'''simple docstring'''

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			:      Tuple					      =				logging.getLogger()







def      UpperCamelCase_						(					)					->     int:






 '''simple docstring'''

 __lowerCAmelCase											=						argparse.ArgumentParser()
 parser.add_argument("""-f"""      )
 __lowerCAmelCase											=						parser.parse_args()
 return args.f







def      UpperCamelCase_						(					snake_case_       :       Union[str, Any]      )					->     List[str]:






 '''simple docstring'''

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







def      UpperCamelCase_						(					)					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.cuda.is_available() and torch_device == """cuda"""
 return is_using_cuda and is_apex_available()


_A			:      List[Any]					      =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 @classmethod
 def a					(      cls   :	List[Any]							)  ->    Optional[Any]:
  # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
  __lowerCAmelCase											=						tempfile.mkdtemp()
  __lowerCAmelCase											=						os.path.join(cls.tmpdir      ,       """default_config.yml"""							)
  write_basic_config(save_location=cls.configPath							)
  __lowerCAmelCase											=						["""accelerate""", """launch""", """--config_file""", cls.configPath]
 @classmethod
 def a					(      cls   :	Tuple							)  ->    Dict:
  shutil.rmtree(cls.tmpdir							)
 @mock.patch.dict(os.environ      ,       {"""WANDB_MODE""": """offline"""}							)
 def a					(      self   :	Tuple							)  ->    List[str]:
  __lowerCAmelCase											=						self.get_auto_remove_tmp_dir()
  __lowerCAmelCase											=						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							)
  __lowerCAmelCase											=						get_results(SCREAMING_SNAKE_CASE__							)
  self.assertGreaterEqual(result["""eval_accuracy"""]      ,       0.7_5							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """epoch_0"""							)							)							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """glue_no_trainer"""							)							)							)
 @mock.patch.dict(os.environ      ,       {"""WANDB_MODE""": """offline"""}							)
 def a					(      self   :	Optional[int]							)  ->    Tuple:
  __lowerCAmelCase											=						self.get_auto_remove_tmp_dir()
  __lowerCAmelCase											=						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							)
  __lowerCAmelCase											=						get_results(SCREAMING_SNAKE_CASE__							)
  self.assertLess(result["""perplexity"""]      ,       1_00							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """epoch_0"""							)							)							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """clm_no_trainer"""							)							)							)
 @mock.patch.dict(os.environ      ,       {"""WANDB_MODE""": """offline"""}							)
 def a					(      self   :	Dict							)  ->    List[Any]:
  __lowerCAmelCase											=						self.get_auto_remove_tmp_dir()
  __lowerCAmelCase											=						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							)
  __lowerCAmelCase											=						get_results(SCREAMING_SNAKE_CASE__							)
  self.assertLess(result["""perplexity"""]      ,       42							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """epoch_0"""							)							)							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """mlm_no_trainer"""							)							)							)
 @mock.patch.dict(os.environ      ,       {"""WANDB_MODE""": """offline"""}							)
 def a					(      self   :	int							)  ->    List[str]:
  # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
  __lowerCAmelCase											=						7 if get_gpu_count() > 1 else 2

  __lowerCAmelCase											=						self.get_auto_remove_tmp_dir()
  __lowerCAmelCase											=						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							)
  __lowerCAmelCase											=						get_results(SCREAMING_SNAKE_CASE__							)
  self.assertGreaterEqual(result["""eval_accuracy"""]      ,       0.7_5							)
  self.assertLess(result["""train_loss"""]      ,       0.5							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """epoch_0"""							)							)							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """ner_no_trainer"""							)							)							)
 @unittest.skip(reason="""Fix me @muellerzr"""							)
 @mock.patch.dict(os.environ      ,       {"""WANDB_MODE""": """offline"""}							)
 def a					(      self   :	Optional[int]							)  ->    Optional[int]:
  __lowerCAmelCase											=						self.get_auto_remove_tmp_dir()
  __lowerCAmelCase											=						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							)
  __lowerCAmelCase											=						get_results(SCREAMING_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(SCREAMING_SNAKE_CASE__      ,       """epoch_0"""							)							)							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """qa_no_trainer"""							)							)							)
 @mock.patch.dict(os.environ      ,       {"""WANDB_MODE""": """offline"""}							)
 def a					(      self   :	Tuple							)  ->    Any:
  __lowerCAmelCase											=						self.get_auto_remove_tmp_dir()
  __lowerCAmelCase											=						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							)
  __lowerCAmelCase											=						get_results(SCREAMING_SNAKE_CASE__							)
  self.assertGreaterEqual(result["""eval_accuracy"""]      ,       0.8							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """swag_no_trainer"""							)							)							)
 @slow
 @mock.patch.dict(os.environ      ,       {"""WANDB_MODE""": """offline"""}							)
 def a					(      self   :	Tuple							)  ->    Dict:
  __lowerCAmelCase											=						self.get_auto_remove_tmp_dir()
  __lowerCAmelCase											=						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							)
  __lowerCAmelCase											=						get_results(SCREAMING_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(SCREAMING_SNAKE_CASE__      ,       """epoch_0"""							)							)							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """summarization_no_trainer"""							)							)							)
 @slow
 @mock.patch.dict(os.environ      ,       {"""WANDB_MODE""": """offline"""}							)
 def a					(      self   :	Optional[Any]							)  ->    Any:
  __lowerCAmelCase											=						self.get_auto_remove_tmp_dir()
  __lowerCAmelCase											=						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							)
  __lowerCAmelCase											=						get_results(SCREAMING_SNAKE_CASE__							)
  self.assertGreaterEqual(result["""eval_bleu"""]      ,       30							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """epoch_0"""							)							)							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """translation_no_trainer"""							)							)							)
 @slow
 def a					(      self   :	Optional[int]							)  ->    Optional[int]:
  __lowerCAmelCase											=						logging.StreamHandler(sys.stdout							)
  logger.addHandler(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						self.get_auto_remove_tmp_dir()
  __lowerCAmelCase											=						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							)
  __lowerCAmelCase											=						get_results(SCREAMING_SNAKE_CASE__							)
  self.assertGreaterEqual(result["""eval_overall_accuracy"""]      ,       0.1_0							)





 @mock.patch.dict(os.environ      ,       {"""WANDB_MODE""": """offline"""}							)
 def a					(      self   :	List[Any]							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						self.get_auto_remove_tmp_dir()
  __lowerCAmelCase											=						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							)
  __lowerCAmelCase											=						get_results(SCREAMING_SNAKE_CASE__							)
  # The base model scores a 25%
  self.assertGreaterEqual(result["""eval_accuracy"""]      ,       0.6							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """step_1"""							)							)							)
  self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__      ,       """image_classification_no_trainer"""							)							)							)







'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:      int					      =				logging.get_logger(__name__)

_A			:      Any					      =				{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					"""ctrl"""
 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					["""past_key_values"""]
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					{
     """max_position_embeddings""": """n_positions""",
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=24_65_34      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=2_56      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=12_80      ,       SCREAMING_SNAKE_CASE__   :	str=81_92      ,       SCREAMING_SNAKE_CASE__   :	List[str]=48      ,       SCREAMING_SNAKE_CASE__   :	Tuple=16      ,       SCREAMING_SNAKE_CASE__   :	str=0.1      ,       SCREAMING_SNAKE_CASE__   :	Dict=0.1      ,       SCREAMING_SNAKE_CASE__   :	Dict=1e-6      ,       SCREAMING_SNAKE_CASE__   :	Any=0.0_2      ,       SCREAMING_SNAKE_CASE__   :	Tuple=True      ,       **SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       )  ->    Tuple:
  __lowerCAmelCase											=						vocab_size
  __lowerCAmelCase											=						n_positions
  __lowerCAmelCase											=						n_embd
  __lowerCAmelCase											=						n_layer
  __lowerCAmelCase											=						n_head
  __lowerCAmelCase											=						dff
  __lowerCAmelCase											=						resid_pdrop
  __lowerCAmelCase											=						embd_pdrop
  __lowerCAmelCase											=						layer_norm_epsilon
  __lowerCAmelCase											=						initializer_range

  __lowerCAmelCase											=						use_cache

  super().__init__(**SCREAMING_SNAKE_CASE__							)






