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

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





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			2 if text == "✅" or text == "❌" else len(SCREAMING_SNAKE_CASE_    )
				lowerCamelCase_						=			(width - text_length) // 2
				lowerCamelCase_						=			width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent





def 			lowerCamelCase_ (				):
				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(SCREAMING_SNAKE_CASE_    )
				lowerCamelCase_						=			collections.defaultdict(SCREAMING_SNAKE_CASE_    )
				lowerCamelCase_						=			collections.defaultdict(SCREAMING_SNAKE_CASE_    )
				lowerCamelCase_						=			collections.defaultdict(SCREAMING_SNAKE_CASE_    )
				lowerCamelCase_						=			collections.defaultdict(SCREAMING_SNAKE_CASE_    )

				# Let's lookup through all transformers object (once).
				for attr_name in dir(SCREAMING_SNAKE_CASE_    ):
								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[:-1_3]
								elif _re_tf_models.match(SCREAMING_SNAKE_CASE_    ) is not None:
												lowerCamelCase_						=			tf_models
												lowerCamelCase_						=			_re_tf_models.match(SCREAMING_SNAKE_CASE_    ).groups()[0]
								elif _re_flax_models.match(SCREAMING_SNAKE_CASE_    ) is not None:
												lowerCamelCase_						=			flax_models
												lowerCamelCase_						=			_re_flax_models.match(SCREAMING_SNAKE_CASE_    ).groups()[0]
								elif _re_pt_models.match(SCREAMING_SNAKE_CASE_    ) is not None:
												lowerCamelCase_						=			pt_models
												lowerCamelCase_						=			_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_						=			True
																				break
																# Try again after removing the last word in the name
																lowerCamelCase_						=			"".join(camel_case_split(SCREAMING_SNAKE_CASE_    )[:-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(SCREAMING_SNAKE_CASE_    ) + 2 for c in columns]
				lowerCamelCase_						=			max([len(SCREAMING_SNAKE_CASE_    ) for name in model_names]    ) + 2

				# Build the table per se
				lowerCamelCase_						=			"|" + "|".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_						=			{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(SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_    ) for l, w in zip(SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_    )]    ) + "|\n"
				return table





def 			lowerCamelCase_ (				lowerCamelCase__=False    ):
				lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			_find_text_in_file(
				    filename=os.path.join(SCREAMING_SNAKE_CASE_	, "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(SCREAMING_SNAKE_CASE_	, "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)






"""simple docstring"""




import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam
class 					lowerCAmelCase	(					datasets.BeamBasedBuilder    ):




		'''simple docstring'''







		def 							__A						(	self				)     ->							Dict:
								return datasets.DatasetInfo(
								    features=datasets.Features({'content': datasets.Value('string'				)}				)						,       supervised_keys=lowerCAmelCase__						,       )







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							str:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,       gen_kwargs={'examples': get_test_dummy_examples()}				)]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Optional[int]:
								import apache_beam as beam

								return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase__				)





class 					lowerCAmelCase	(					datasets.BeamBasedBuilder    ):




		'''simple docstring'''







		def 							__A						(	self				)     ->							int:
								return datasets.DatasetInfo(
								    features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'				)}				)}				)						,       supervised_keys=lowerCAmelCase__						,       )







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Dict:
								return [
								    datasets.SplitGenerator(name=datasets.Split.TRAIN						,       gen_kwargs={'examples': get_test_nested_examples()}				)
								]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Optional[Any]:
								import apache_beam as beam

								return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase__				)





def     lowercase			()   ->						str:
						return [(i, {"content": content}) for i, content in enumerate(['foo', 'bar', 'foobar']  )]


def     lowercase			()   ->						Optional[Any]:
						return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['foo', 'bar', 'foobar']  )]


class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		@require_beam
		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					len(get_test_dummy_examples()				)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					DummyBeamDataset(cache_dir=lowerCAmelCase__						,       beam_runner='DirectRunner'				)
														builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train.arrow'				)				)				)
														self.assertDictEqual(builder.info.features						,       datasets.Features({'content': datasets.Value('string'				)}				)				)
														SCREAMING_SNAKE_CASE													=					builder.as_dataset()
														self.assertEqual(dset['train'].num_rows						,       lowerCAmelCase__				)
														self.assertEqual(dset['train'].info.splits['train'].num_examples						,       lowerCAmelCase__				)
														self.assertDictEqual(dset['train'][0]						,       get_test_dummy_examples()[0][1]				)
														self.assertDictEqual(
														    dset['train'][expected_num_examples - 1]						,       get_test_dummy_examples()[expected_num_examples - 1][1]				)
														self.assertTrue(
														    os.path.exists(os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       'dataset_info.json'				)				)				)
														del dset







		@require_beam
		def 							__A						(	self				)     ->							int:
								import apache_beam as beam

								SCREAMING_SNAKE_CASE													=					beam.io.parquetio.WriteToParquet

								SCREAMING_SNAKE_CASE													=					len(get_test_dummy_examples()				)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					DummyBeamDataset(cache_dir=lowerCAmelCase__						,       beam_runner='DirectRunner'				)
														with patch('apache_beam.io.parquetio.WriteToParquet'				) as write_parquet_mock:
																				SCREAMING_SNAKE_CASE													=					partial(lowerCAmelCase__						,       num_shards=2				)
																				builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(
														            lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train-00000-of-00002.arrow'				)				)				)
														self.assertTrue(
														    os.path.exists(
														        os.path.join(
														            lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train-00000-of-00002.arrow'				)				)				)
														self.assertDictEqual(builder.info.features						,       datasets.Features({'content': datasets.Value('string'				)}				)				)
														SCREAMING_SNAKE_CASE													=					builder.as_dataset()
														self.assertEqual(dset['train'].num_rows						,       lowerCAmelCase__				)
														self.assertEqual(dset['train'].info.splits['train'].num_examples						,       lowerCAmelCase__				)
														# Order is not preserved when sharding, so we just check that all the elements are there
														self.assertListEqual(sorted(dset['train']['content']				)						,       sorted(['foo', 'bar', 'foobar']				)				)
														self.assertTrue(
														    os.path.exists(os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       'dataset_info.json'				)				)				)
														del dset







		@require_beam
		def 							__A						(	self				)     ->							Optional[Any]:
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					DummyBeamDataset(cache_dir=lowerCAmelCase__				)
														self.assertRaises(datasets.builder.MissingBeamOptions						,       builder.download_and_prepare				)







		@require_beam
		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					len(get_test_nested_examples()				)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					NestedBeamDataset(cache_dir=lowerCAmelCase__						,       beam_runner='DirectRunner'				)
														builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train.arrow'				)				)				)
														self.assertDictEqual(
														    builder.info.features						,       datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'				)}				)}				)				)
														SCREAMING_SNAKE_CASE													=					builder.as_dataset()
														self.assertEqual(dset['train'].num_rows						,       lowerCAmelCase__				)
														self.assertEqual(dset['train'].info.splits['train'].num_examples						,       lowerCAmelCase__				)
														self.assertDictEqual(dset['train'][0]						,       get_test_nested_examples()[0][1]				)
														self.assertDictEqual(
														    dset['train'][expected_num_examples - 1]						,       get_test_nested_examples()[expected_num_examples - 1][1]				)
														self.assertTrue(
														    os.path.exists(os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       'dataset_info.json'				)				)				)
														del dset


