
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_						=						logging.getLogger()





def 				_lowerCamelCase      (       ):



				'''simple docstring'''







				A  :	List[str]				=					argparse.ArgumentParser()
				parser.add_argument('''-f'''		)
				A  :	List[str]				=					parser.parse_args()
				return args.f





def 				_lowerCamelCase      (       lowerCamelCase_:	List[str]		):



				'''simple docstring'''







				A  :	str				=					{}
				A  :	Union[str, Any]				=					os.path.join(_lowerCamelCase					,						'''all_results.json'''		)
				if os.path.exists(_lowerCamelCase		):
								with open(_lowerCamelCase					,						'''r'''		) as f:
												A  :	Dict				=					json.load(_lowerCamelCase		)
				else:
								raise ValueError(f"""can't find {path}"""		)
				return results





def 				_lowerCamelCase      (       ):



				'''simple docstring'''







				A  :	List[str]				=					torch.cuda.is_available() and torch_device == '''cuda'''
				return is_using_cuda and is_apex_available()


UpperCamelCase_						=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class      _SCREAMING_SNAKE_CASE				(		UpperCamelCase__				):





				@classmethod
				def 							_UpperCAmelCase    (		cls			:		List[Any]				):


								"""simple docstring"""

								A  :	Optional[Any]				=					tempfile.mkdtemp()
								A  :	int				=					os.path.join(cls.tmpdir       ,   '''default_config.yml'''				)
								write_basic_config(save_location=cls.configPath				)
								A  :	Union[str, Any]				=					['''accelerate''', '''launch''', '''--config_file''', cls.configPath]





				@classmethod
				def 							_UpperCAmelCase    (		cls			:		Tuple				):


								"""simple docstring"""

								shutil.rmtree(cls.tmpdir				)





				@mock.patch.dict(os.environ       ,   {'''WANDB_MODE''': '''offline'''}				)
				def 							_UpperCAmelCase    (		self			:		Union[str, Any]				):


								"""simple docstring"""

								A  :	List[Any]				=					self.get_auto_remove_tmp_dir()
								A  :	int				=					f"""\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

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

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





				@mock.patch.dict(os.environ       ,   {'''WANDB_MODE''': '''offline'''}				)
				def 							_UpperCAmelCase    (		self			:		List[Any]				):


								"""simple docstring"""

								A  :	str				=					self.get_auto_remove_tmp_dir()
								A  :	str				=					f"""\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        """.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				)
								A  :	List[str]				=					get_results(lowercase_				)
								self.assertLess(result['''perplexity''']       ,   100				)
								self.assertTrue(os.path.exists(os.path.join(lowercase_       ,   '''epoch_0'''				)				)				)
								self.assertTrue(os.path.exists(os.path.join(lowercase_       ,   '''clm_no_trainer'''				)				)				)





				@mock.patch.dict(os.environ       ,   {'''WANDB_MODE''': '''offline'''}				)
				def 							_UpperCAmelCase    (		self			:		Any				):


								"""simple docstring"""

								A  :	Union[str, Any]				=					self.get_auto_remove_tmp_dir()
								A  :	List[Any]				=					f"""\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

								run_command(self._launch_args + testargs				)
								A  :	List[str]				=					get_results(lowercase_				)
								self.assertLess(result['''perplexity''']       ,   42				)
								self.assertTrue(os.path.exists(os.path.join(lowercase_       ,   '''epoch_0'''				)				)				)
								self.assertTrue(os.path.exists(os.path.join(lowercase_       ,   '''mlm_no_trainer'''				)				)				)





				@mock.patch.dict(os.environ       ,   {'''WANDB_MODE''': '''offline'''}				)
				def 							_UpperCAmelCase    (		self			:		Optional[Any]				):


								"""simple docstring"""

								A  :	List[str]				=					7 if get_gpu_count() > 1 else 2

								A  :	Optional[Any]				=					self.get_auto_remove_tmp_dir()
								A  :	Union[str, Any]				=					f"""\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

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





				@unittest.skip(reason='''Fix me @muellerzr'''				)
				@mock.patch.dict(os.environ       ,   {'''WANDB_MODE''': '''offline'''}				)
				def 							_UpperCAmelCase    (		self			:		Any				):


								"""simple docstring"""

								A  :	Union[str, Any]				=					self.get_auto_remove_tmp_dir()
								A  :	Dict				=					f"""\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

								run_command(self._launch_args + testargs				)
								A  :	List[str]				=					get_results(lowercase_				)
								# 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(lowercase_       ,   '''epoch_0'''				)				)				)
								self.assertTrue(os.path.exists(os.path.join(lowercase_       ,   '''qa_no_trainer'''				)				)				)





				@mock.patch.dict(os.environ       ,   {'''WANDB_MODE''': '''offline'''}				)
				def 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

								A  :	Tuple				=					self.get_auto_remove_tmp_dir()
								A  :	str				=					f"""\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        """.split()

								run_command(self._launch_args + testargs				)
								A  :	List[str]				=					get_results(lowercase_				)
								self.assertGreaterEqual(result['''eval_accuracy''']       ,   0.8				)
								self.assertTrue(os.path.exists(os.path.join(lowercase_       ,   '''swag_no_trainer'''				)				)				)





				@slow
				@mock.patch.dict(os.environ       ,   {'''WANDB_MODE''': '''offline'''}				)
				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								A  :	Tuple				=					self.get_auto_remove_tmp_dir()
								A  :	Optional[int]				=					f"""\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

								run_command(self._launch_args + testargs				)
								A  :	Any				=					get_results(lowercase_				)
								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(lowercase_       ,   '''epoch_0'''				)				)				)
								self.assertTrue(os.path.exists(os.path.join(lowercase_       ,   '''summarization_no_trainer'''				)				)				)





				@slow
				@mock.patch.dict(os.environ       ,   {'''WANDB_MODE''': '''offline'''}				)
				def 							_UpperCAmelCase    (		self			:		Union[str, Any]				):


								"""simple docstring"""

								A  :	Tuple				=					self.get_auto_remove_tmp_dir()
								A  :	int				=					f"""\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

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





				@slow
				def 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

								A  :	Any				=					logging.StreamHandler(sys.stdout				)
								logger.addHandler(lowercase_				)

								A  :	Tuple				=					self.get_auto_remove_tmp_dir()
								A  :	Tuple				=					f"""\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        """.split()

								run_command(self._launch_args + testargs				)
								A  :	int				=					get_results(lowercase_				)
								self.assertGreaterEqual(result['''eval_overall_accuracy''']       ,   0.10				)






				@mock.patch.dict(os.environ       ,   {'''WANDB_MODE''': '''offline'''}				)
				def 							_UpperCAmelCase    (		self			:		str				):


								"""simple docstring"""

								A  :	str				=					self.get_auto_remove_tmp_dir()
								A  :	Tuple				=					f"""\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        """.split()

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

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






'''simple docstring'''

import string
from math import logaa





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str			):
    A__							=     document.translate(
        str.maketrans(""		,			""		,			string.punctuation			)			).replace("\n"		,			""			)
    A__							=     document_without_punctuation.split(" "			)  # word tokenization
    return len([word for word in tokenize_document if word.lower() == term.lower()]			)





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str			):
    A__							=     corpus.lower().translate(
        str.maketrans(""		,			""		,			string.punctuation			)			)  # strip all punctuation and replace it with ''
    A__							=     corpus_without_punctuation.split("\n"			)
    A__							=     term.lower()
    return (len([doc for doc in docs if term in doc]			), len(_lowerCamelCase			))





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   Tuple=False			):
    if smoothing:
        if n == 0:
            raise ValueError("log10(0) is undefined."			)
        return round(1 + logaa(n / (1 + df)			)		,			3			)

    if df == 0:
        raise ZeroDivisionError("df must be > 0"			)
    elif n == 0:
        raise ValueError("log10(0) is undefined."			)
    return round(logaa(n / df			)		,			3			)





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int			):
    return round(tf * idf		,			3			)



