


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)

snake_case    :   Tuple	    =					logging.getLogger()
def     __lowerCamelCase						(			):




      """simple docstring"""





      a						:Optional[int]										=	argparse.ArgumentParser()
      parser.add_argument('''-f'''						)
      a						:int										=	parser.parse_args()
      return args.f
def     __lowerCamelCase						(			UpperCAmelCase_						:		List[Any]						):




      """simple docstring"""





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




      """simple docstring"""





      a						:int										=	torch.cuda.is_available() and torch_device == '''cuda'''
      return is_using_cuda and is_apex_available()


snake_case    :   int	    =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 		_snake_case  (				_snake_case							):




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




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




 @mock.patch.dict(os.environ ,  {'''WANDB_MODE''': '''offline'''}   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[Any]										=	self.get_auto_remove_tmp_dir()
       a						:Union[str, Any]										=	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   )
       a						:Optional[Any]										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[Any]										=	self.get_auto_remove_tmp_dir()
       a						:Dict										=	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   )
       a						:str										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[Any]										=	self.get_auto_remove_tmp_dir()
       a						:int										=	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   )
       a						:str										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
       # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
       a						:Dict										=	7 if get_gpu_count() > 1 else 2

       a						:Any										=	self.get_auto_remove_tmp_dir()
       a						: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   )
       a						:Tuple										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Union[str, Any]										=	self.get_auto_remove_tmp_dir()
       a						:List[Any]										=	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   )
       a						:List[Any]										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[Any]										=	self.get_auto_remove_tmp_dir()
       a						: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   )
       a						:List[str]										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[str]										=	self.get_auto_remove_tmp_dir()
       a						: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   )
       a						:Tuple										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Union[str, Any]										=	self.get_auto_remove_tmp_dir()
       a						:Optional[int]										=	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   )
       a						:Optional[Any]										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Tuple										=	logging.StreamHandler(sys.stdout   )
       logger.addHandler(_lowerCamelCase   )

       a						:Dict										=	self.get_auto_remove_tmp_dir()
       a						:Dict										=	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   )
       a						:Optional[int]										=	get_results(_lowerCamelCase   )
       self.assertGreaterEqual(result['''eval_overall_accuracy'''] ,  0.10   )




 @mock.patch.dict(os.environ ,  {'''WANDB_MODE''': '''offline'''}   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Union[str, Any]										=	self.get_auto_remove_tmp_dir()
       a						:Any										=	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   )
       a						:Optional[int]										=	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'''   )   )   )









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case    :   Optional[Any]	    =					logging.get_logger(__name__)

snake_case    :   Dict	    =					{
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'data2vec-vision'




 def __init__(		self ,  _lowerCamelCase=768 ,  _lowerCamelCase=12 ,  _lowerCamelCase=12 ,  _lowerCamelCase=3072 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.0 ,  _lowerCamelCase=0.0 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-12 ,  _lowerCamelCase=224 ,  _lowerCamelCase=16 ,  _lowerCamelCase=3 ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=True ,  _lowerCamelCase=[3, 5, 7, 11] ,  _lowerCamelCase=[1, 2, 3, 6] ,  _lowerCamelCase=True ,  _lowerCamelCase=0.4 ,  _lowerCamelCase=256 ,  _lowerCamelCase=1 ,  _lowerCamelCase=False ,  _lowerCamelCase=255 ,  **_lowerCamelCase ,  ):
       super().__init__(**_lowerCamelCase   )

       a						:Tuple										=	hidden_size
       a						:Any										=	num_hidden_layers
       a						:Optional[int]										=	num_attention_heads
       a						:Dict										=	intermediate_size
       a						:List[Any]										=	hidden_act
       a						:List[str]										=	hidden_dropout_prob
       a						:Union[str, Any]										=	attention_probs_dropout_prob
       a						:Any										=	initializer_range
       a						:Any										=	layer_norm_eps

       a						:Union[str, Any]										=	image_size
       a						:int										=	patch_size
       a						:Optional[int]										=	num_channels
       a						:Union[str, Any]										=	use_mask_token
       a						:Optional[Any]										=	use_absolute_position_embeddings
       a						:Tuple										=	use_relative_position_bias
       a						:List[Any]										=	use_shared_relative_position_bias
       a						:Dict										=	layer_scale_init_value
       a						:Optional[int]										=	drop_path_rate
       a						:List[str]										=	use_mean_pooling
       # decode head attributes (semantic segmentation)
       a						:str										=	out_indices
       a						:Tuple										=	pool_scales
       # auxiliary head attributes (semantic segmentation)
       a						:List[Any]										=	use_auxiliary_head
       a						:List[Any]										=	auxiliary_loss_weight
       a						:Optional[int]										=	auxiliary_channels
       a						:List[str]										=	auxiliary_num_convs
       a						:str										=	auxiliary_concat_input
       a						:Union[str, Any]										=	semantic_loss_ignore_index







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	version.parse('1.11'							)




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ]   )




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return 1e-4





