






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
lowercase  							= "src/diffusers"
lowercase  							= "docs/source/en"
lowercase  							= "."





def 						__UpperCAmelCase      (					a_							,					a_							,					a_):
							with open(a_							,					'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(a_):
														start_index += 1
							start_index += 1

							snake_case_           =							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 |
lowercase  							= "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
lowercase  							= re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
lowercase  							= 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.
lowercase  							= re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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





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





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							2 if text == '✅' or text == '❌' else len(a_)
							snake_case_           =							(width - text_length) // 2
							snake_case_           =							width - text_length - left_indent
							return " " * left_indent + text + " " * right_indent





def 						__UpperCAmelCase      (					):
							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(a_)
							snake_case_           =							collections.defaultdict(a_)
							snake_case_           =							collections.defaultdict(a_)
							snake_case_           =							collections.defaultdict(a_)
							snake_case_           =							collections.defaultdict(a_)

							# Let's lookup through all diffusers object (once).
							for attr_name in dir(a_):
														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(a_) is not None:
																					snake_case_           =							tf_models
																					snake_case_           =							_re_tf_models.match(a_).groups()[0]
														elif _re_flax_models.match(a_) is not None:
																					snake_case_           =							flax_models
																					snake_case_           =							_re_flax_models.match(a_).groups()[0]
														elif _re_pt_models.match(a_) is not None:
																					snake_case_           =							pt_models
																					snake_case_           =							_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():
																																			snake_case_           =							True
																																			break
																												# Try again after removing the last word in the name
																												snake_case_           =							''.join(camel_case_split(a_)[:-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(a_) + 2 for c in columns]
							snake_case_           =							max([len(a_) for name in model_names]) + 2

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

							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(a_							,					a_) for l, w in zip(a_							,					a_)]) + "|\n"
							return table





def 						__UpperCAmelCase      (					a_=False):
							snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_           =							_find_text_in_file(
							    filename=os.path.join(a_							,					'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(a_							,					'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__":
	lowercase  							= argparse.ArgumentParser()
	parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
	lowercase  							= parser.parse_args()

	check_model_table(args.fix_and_overwrite)













import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
	import transformer_engine.pytorch as te





def 						__UpperCAmelCase      (					a_							,					a_=True							,					a_=True							,					a_=True):
							if not is_fpa_available():
														raise ImportError('Using `convert_model` requires transformer_engine to be installed.')
							for name, module in model.named_children():
														if isinstance(a_							,					nn.Linear) and to_transformer_engine and _convert_linear:
																					# Return early if the linear layer weights are not multiples of 16
																					if any(p % 16 != 0 for p in module.weight.shape):
																												return
																					snake_case_           =							module.bias is not None
																					snake_case_           =							te.Linear(
																					    module.in_features							,					module.out_features							,					bias=a_							,					params_dtype=module.weight.dtype)
																					snake_case_           =							module.weight.data.clone()
																					if has_bias:
																												snake_case_           =							module.bias.data.clone()

																					setattr(a_							,					a_							,					a_)
														elif isinstance(a_							,					nn.LayerNorm) and to_transformer_engine and _convert_ln:
																					snake_case_           =							te.LayerNorm(module.normalized_shape[0]							,					eps=module.eps							,					params_dtype=module.weight.dtype)
																					snake_case_           =							module.weight.data.clone()
																					snake_case_           =							module.bias.data.clone()

																					setattr(a_							,					a_							,					a_)
														elif isinstance(a_							,					te.Linear) and not to_transformer_engine and _convert_linear:
																					snake_case_           =							module.bias is not None
																					snake_case_           =							nn.Linear(
																					    module.in_features							,					module.out_features							,					bias=a_							,					params_dtype=module.weight.dtype)
																					snake_case_           =							module.weight.data.clone()
																					if has_bias:
																												snake_case_           =							module.bias.data.clone()

																					setattr(a_							,					a_							,					a_)
														elif isinstance(a_							,					te.LayerNorm) and not to_transformer_engine and _convert_ln:
																					snake_case_           =							nn.LayerNorm(module.normalized_shape[0]							,					eps=module.eps							,					params_dtype=module.weight.dtype)
																					snake_case_           =							module.weight.data.clone()
																					snake_case_           =							module.bias.data.clone()

																					setattr(a_							,					a_							,					a_)
														else:
																					convert_model(
																					    a_							,					to_transformer_engine=a_							,					_convert_linear=_convert_linear							,					_convert_ln=_convert_ln							,					)





def 						__UpperCAmelCase      (					a_):
							if not is_fpa_available():
														raise ImportError('Using `has_transformer_engine_layers` requires transformer_engine to be installed.')
							for m in model.modules():
														if isinstance(a_							,					(te.LayerNorm, te.Linear)):
																					return True
							return False





