

import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

_UpperCAmelCase		:       int						    =	logging.getLogger()





def  __lowerCamelCase     (			):







							'''simple docstring'''






							snake_case_          =   argparse.ArgumentParser()
							parser.add_argument('-f'				)
							snake_case_          =   parser.parse_args()
							return args.f





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   {}
							snake_case_          =   os.path.join(UpperCamelCase__       ,							'all_results.json'				)
							if os.path.exists(UpperCamelCase__				):
														with open(UpperCamelCase__       ,							'r'				) as f:
																					snake_case_          =   json.load(UpperCamelCase__				)
							else:
														raise ValueError(F'''can\'t find {path}'''				)
							return results





def  __lowerCamelCase     (			):







							'''simple docstring'''






							snake_case_          =   torch.cuda.is_available() and torch_device == 'cuda'
							return is_using_cuda and is_apex_available()


_UpperCAmelCase		:       Union[str, Any]						    =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 			lowercase    (	lowercase_      ):




				@classmethod
				def    a							( cls	):
											# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
											snake_case_          =   tempfile.mkdtemp()
											snake_case_          =   os.path.join(cls.tmpdir				,       'default_config.yml'	)
											write_basic_config(save_location=cls.configPath	)
											snake_case_          =   ['accelerate', 'launch', '--config_file', cls.configPath]




				@classmethod
				def    a							( cls	):
											shutil.rmtree(cls.tmpdir	)




				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

											if is_cuda_and_apex_available():
																		testargs.append('--fp16'	)

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertGreaterEqual(result['eval_accuracy']				,       0.75	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'epoch_0'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'glue_no_trainer'	)	)	)




				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

											if torch.cuda.device_count() > 1:
																		# Skipping because there are not enough batches to train the model + would need a drop_last to work.
																		return

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertLess(result['perplexity']				,       100	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'epoch_0'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'clm_no_trainer'	)	)	)




				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertLess(result['perplexity']				,       42	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'epoch_0'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'mlm_no_trainer'	)	)	)




				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
											snake_case_          =   7 if get_gpu_count() > 1 else 2

											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertGreaterEqual(result['eval_accuracy']				,       0.75	)
											self.assertLess(result['train_loss']				,       0.5	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'epoch_0'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'ner_no_trainer'	)	)	)




				@unittest.skip(reason='Fix me @muellerzr'	)
				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
											self.assertGreaterEqual(result['eval_f1']				,       28	)
											self.assertGreaterEqual(result['eval_exact']				,       28	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'epoch_0'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'qa_no_trainer'	)	)	)




				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        '''.split()

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertGreaterEqual(result['eval_accuracy']				,       0.8	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'swag_no_trainer'	)	)	)




				@slow
				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertGreaterEqual(result['eval_rouge1']				,       10	)
											self.assertGreaterEqual(result['eval_rouge2']				,       2	)
											self.assertGreaterEqual(result['eval_rougeL']				,       7	)
											self.assertGreaterEqual(result['eval_rougeLsum']				,       7	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'epoch_0'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'summarization_no_trainer'	)	)	)




				@slow
				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertGreaterEqual(result['eval_bleu']				,       30	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'epoch_0'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'translation_no_trainer'	)	)	)




				@slow
				def    a							( self	):
											snake_case_          =   logging.StreamHandler(sys.stdout	)
											logger.addHandler(snake_case	)

											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        '''.split()

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertGreaterEqual(result['eval_overall_accuracy']				,       0.10	)




				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        '''.split()

											if is_cuda_and_apex_available():
																		testargs.append('--fp16'	)

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											# The base model scores a 25%
											self.assertGreaterEqual(result['eval_accuracy']				,       0.6	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'step_1'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'image_classification_no_trainer'	)	)	)







# Lint as: python3
import itertools
import os
import re


_UpperCAmelCase		:       str						    =	re.compile(R"""([A-Z]+)([A-Z][a-z])""")
_UpperCAmelCase		:       Dict						    =	re.compile(R"""([a-z\d])([A-Z])""")

_UpperCAmelCase		:       Dict						    =	re.compile(R"""(?<!_)_(?!_)""")
_UpperCAmelCase		:       Tuple						    =	re.compile(R"""(_{2,})""")

_UpperCAmelCase		:       Any						    =	R"""^\w+(\.\w+)*$"""

_UpperCAmelCase		:       List[str]						    =	R"""<>:/\|?*"""





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   _uppercase_uppercase_re.sub(r'\1_\2'       ,							UpperCamelCase__				)
							snake_case_          =   _lowercase_uppercase_re.sub(r'\1_\2'       ,							UpperCamelCase__				)
							return name.lower()





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   _single_underscore_re.split(UpperCamelCase__				)
							snake_case_          =   [_multiple_underscores_re.split(UpperCamelCase__				) for n in name]
							return "".join(n.capitalize() for n in itertools.chain.from_iterable(UpperCamelCase__				) if n != ''				)





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							if os.path.basename(UpperCamelCase__				) != name:
														raise ValueError(F'''Should be a dataset name, not a path: {name}'''				)
							return camelcase_to_snakecase(UpperCamelCase__				)





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							if os.path.basename(UpperCamelCase__				) != name:
														raise ValueError(F'''Should be a dataset name, not a path: {name}'''				)
							if not re.match(_split_re       ,							UpperCamelCase__				):
														raise ValueError(F'''Split name should match \'{_split_re}\'\' but got \'{split}\'.'''				)
							return F'''{filename_prefix_for_name(UpperCamelCase__				)}-{split}'''





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=None				):







							'''simple docstring'''






							snake_case_          =   filename_prefix_for_split(UpperCamelCase__       ,							UpperCamelCase__				)
							if filetype_suffix:
														prefix += F'''.{filetype_suffix}'''
							snake_case_          =   os.path.join(UpperCamelCase__       ,							UpperCamelCase__				)
							return F'''{filepath}*'''





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=None       ,							UpperCamelCase__=None				):







							'''simple docstring'''






							snake_case_          =   filename_prefix_for_split(UpperCamelCase__       ,							UpperCamelCase__				)
							snake_case_          =   os.path.join(UpperCamelCase__       ,							UpperCamelCase__				)

							if shard_lengths:
														snake_case_          =   len(UpperCamelCase__				)
														snake_case_          =   [F'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(UpperCamelCase__				)]
														if filetype_suffix:
																					snake_case_          =   [filename + F'''.{filetype_suffix}''' for filename in filenames]
														return filenames
							else:
														snake_case_          =   prefix
														if filetype_suffix:
																					filename += F'''.{filetype_suffix}'''
														return [filename]




