lowerCamelCase       :				Optional[Any]      			= 2 if text == '''✅''' or text == '''❌''' else len(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Optional[int]      			= (width - text_length) // 2
       lowerCamelCase       :				Tuple      			= width - text_length - left_indent
       return " " * left_indent + text + " " * right_indent

def 					A				(				)     ->			int:
       lowerCamelCase       :				Dict      			= diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       lowerCamelCase       :				int      			= {
           name: config_mapping_names[code]
           for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
           if code in config_mapping_names
       }
       lowerCamelCase       :				str      			= {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.
       lowerCamelCase       :				List[str]      			= collections.defaultdict(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Union[str, Any]      			= collections.defaultdict(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Optional[Any]      			= collections.defaultdict(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= collections.defaultdict(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Tuple      			= collections.defaultdict(_SCREAMING_SNAKE_CASE   )

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

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

              # Let's build that table!
       lowerCamelCase       :				Optional[int]      			= list(model_name_to_config.keys()   )
       model_names.sort(key=str.lower   )
       lowerCamelCase       :				Optional[Any]      			= ['''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).
       lowerCamelCase       :				Optional[Any]      			= [len(_SCREAMING_SNAKE_CASE   ) + 2 for c in columns]
       lowerCamelCase       :				Tuple      			= max([len(_SCREAMING_SNAKE_CASE   ) for name in model_names]   ) + 2

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

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

def 					A				(				_SCREAMING_SNAKE_CASE=False   )     ->			Tuple:
       lowerCamelCase       :				List[str]      			= _find_text_in_file(
           filename=os.path.join(_SCREAMING_SNAKE_CASE			,"index.mdx"   )			,start_prompt="<!--This table is updated automatically from the auto modules"			,end_prompt="<!-- End table-->"			,)
       lowerCamelCase       :				Optional[Any]      			= get_model_table_from_auto_modules()

       if current_table != new_table:
              if overwrite:
                     with open(os.path.join(_SCREAMING_SNAKE_CASE			,"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__":
       SCREAMING_SNAKE_CASE__	:				Dict  =       argparse.ArgumentParser()
       parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
       SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       parser.parse_args()

       check_model_table(args.fix_and_overwrite)












import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def 					A				(				*_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				List[str]      			= torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''   )
       with patch_environment(
           world_size=_SCREAMING_SNAKE_CASE			,master_addr="127.0.01"			,master_port="29500"			,mixed_precision=args[1].mixed_precision   ):
              lowerCamelCase       :				Union[str, Any]      			= PrepareForLaunch(_SCREAMING_SNAKE_CASE			,distributed_type="MULTI_GPU"   )
              torch.multiprocessing.start_processes(_SCREAMING_SNAKE_CASE			,args=_SCREAMING_SNAKE_CASE			,nprocs=_SCREAMING_SNAKE_CASE			,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
       SCREAMING_SNAKE_CASE__	:				Tuple  =       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
       SCREAMING_SNAKE_CASE__	:				int  =       [
           'pip:./',
           'transformers',
           'datasets',
           'evaluate',
           'tqdm',
           'scipy',
           'scikit-learn',
           'tensorboard',
           'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
       ]
       SCREAMING_SNAKE_CASE__	:				Dict  =       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

       # Define train args/config, run train function
       SCREAMING_SNAKE_CASE__	:				int  =       argparse.Namespace(cpu=False, mixed_precision='fp16')
       SCREAMING_SNAKE_CASE__	:				int  =       {'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'])






