
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
_A   =     "src/transformers"
_A   =     "docs/source/en"
_A   =     "."






def    lowerCamelCase__				(				__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  Optional[int]				):




			"""simple docstring"""


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

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


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






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[Any]				):




			"""simple docstring"""


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






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  Optional[int]				):




			"""simple docstring"""


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






def    lowerCamelCase__				(				):




			"""simple docstring"""


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

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

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

			# Build the table per se
			lowerCAmelCase_						=	"|" + "|".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"

			lowerCAmelCase_						=	{True: "✅", False: "❌"}
			for name in model_names:
						lowerCAmelCase_						=	model_name_to_prefix[name]
						lowerCAmelCase_						=	[
						    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    lowerCamelCase__				(				__lowerCAmelCase  :  Tuple=False				):




			"""simple docstring"""


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

			if current_table != new_table:
						if overwrite:
									with open(os.path.join(__lowerCAmelCase    ,							"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__":
			_A   =     argparse.ArgumentParser()
			parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
			_A   =     parser.parse_args()

			check_model_table(args.fix_and_overwrite)





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase=2				,				_UpperCamelCase=3				,				_UpperCamelCase=64				,				_UpperCamelCase=None     )				->      Optional[Any]:
							lowerCAmelCase_						=	np.random.default_rng(_UpperCamelCase     )
							lowerCAmelCase_						=	length
							lowerCAmelCase_						=	rng.normal(size=(length,)     ).astype(np.floataa     )
							lowerCAmelCase_						=	a * self.x + b + rng.normal(scale=0.1				,				size=(length,)     ).astype(np.floataa     )




				def __len__(					self     )				->      int:
							return self.length




				def __getitem__(					self				,				_UpperCamelCase     )				->      Dict:
							return {"x": self.x[i], "y": self.y[i]}





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      List[Any]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      Any:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a[0] + self.b[0]





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      Optional[int]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      int:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a + self.b






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  int = 16				):




			"""simple docstring"""


			from datasets import load_dataset
			from transformers import AutoTokenizer

			lowerCAmelCase_						=	AutoTokenizer.from_pretrained("bert-base-cased"				)
			lowerCAmelCase_						=	{"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
			lowerCAmelCase_						=	load_dataset("csv"    ,							data_files=__lowerCAmelCase				)
			lowerCAmelCase_						=	datasets["train"].unique("label"				)

			lowerCAmelCase_						=	{v: i for i, v in enumerate(__lowerCAmelCase				)}

			def tokenize_function(__lowerCAmelCase  :  List[Any]				):
						# max_length=None => use the model max length (it's actually the default)
						lowerCAmelCase_						=	tokenizer(
						    examples["sentence1"]    ,							examples["sentence2"]    ,							truncation=__lowerCAmelCase    ,							max_length=__lowerCAmelCase    ,							padding="max_length"				)
						if "label" in examples:
									lowerCAmelCase_						=	[label_to_id[l] for l in examples["label"]]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			lowerCAmelCase_						=	datasets.map(
			    __lowerCAmelCase    ,							batched=__lowerCAmelCase    ,							remove_columns=["sentence1", "sentence2", "label"]    ,							)

			def collate_fn(__lowerCAmelCase  :  Optional[int]				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(__lowerCAmelCase    ,							padding="max_length"    ,							max_length=128    ,							return_tensors="pt"				)
						return tokenizer.pad(__lowerCAmelCase    ,							padding="longest"    ,							return_tensors="pt"				)

			# Instantiate dataloaders.
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["train"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=2				)
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["validation"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=1				)

			return train_dataloader, eval_dataloader



