



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)

a__    =	logging.getLogger()



def       lowercase       (   )  ->     List[Any]:
 _snake_case    :  Tuple					=					argparse.ArgumentParser()
 parser.add_argument("""-f"""				)
 _snake_case    :  List[Any]					=					parser.parse_args()
 return args.f



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				)  ->     Dict:
 _snake_case    :  Optional[Any]					=					{}
 _snake_case    :  int					=					os.path.join(SCREAMING_SNAKE_CASE__				,       """all_results.json"""				)
 if os.path.exists(SCREAMING_SNAKE_CASE__				):
  with open(SCREAMING_SNAKE_CASE__				,       """r"""				) as f:
   _snake_case    :  int					=					json.load(SCREAMING_SNAKE_CASE__				)
 else:
  raise ValueError(F'''can\'t find {path}'''				)
 return results



def       lowercase       (   )  ->     List[Any]:
 _snake_case    :  Dict					=					torch.cuda.is_available() and torch_device == """cuda"""
 return is_using_cuda and is_apex_available()


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







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   @classmethod
   def      UpperCamelCase_  (					cls							:			int)				->				Dict:

    """simple docstring"""







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

   @classmethod
   def      UpperCamelCase_  (					cls							:			List[Any])				->				Optional[int]:

    """simple docstring"""







    shutil.rmtree(cls.tmpdir)

   @mock.patch.dict(os.environ				,		{"""WANDB_MODE""": """offline"""})
   def      UpperCamelCase_  (					self							:			Any)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  Dict					=					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    :  Dict					=					get_results(lowerCAmelCase)
    self.assertGreaterEqual(result["""eval_accuracy"""]				,		0.75)
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""epoch_0""")))
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""glue_no_trainer""")))

   @mock.patch.dict(os.environ				,		{"""WANDB_MODE""": """offline"""})
   def      UpperCamelCase_  (					self							:			Tuple)				->				Tuple:

    """simple docstring"""







    _snake_case    :  str					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  Optional[Any]					=					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    :  Any					=					get_results(lowerCAmelCase)
    self.assertLess(result["""perplexity"""]				,		100)
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""epoch_0""")))
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""clm_no_trainer""")))

   @mock.patch.dict(os.environ				,		{"""WANDB_MODE""": """offline"""})
   def      UpperCamelCase_  (					self							:			Optional[int])				->				List[Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  Any					=					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    :  Optional[Any]					=					get_results(lowerCAmelCase)
    self.assertLess(result["""perplexity"""]				,		42)
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""epoch_0""")))
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""mlm_no_trainer""")))

   @mock.patch.dict(os.environ				,		{"""WANDB_MODE""": """offline"""})
   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				Tuple:

    """simple docstring"""







    _snake_case    :  int					=					7 if get_gpu_count() > 1 else 2

    _snake_case    :  Any					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  Dict					=					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    :  Union[str, Any]					=					get_results(lowerCAmelCase)
    self.assertGreaterEqual(result["""eval_accuracy"""]				,		0.75)
    self.assertLess(result["""train_loss"""]				,		0.5)
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""epoch_0""")))
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""ner_no_trainer""")))

   @unittest.skip(reason="""Fix me @muellerzr""")
   @mock.patch.dict(os.environ				,		{"""WANDB_MODE""": """offline"""})
   def      UpperCamelCase_  (					self							:			str)				->				int:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  Tuple					=					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    :  Dict					=					get_results(lowerCAmelCase)
    # 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(lowerCAmelCase				,		"""epoch_0""")))
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""qa_no_trainer""")))

   @mock.patch.dict(os.environ				,		{"""WANDB_MODE""": """offline"""})
   def      UpperCamelCase_  (					self							:			Any)				->				Union[str, Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  Union[str, Any]					=					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    :  int					=					get_results(lowerCAmelCase)
    self.assertGreaterEqual(result["""eval_accuracy"""]				,		0.8)
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""swag_no_trainer""")))

   @slow
   @mock.patch.dict(os.environ				,		{"""WANDB_MODE""": """offline"""})
   def      UpperCamelCase_  (					self							:			Optional[int])				->				Tuple:

    """simple docstring"""







    _snake_case    :  List[str]					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  int					=					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    :  str					=					get_results(lowerCAmelCase)
    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(lowerCAmelCase				,		"""epoch_0""")))
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""summarization_no_trainer""")))

   @slow
   @mock.patch.dict(os.environ				,		{"""WANDB_MODE""": """offline"""})
   def      UpperCamelCase_  (					self							:			Optional[int])				->				int:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  Optional[Any]					=					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    :  Dict					=					get_results(lowerCAmelCase)
    self.assertGreaterEqual(result["""eval_bleu"""]				,		30)
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""epoch_0""")))
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""translation_no_trainer""")))

   @slow
   def      UpperCamelCase_  (					self							:			Optional[int])				->				List[Any]:

    """simple docstring"""







    _snake_case    :  str					=					logging.StreamHandler(sys.stdout)
    logger.addHandler(lowerCAmelCase)

    _snake_case    :  List[Any]					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  Optional[int]					=					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    :  List[Any]					=					get_results(lowerCAmelCase)
    self.assertGreaterEqual(result["""eval_overall_accuracy"""]				,		0.10)


   @mock.patch.dict(os.environ				,		{"""WANDB_MODE""": """offline"""})
   def      UpperCamelCase_  (					self							:			str)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  Any					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  List[str]					=					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    :  Union[str, Any]					=					get_results(lowerCAmelCase)
    # The base model scores a 25%
    self.assertGreaterEqual(result["""eval_accuracy"""]				,		0.6)
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""step_1""")))
    self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase				,		"""image_classification_no_trainer""")))





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_camembert import CamembertTokenizer
else:
  a__    =	None


a__    =	logging.get_logger(__name__)

a__    =	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

a__    =	{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

a__    =	{
    """camembert-base""": 5_12,
}

a__    =	"""▁"""







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		VOCAB_FILES_NAMES
   snake_case_					: str          =		PRETRAINED_VOCAB_FILES_MAP
   snake_case_					: Optional[Any]          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_					: List[Any]          =		["""input_ids""", """attention_mask"""]
   snake_case_					: int          =		CamembertTokenizer

   def __init__(					self							:			Optional[int]				,		lowerCAmelCase							:			Optional[Any]=None				,		lowerCAmelCase							:			int=None				,		lowerCAmelCase							:			List[Any]="<s>"				,		lowerCAmelCase							:			List[str]="</s>"				,		lowerCAmelCase							:			Union[str, Any]="</s>"				,		lowerCAmelCase							:			Dict="<s>"				,		lowerCAmelCase							:			Dict="<unk>"				,		lowerCAmelCase							:			Any="<pad>"				,		lowerCAmelCase							:			List[str]="<mask>"				,		lowerCAmelCase							:			List[str]=["<s>NOTUSED", "</s>NOTUSED"]				,		**lowerCAmelCase							:			Dict				,		)				->				Tuple:

    """simple docstring"""







    _snake_case    :  Dict					=					AddedToken(lowerCAmelCase				,		lstrip=lowerCAmelCase				,		rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase				,		lowerCAmelCase) else mask_token

    super().__init__(
        lowerCAmelCase				,		tokenizer_file=lowerCAmelCase				,		bos_token=lowerCAmelCase				,		eos_token=lowerCAmelCase				,		sep_token=lowerCAmelCase				,		cls_token=lowerCAmelCase				,		unk_token=lowerCAmelCase				,		pad_token=lowerCAmelCase				,		mask_token=lowerCAmelCase				,		additional_special_tokens=lowerCAmelCase				,		**lowerCAmelCase				,		)

    _snake_case    :  List[str]					=					vocab_file
    _snake_case    :  Optional[Any]					=					False if not self.vocab_file else True

   def      UpperCamelCase_  (					self							:			Optional[int]				,		lowerCAmelCase							:			List[int]				,		lowerCAmelCase							:			Optional[List[int]] = None)				->				List[int]:

    """simple docstring"""







    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    _snake_case    :  Optional[Any]					=					[self.cls_token_id]
    _snake_case    :  List[Any]					=					[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			List[int]				,		lowerCAmelCase							:			Optional[List[int]] = None)				->				List[int]:

    """simple docstring"""







    _snake_case    :  str					=					[self.sep_token_id]
    _snake_case    :  List[str]					=					[self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Optional[str] = None)				->				Tuple[str]:

    """simple docstring"""







    if not self.can_save_slow_tokenizer:
     raise ValueError(
         """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
         """tokenizer.""")

    if not os.path.isdir(lowerCAmelCase):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
     return
    _snake_case    :  Optional[int]					=					os.path.join(
        lowerCAmelCase				,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

    if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase):
     copyfile(self.vocab_file				,		lowerCAmelCase)

    return (out_vocab_file,)
