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
__UpperCamelCase	: int    		=  'src/transformers'
__UpperCamelCase	: Optional[Any]    		=  'docs/source/en'
__UpperCamelCase	: Union[str, Any]    		=  '.'
def    A			(					_lowercase	,						_lowercase	,						_lowercase		)     ->   Dict:
						with open(_lowercase	,						'''r'''	,						encoding='''utf-8'''	,						newline='''\n'''		) as f:
												SCREAMING_SNAKE_CASE							:					Any           =							f.readlines()
						# Find the start prompt.
						SCREAMING_SNAKE_CASE							:					List[Any]           =							0
						while not lines[start_index].startswith(_lowercase		):
												start_index += 1
						start_index += 1

						SCREAMING_SNAKE_CASE							:					Tuple           =							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 |
__UpperCamelCase	: Tuple    		=  'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__UpperCamelCase	: Union[str, Any]    		=  re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__UpperCamelCase	: Dict    		=  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.
__UpperCamelCase	: Tuple    		=  re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
__UpperCamelCase	: List[Any]    		=  direct_transformers_import(TRANSFORMERS_PATH)
def    A			(					_lowercase		)     ->   str:
						SCREAMING_SNAKE_CASE							:					Tuple           =							re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''	,						_lowercase		)
						return [m.group(0		) for m in matches]
def    A			(					_lowercase	,						_lowercase		)     ->   Any:
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							2 if text == '''✅''' or text == '''❌''' else len(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Tuple           =							(width - text_length) // 2
						SCREAMING_SNAKE_CASE							:					List[str]           =							width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent
def    A			(					)     ->   Union[str, Any]:
						SCREAMING_SNAKE_CASE							:					Tuple           =							transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						SCREAMING_SNAKE_CASE							:					List[Any]           =							{
						    name: config_maping_names[code]
						    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_maping_names
						}
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							{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.
						SCREAMING_SNAKE_CASE							:					List[Any]           =							collections.defaultdict(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							collections.defaultdict(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							collections.defaultdict(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Any           =							collections.defaultdict(_lowercase		)
						SCREAMING_SNAKE_CASE							:					int           =							collections.defaultdict(_lowercase		)

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

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

						# Build the table per se
						SCREAMING_SNAKE_CASE							:					Any           =							'''|''' + '''|'''.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"

						SCREAMING_SNAKE_CASE							:					str           =							{True: '''✅''', False: '''❌'''}
						for name in model_names:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							model_name_to_prefix[name]
												SCREAMING_SNAKE_CASE							:					List[Any]           =							[
												    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=False		)     ->   Union[str, Any]:
						SCREAMING_SNAKE_CASE							:					int           =							_find_text_in_file(
						    filename=os.path.join(_lowercase	,						'''index.md'''		)	,						start_prompt='''<!--This table is updated automatically from the auto modules'''	,						end_prompt='''<!-- End table-->'''	,						)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							get_model_table_from_auto_modules()

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

					check_model_table(args.fix_and_overwrite)






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
					from sagemaker import Session, TrainingJobAnalytics
					from sagemaker.huggingface import HuggingFace





@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER"""			,							"""False""")) is not True			,							reason="""Skipping test because should only be run when releasing minor transformers version"""			,							)
@pytest.mark.usefixtures("""sm_env""")
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ])
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Any		):





												'''simple docstring'''





												if self.framework == "pytorch":
																		subprocess.run(
																		    f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()					,	encoding='''utf-8'''					,	check=UpperCamelCase__					,	)
												assert hasattr(self					,	'''env'''		)


						def  __A ( self			:       str					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							f"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
												# distributed data settings
												SCREAMING_SNAKE_CASE							:					Any           =							{'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None

												# creates estimator
												return HuggingFace(
												    entry_point=self.script					,	source_dir=self.env.test_path					,	role=self.env.role					,	image_uri=self.env.image_uri					,	base_job_name=UpperCamelCase__					,	instance_count=UpperCamelCase__					,	instance_type=self.instance_type					,	debugger_hook_config=UpperCamelCase__					,	hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}					,	metric_definitions=self.env.metric_definitions					,	distribution=UpperCamelCase__					,	py_version='''py36'''					,	)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												TrainingJobAnalytics(UpperCamelCase__		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)



						@parameterized.expand([(2,)]		)
						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.create_estimator(UpperCamelCase__		)

												# run training
												estimator.fit()

												# result dataframe
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

												# extract kpis
												SCREAMING_SNAKE_CASE							:					List[str]           =							list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']		)
												SCREAMING_SNAKE_CASE							:					Dict           =							list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']		)
												# get train time from SageMaker job, this includes starting, preprocessing, stopping
												SCREAMING_SNAKE_CASE							:					List[Any]           =							(
												    Session().describe_training_job(estimator.latest_training_job.name		).get('''TrainingTimeInSeconds'''					,	99_9999		)
												)

												# assert kpis
												assert train_runtime <= self.results["train_runtime"]
												assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy		)
												assert all(t <= self.results['''eval_loss'''] for t in eval_loss		)

												# dump tests result into json file to share in PR
												with open(f"""{estimator.latest_training_job.name}.json"""					,	'''w'''		) as outfile:
																		json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss}					,	UpperCamelCase__		)





