






import argparse
import os
import re

import packaging.version


__snake_case      :      str             =       """examples/"""
__snake_case      :      Optional[Any]             =       {
    """examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
    """init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
    """setup""": (re.compile(R"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R"""\1version=\"VERSION\","""),
    """doc""": (re.compile(R"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
__snake_case      :      Optional[Any]             =       {
    """init""": """src/transformers/__init__.py""",
    """setup""": """setup.py""",
}
__snake_case      :      List[Any]             =       """README.md"""







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   List[str]						)					->				Dict:

						"""simple docstring"""



						with open(UpperCamelCase_      ,    'r'      ,    encoding='utf-8'      ,    newline='\n'						) as f:
												lowerCAmelCase__        =  f.read()
						lowerCAmelCase__  ,    lowerCAmelCase__        =  REPLACE_PATTERNS[pattern]
						lowerCAmelCase__        =  replace.replace('VERSION'      ,    UpperCamelCase_						)
						lowerCAmelCase__        =  re_pattern.sub(UpperCamelCase_      ,    UpperCamelCase_						)
						with open(UpperCamelCase_      ,    'w'      ,    encoding='utf-8'      ,    newline='\n'						) as f:
												f.write(UpperCamelCase_						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				int:

						"""simple docstring"""



						for folder, directories, fnames in os.walk(UpperCamelCase_						):
												# Removing some of the folders with non-actively maintained examples from the walk
												if "research_projects" in directories:
																		directories.remove('research_projects'						)
												if "legacy" in directories:
																		directories.remove('legacy'						)
												for fname in fnames:
																		if fname.endswith('.py'						):
																								update_version_in_file(os.path.join(UpperCamelCase_      ,    UpperCamelCase_						)      ,    UpperCamelCase_      ,    pattern='examples'						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   Optional[Any]=False						)					->				Tuple:

						"""simple docstring"""



						for pattern, fname in REPLACE_FILES.items():
												update_version_in_file(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
						if not patch:
												update_version_in_examples(UpperCamelCase_						)







def 		_UpperCamelCase				(      )					->				List[str]:

						"""simple docstring"""



						lowerCAmelCase__        =  '🤗 Transformers currently provides the following architectures'
						lowerCAmelCase__        =  '1. Want to contribute a new model?'
						with open(UpperCamelCase_      ,    'r'      ,    encoding='utf-8'      ,    newline='\n'						) as f:
												lowerCAmelCase__        =  f.readlines()

						# Find the start of the list.
						lowerCAmelCase__        =  0
						while not lines[start_index].startswith(_start_prompt						):
												start_index += 1
						start_index += 1

						lowerCAmelCase__        =  start_index
						# Update the lines in the model list.
						while not lines[index].startswith(_end_prompt						):
												if lines[index].startswith('1.'						):
																		lowerCAmelCase__        =  lines[index].replace(
																		    'https://huggingface.co/docs/transformers/main/model_doc'      ,    'https://huggingface.co/docs/transformers/model_doc'      ,    )
												index += 1

						with open(UpperCamelCase_      ,    'w'      ,    encoding='utf-8'      ,    newline='\n'						) as f:
												f.writelines(UpperCamelCase_						)







def 		_UpperCamelCase				(      )					->				Optional[Any]:

						"""simple docstring"""



						with open(REPLACE_FILES['init']      ,    'r'						) as f:
												lowerCAmelCase__        =  f.read()
						lowerCAmelCase__        =  REPLACE_PATTERNS['init'][0].search(UpperCamelCase_						).groups()[0]
						return packaging.version.parse(UpperCamelCase_						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]=False						)					->				str:

						"""simple docstring"""



						lowerCAmelCase__        =  get_version()
						if patch and default_version.is_devrelease:
												raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!'						)
						if default_version.is_devrelease:
												lowerCAmelCase__        =  default_version.base_version
						elif patch:
												lowerCAmelCase__        =  F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
						else:
												lowerCAmelCase__        =  F"{default_version.major}.{default_version.minor + 1}.0"

						# Now let's ask nicely if that's the right one.
						lowerCAmelCase__        =  input(F"Which version are you releasing? [{default_version}]"						)
						if len(UpperCamelCase_						) == 0:
												lowerCAmelCase__        =  default_version

						print(F"Updating version to {version}."						)
						global_version_update(UpperCamelCase_      ,    patch=UpperCamelCase_						)
						if not patch:
												print('Cleaning main README, don\'t forget to run `make fix-copies`.'						)
												clean_main_ref_in_model_list()







def 		_UpperCamelCase				(      )					->				List[Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  get_version()
						lowerCAmelCase__        =  F"{current_version.major}.{current_version.minor + 1}.0.dev0"
						lowerCAmelCase__        =  current_version.base_version

						# Check with the user we got that right.
						lowerCAmelCase__        =  input(F"Which version are we developing now? [{dev_version}]"						)
						if len(UpperCamelCase_						) == 0:
												lowerCAmelCase__        =  dev_version

						print(F"Updating version to {version}."						)
						global_version_update(UpperCamelCase_						)
						print('Cleaning main README, don\'t forget to run `make fix-copies`.'						)
						clean_main_ref_in_model_list()


if __name__ == "__main__":
					__snake_case      :      Dict             =       argparse.ArgumentParser()
					parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
					parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
					__snake_case      :      Tuple             =       parser.parse_args()
					if not args.post_release:
										pre_release_work(patch=args.patch)
					elif args.patch:
										print("""Nothing to do after a patch :-)""")
					else:
										post_release_work()













import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


__snake_case      :      Tuple             =       """sshleifer/mar_enro_6_3_student"""


class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												super().setUp()

												lowerCAmelCase__        =  cached_path(
												    'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'       ,  extract_compressed_file=_UpperCamelCase       ,  )
												lowerCAmelCase__        =  F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"






						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												MarianMTModel.from_pretrained(_UpperCamelCase				)







						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  {
												    '$MAX_LEN': 64,
												    '$BS': 64,
												    '$GAS': 1,
												    '$ENRO_DIR': self.data_dir,
												    'facebook/mbart-large-cc25': MARIAN_MODEL,
												    # "val_check_interval=0.25": "val_check_interval=1.0",
												    '--learning_rate=3e-5': '--learning_rate 3e-4',
												    '--num_train_epochs 6': '--num_train_epochs 1',
												}

												# Clean up bash script
												lowerCAmelCase__        =  (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'				)[1].strip()
												lowerCAmelCase__        =  bash_script.replace('\\\n'       ,  ''				).strip().replace('"$@"'       ,  ''				)
												for k, v in env_vars_to_replace.items():
																		lowerCAmelCase__        =  bash_script.replace(_UpperCamelCase       ,  str(_UpperCamelCase				)				)
												lowerCAmelCase__        =  self.get_auto_remove_tmp_dir()

												# bash_script = bash_script.replace("--fp16 ", "")
												lowerCAmelCase__        =  F"\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        ".split()
												# XXX: args.gpus > 1 : handle multi_gpu in the future

												lowerCAmelCase__        =  ['finetune.py'] + bash_script.split() + args
												with patch.object(_UpperCamelCase       ,  'argv'       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  argparse.ArgumentParser()
																		lowerCAmelCase__        =  pl.Trainer.add_argparse_args(_UpperCamelCase				)
																		lowerCAmelCase__        =  SummarizationModule.add_model_specific_args(_UpperCamelCase       ,  os.getcwd()				)
																		lowerCAmelCase__        =  parser.parse_args()
																		lowerCAmelCase__        =  main(_UpperCamelCase				)

												# Check metrics
												lowerCAmelCase__        =  load_json(model.metrics_save_path				)
												lowerCAmelCase__        =  metrics['val'][0]
												lowerCAmelCase__        =  metrics['val'][-1]
												self.assertEqual(len(metrics['val']				)       ,  (args.max_epochs / args.val_check_interval)				)
												assert isinstance(last_step_stats[F"val_avg_{model.val_metric}"]       ,  _UpperCamelCase				)

												self.assertGreater(last_step_stats['val_avg_gen_time']       ,  0.01				)
												# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
												self.assertLessEqual(last_step_stats['val_avg_gen_time']       ,  1.0				)

												# test learning requirements:

												# 1. BLEU improves over the course of training by more than 2 pts
												self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu']       ,  2				)

												# 2. BLEU finishes above 17
												self.assertGreater(last_step_stats['val_avg_bleu']       ,  17				)

												# 3. test BLEU and val BLEU within ~1.1 pt.
												self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu']				)       ,  1.1				)

												# check lightning ckpt can be loaded and has a reasonable statedict
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  map_location='cpu'				)
												lowerCAmelCase__        =  'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
												assert expected_key in ckpt["state_dict"]
												assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

												# TODO: turn on args.do_predict when PL bug fixed.
												if args.do_predict:
																		lowerCAmelCase__        =  {os.path.basename(_UpperCamelCase				) for p in contents}
																		assert "test_generations.txt" in contents
																		assert "test_results.txt" in contents
																		# assert len(metrics["val"]) ==  desired_n_evals
																		assert len(metrics['test']				) == 1




class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						@timeout_decorator.timeout(6_00				)
						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
												lowerCAmelCase__        =  {
												    '--fp16_opt_level=O1': '',
												    '$MAX_LEN': 1_28,
												    '$BS': 16,
												    '$GAS': 1,
												    '$ENRO_DIR': data_dir,
												    '$m': 'sshleifer/student_marian_en_ro_6_1',
												    'val_check_interval=0.25': 'val_check_interval=1.0',
												}

												# Clean up bash script
												lowerCAmelCase__        =  (
												    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'				)[1].strip()
												)
												lowerCAmelCase__        =  bash_script.replace('\\\n'       ,  ''				).strip().replace('"$@"'       ,  ''				)
												lowerCAmelCase__        =  bash_script.replace('--fp16 '       ,  ' '				)

												for k, v in env_vars_to_replace.items():
																		lowerCAmelCase__        =  bash_script.replace(_UpperCamelCase       ,  str(_UpperCamelCase				)				)
												lowerCAmelCase__        =  self.get_auto_remove_tmp_dir()
												lowerCAmelCase__        =  bash_script.replace('--fp16'       ,  ''				)
												lowerCAmelCase__        =  6
												lowerCAmelCase__        =  (
												    ['distillation.py']
												    + bash_script.split()
												    + [
												        F"--output_dir={output_dir}",
												        '--gpus=1',
												        '--learning_rate=1e-3',
												        F"--num_train_epochs={epochs}",
												        '--warmup_steps=10',
												        '--val_check_interval=1.0',
												        '--do_predict',
												    ]
												)
												with patch.object(_UpperCamelCase       ,  'argv'       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  argparse.ArgumentParser()
																		lowerCAmelCase__        =  pl.Trainer.add_argparse_args(_UpperCamelCase				)
																		lowerCAmelCase__        =  SummarizationDistiller.add_model_specific_args(_UpperCamelCase       ,  os.getcwd()				)
																		lowerCAmelCase__        =  parser.parse_args()
																		# assert args.gpus == gpus THIS BREAKS for multi_gpu

																		lowerCAmelCase__        =  distill_main(_UpperCamelCase				)

												# Check metrics
												lowerCAmelCase__        =  load_json(model.metrics_save_path				)
												lowerCAmelCase__        =  metrics['val'][0]
												lowerCAmelCase__        =  metrics['val'][-1]
												assert len(metrics['val']				) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

												assert last_step_stats["val_avg_gen_time"] >= 0.01

												assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
												assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
												assert isinstance(last_step_stats[F"val_avg_{model.val_metric}"]       ,  _UpperCamelCase				)

												# check lightning ckpt can be loaded and has a reasonable statedict
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  map_location='cpu'				)
												lowerCAmelCase__        =  'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
												assert expected_key in ckpt["state_dict"]
												assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

												# TODO: turn on args.do_predict when PL bug fixed.
												if args.do_predict:
																		lowerCAmelCase__        =  {os.path.basename(_UpperCamelCase				) for p in contents}
																		assert "test_generations.txt" in contents
																		assert "test_results.txt" in contents
																		# assert len(metrics["val"]) ==  desired_n_evals
																		assert len(metrics['test']				) == 1





