'''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=  logging.getLogger()







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  argparse.ArgumentParser()
    parser.add_argument('-f'			)
    __UpperCamelCase	:	Dict		    =  parser.parse_args()
    return args.f







def  _UpperCamelCase (     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  {}
    __UpperCamelCase	:	List[str]		    =  os.path.join(_lowercase				,     'all_results.json'			)
    if os.path.exists(_lowercase			):
        with open(_lowercase				,     'r'			) as f:
            __UpperCamelCase	:	Optional[int]		    =  json.load(_lowercase			)
    else:
        raise ValueError(f"""can't find {path}"""			)
    return results







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






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







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







     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}    )
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.get_auto_remove_tmp_dir()
         __UpperCamelCase	:	Tuple		    =  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    )
         __UpperCamelCase	:	str		    =  get_results(__A    )
         self.assertGreaterEqual(result['eval_accuracy']     ,			0.7_5    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'epoch_0'    )    )    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'glue_no_trainer'    )    )    )







     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}    )
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.get_auto_remove_tmp_dir()
         __UpperCamelCase	:	str		    =  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    )
         __UpperCamelCase	:	List[str]		    =  get_results(__A    )
         self.assertLess(result['perplexity']     ,			1_0_0    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'epoch_0'    )    )    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'clm_no_trainer'    )    )    )







     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}    )
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  self.get_auto_remove_tmp_dir()
         __UpperCamelCase	:	Tuple		    =  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    )
         __UpperCamelCase	:	int		    =  get_results(__A    )
         self.assertLess(result['perplexity']     ,			4_2    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'epoch_0'    )    )    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'mlm_no_trainer'    )    )    )







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

         __UpperCamelCase	:	Tuple		    =  self.get_auto_remove_tmp_dir()
         __UpperCamelCase	:	Any		    =  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    )
         __UpperCamelCase	:	Optional[Any]		    =  get_results(__A    )
         self.assertGreaterEqual(result['eval_accuracy']     ,			0.7_5    )
         self.assertLess(result['train_loss']     ,			0.5    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'epoch_0'    )    )    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'ner_no_trainer'    )    )    )







     @unittest.skip(reason='Fix me @muellerzr'    )
     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}    )
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  self.get_auto_remove_tmp_dir()
         __UpperCamelCase	:	List[str]		    =  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    )
         __UpperCamelCase	:	str		    =  get_results(__A    )
         # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
         self.assertGreaterEqual(result['eval_f1']     ,			2_8    )
         self.assertGreaterEqual(result['eval_exact']     ,			2_8    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'epoch_0'    )    )    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'qa_no_trainer'    )    )    )







     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}    )
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  self.get_auto_remove_tmp_dir()
         __UpperCamelCase	:	Dict		    =  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    )
         __UpperCamelCase	:	Optional[int]		    =  get_results(__A    )
         self.assertGreaterEqual(result['eval_accuracy']     ,			0.8    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'swag_no_trainer'    )    )    )







     @slow
     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}    )
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  self.get_auto_remove_tmp_dir()
         __UpperCamelCase	:	List[Any]		    =  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    )
         __UpperCamelCase	:	Optional[Any]		    =  get_results(__A    )
         self.assertGreaterEqual(result['eval_rouge1']     ,			1_0    )
         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(__A     ,			'epoch_0'    )    )    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'summarization_no_trainer'    )    )    )







     @slow
     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}    )
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  self.get_auto_remove_tmp_dir()
         __UpperCamelCase	:	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    )
         __UpperCamelCase	:	List[str]		    =  get_results(__A    )
         self.assertGreaterEqual(result['eval_bleu']     ,			3_0    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'epoch_0'    )    )    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'translation_no_trainer'    )    )    )







     @slow
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  logging.StreamHandler(sys.stdout    )
         logger.addHandler(__A    )

         __UpperCamelCase	:	Optional[Any]		    =  self.get_auto_remove_tmp_dir()
         __UpperCamelCase	:	Tuple		    =  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    )
         __UpperCamelCase	:	Optional[int]		    =  get_results(__A    )
         self.assertGreaterEqual(result['eval_overall_accuracy']     ,			0.1_0    )







     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}    )
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  self.get_auto_remove_tmp_dir()
         __UpperCamelCase	:	Tuple		    =  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    )
         __UpperCamelCase	:	int		    =  get_results(__A    )
         # The base model scores a 25%
         self.assertGreaterEqual(result['eval_accuracy']     ,			0.6    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'step_1'    )    )    )
         self.assertTrue(os.path.exists(os.path.join(__A     ,			'image_classification_no_trainer'    )    )    )




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a=  {
    '''configuration_bridgetower''': [
        '''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BridgeTowerConfig''',
        '''BridgeTowerTextConfig''',
        '''BridgeTowerVisionConfig''',
    ],
    '''processing_bridgetower''': ['''BridgeTowerProcessor'''],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''BridgeTowerImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BridgeTowerForContrastiveLearning''',
      '''BridgeTowerForImageAndTextRetrieval''',
      '''BridgeTowerForMaskedLM''',
      '''BridgeTowerModel''',
      '''BridgeTowerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_bridgetower import (
      BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BridgeTowerConfig,
      BridgeTowerTextConfig,
      BridgeTowerVisionConfig,
  )
  from .processing_bridgetower import BridgeTowerProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_bridgetower import BridgeTowerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bridgetower import (
        BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
        BridgeTowerForContrastiveLearning,
        BridgeTowerForImageAndTextRetrieval,
        BridgeTowerForMaskedLM,
        BridgeTowerModel,
        BridgeTowerPreTrainedModel,
    )


else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)



