





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      A__       (    )  ->  List[Any]:






   """simple docstring"""







   _UpperCAmelCase           =   argparse.ArgumentParser()
   parser.add_argument('''-f'''						)
   _UpperCAmelCase           =   parser.parse_args()
   return args.f
def      A__       (    SCREAMING_SNAKE_CASE_   :      Dict						)  ->  str:






   """simple docstring"""







   _UpperCAmelCase           =   {}
   _UpperCAmelCase           =   os.path.join(SCREAMING_SNAKE_CASE_				,	'''all_results.json'''						)
   if os.path.exists(SCREAMING_SNAKE_CASE_						):
      with open(SCREAMING_SNAKE_CASE_				,	'''r'''						) as f:
         _UpperCAmelCase           =   json.load(SCREAMING_SNAKE_CASE_						)
   else:
      raise ValueError(F'''can\'t find {path}'''						)
   return results
def      A__       (    )  ->  Union[str, Any]:






   """simple docstring"""







   _UpperCAmelCase           =   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 			__UpperCamelCase    (							A__    ):





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





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





     @mock.patch.dict(os.environ				,							{'''WANDB_MODE''': '''offline'''}					)
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_auto_remove_tmp_dir()
        _UpperCAmelCase           =   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           =   get_results(_UpperCamelCase					)
        self.assertGreaterEqual(result['''eval_accuracy''']				,							0.75					)
        self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,							'''epoch_0'''					)					)					)
        self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,							'''glue_no_trainer'''					)					)					)





     @mock.patch.dict(os.environ				,							{'''WANDB_MODE''': '''offline'''}					)
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_auto_remove_tmp_dir()
        _UpperCAmelCase           =   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           =   get_results(_UpperCamelCase					)
        self.assertLess(result['''perplexity''']				,							100					)
        self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,							'''epoch_0'''					)					)					)
        self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,							'''clm_no_trainer'''					)					)					)





     @mock.patch.dict(os.environ				,							{'''WANDB_MODE''': '''offline'''}					)
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_auto_remove_tmp_dir()
        _UpperCAmelCase           =   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           =   get_results(_UpperCamelCase					)
        self.assertLess(result['''perplexity''']				,							42					)
        self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,							'''epoch_0'''					)					)					)
        self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,							'''mlm_no_trainer'''					)					)					)





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

        _UpperCAmelCase           =   self.get_auto_remove_tmp_dir()
        _UpperCAmelCase           =   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           =   get_results(_UpperCamelCase					)
        self.assertGreaterEqual(result['''eval_accuracy''']				,							0.75					)
        self.assertLess(result['''train_loss''']				,							0.5					)
        self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,							'''epoch_0'''					)					)					)
        self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,							'''ner_no_trainer'''					)					)					)





     @unittest.skip(reason='''Fix me @muellerzr'''					)
     @mock.patch.dict(os.environ				,							{'''WANDB_MODE''': '''offline'''}					)
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_auto_remove_tmp_dir()
        _UpperCAmelCase           =   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           =   get_results(_UpperCamelCase					)
        # 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(_UpperCamelCase				,							'''epoch_0'''					)					)					)
        self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,							'''qa_no_trainer'''					)					)					)





     @mock.patch.dict(os.environ				,							{'''WANDB_MODE''': '''offline'''}					)
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_auto_remove_tmp_dir()
        _UpperCAmelCase           =   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           =   get_results(_UpperCamelCase					)
        self.assertGreaterEqual(result['''eval_accuracy''']				,							0.8					)
        self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,							'''swag_no_trainer'''					)					)					)





     @slow
     @mock.patch.dict(os.environ				,							{'''WANDB_MODE''': '''offline'''}					)
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_auto_remove_tmp_dir()
        _UpperCAmelCase           =   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           =   get_results(_UpperCamelCase					)
        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(_UpperCamelCase				,							'''epoch_0'''					)					)					)
        self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,							'''summarization_no_trainer'''					)					)					)





     @slow
     @mock.patch.dict(os.environ				,							{'''WANDB_MODE''': '''offline'''}					)
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_auto_remove_tmp_dir()
        _UpperCAmelCase           =   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           =   get_results(_UpperCamelCase					)
        self.assertGreaterEqual(result['''eval_bleu''']				,							30					)
        self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,							'''epoch_0'''					)					)					)
        self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,							'''translation_no_trainer'''					)					)					)





     @slow
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   logging.StreamHandler(sys.stdout					)
        logger.addHandler(_UpperCamelCase					)

        _UpperCAmelCase           =   self.get_auto_remove_tmp_dir()
        _UpperCAmelCase           =   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           =   get_results(_UpperCamelCase					)
        self.assertGreaterEqual(result['''eval_overall_accuracy''']				,							0.10					)





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






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			__UpperCamelCase    (							A__    ):
     __A	:  Dict							  =   ["""image_processor""", """tokenizer"""]
     __A	:  List[str]							  =   """BridgeTowerImageProcessor"""
     __A	:  str							  =   ("""RobertaTokenizer""", """RobertaTokenizerFast""")





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__(_UpperCamelCase				,							_UpperCamelCase					)





     def __call__(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = False				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = 0				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   self.tokenizer(
            text=_UpperCamelCase				,							add_special_tokens=_UpperCamelCase				,							padding=_UpperCamelCase				,							truncation=_UpperCamelCase				,							max_length=_UpperCamelCase				,							stride=_UpperCamelCase				,							pad_to_multiple_of=_UpperCamelCase				,							return_token_type_ids=_UpperCamelCase				,							return_attention_mask=_UpperCamelCase				,							return_overflowing_tokens=_UpperCamelCase				,							return_special_tokens_mask=_UpperCamelCase				,							return_offsets_mapping=_UpperCamelCase				,							return_length=_UpperCamelCase				,							verbose=_UpperCamelCase				,							return_tensors=_UpperCamelCase				,							**_UpperCamelCase				,							)
        # add pixel_values + pixel_mask
        _UpperCAmelCase           =   self.image_processor(
            _UpperCamelCase				,							return_tensors=_UpperCamelCase				,							do_normalize=_UpperCamelCase				,							do_center_crop=_UpperCamelCase				,							**_UpperCamelCase					)
        encoding.update(_UpperCamelCase					)

        return encoding





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.batch_decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     @property
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.tokenizer.model_input_names
        _UpperCAmelCase           =   self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)