
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
_lowerCamelCase :       Any     					=    '''src/diffusers'''
_lowerCamelCase :       Any     					=    '''docs/source/en'''
_lowerCamelCase :       Dict     					=    '''.'''






def  a_	( __lowercase     :			List[Any] ,      __lowercase     :			Optional[Any] ,      __lowercase     :			Optional[int]				)				->						Dict:
			with open(__lowercase ,      'r' ,      encoding='utf-8' ,      newline='\n'				) as f:
						_snake_case						=    f.readlines()
			# Find the start prompt.
			_snake_case						=    0
			while not lines[start_index].startswith(__lowercase				):
						start_index += 1
			start_index += 1

			_snake_case						=    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 :       List[Any]     					=    '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
_lowerCamelCase :       Tuple     					=    re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
_lowerCamelCase :       int     					=    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 :       Dict     					=    re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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






def  a_	( __lowercase     :			Tuple				)				->						Tuple:
			_snake_case						=    re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)' ,      __lowercase				)
			return [m.group(0				) for m in matches]






def  a_	( __lowercase     :			Optional[Any] ,      __lowercase     :			List[str]				)				->						List[str]:
			_snake_case						=    2 if text == '✅' or text == '❌' else len(__lowercase				)
			_snake_case						=    (width - text_length) // 2
			_snake_case						=    width - text_length - left_indent
			return " " * left_indent + text + " " * right_indent






def  a_	( )				->						str:
			_snake_case						=    diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			_snake_case						=    {
			    name: config_mapping_names[code]
			    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
			    if code in config_mapping_names
			}
			_snake_case						=    {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.
			_snake_case						=    collections.defaultdict(__lowercase				)
			_snake_case						=    collections.defaultdict(__lowercase				)
			_snake_case						=    collections.defaultdict(__lowercase				)
			_snake_case						=    collections.defaultdict(__lowercase				)
			_snake_case						=    collections.defaultdict(__lowercase				)

			# Let's lookup through all diffusers object (once).
			for attr_name in dir(__lowercase				):
						_snake_case						=    None
						if attr_name.endswith('Tokenizer'				):
									_snake_case						=    slow_tokenizers
									_snake_case						=    attr_name[:-9]
						elif attr_name.endswith('TokenizerFast'				):
									_snake_case						=    fast_tokenizers
									_snake_case						=    attr_name[:-13]
						elif _re_tf_models.match(__lowercase				) is not None:
									_snake_case						=    tf_models
									_snake_case						=    _re_tf_models.match(__lowercase				).groups()[0]
						elif _re_flax_models.match(__lowercase				) is not None:
									_snake_case						=    flax_models
									_snake_case						=    _re_flax_models.match(__lowercase				).groups()[0]
						elif _re_pt_models.match(__lowercase				) is not None:
									_snake_case						=    pt_models
									_snake_case						=    _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():
															_snake_case						=    True
															break
												# Try again after removing the last word in the name
												_snake_case						=    ''.join(camel_case_split(__lowercase				)[:-1]				)

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

			# Build the table per se
			_snake_case						=    '|' + '|'.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"

			_snake_case						=    {True: '✅', False: '❌'}
			for name in model_names:
						_snake_case						=    model_name_to_prefix[name]
						_snake_case						=    [
						    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  a_	( __lowercase     :			Dict=False				)				->						int:
			_snake_case			,      _snake_case			,      _snake_case			,      _snake_case						=    _find_text_in_file(
			    filename=os.path.join(__lowercase ,      'index.mdx'				) ,      start_prompt='<!--This table is updated automatically from the auto modules' ,      end_prompt='<!-- End table-->' ,      )
			_snake_case						=    get_model_table_from_auto_modules()

			if current_table != new_table:
						if overwrite:
									with open(os.path.join(__lowercase ,      '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__":
			_lowerCamelCase :       str     					=    argparse.ArgumentParser()
			parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
			_lowerCamelCase :       List[str]     					=    parser.parse_args()

			check_model_table(args.fix_and_overwrite)

import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def  a_	( __lowercase     :			Optional[Any] ,      __lowercase     :			Union[str, Any] ,      __lowercase     :			str				)				->						Any:
			# Construct configuration depending on size
			_snake_case						=    {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_snake_case			,      _snake_case			,      _snake_case						=    MODELS[model_size]  # set model hyperparameters
			_snake_case						=    ImageGPTConfig(n_embd=__lowercase ,      n_layer=__lowercase ,      n_head=__lowercase				)
			_snake_case						=    ImageGPTForCausalLM(__lowercase				)

			# Load weights from numpy
			load_tf_weights_in_imagegpt(__lowercase ,      __lowercase ,      __lowercase				)

			# Save pytorch-model
			_snake_case						=    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_snake_case						=    pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''				)
			torch.save(model.state_dict() ,      __lowercase				)
			print(f'''Save configuration file to {pytorch_config_dump_path}'''				)
			with open(__lowercase ,      'w' ,      encoding='utf-8'				) as f:
						f.write(config.to_json_string()				)


if __name__ == "__main__":
			_lowerCamelCase :       List[Any]     					=    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--imagegpt_checkpoint_path''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Path to the TensorFlow checkpoint path.''',
			)
			parser.add_argument(
			    '''--model_size''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Size of the model (can be either \'small\', \'medium\' or \'large\').''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			_lowerCamelCase :       Optional[Any]     					=    parser.parse_args()
			convert_imagegpt_checkpoint_to_pytorch(
			    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
			)