





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       : List[str]			     =       logging.getLogger()


def  SCREAMING_SNAKE_CASE   ( )    ->  List[Any]:
    lowerCamelCase__					:   List[str]      				=	argparse.ArgumentParser()
    parser.add_argument('-f'				)
    lowerCamelCase__					:   Union[str, Any]      				=	parser.parse_args()
    return args.f


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


def  SCREAMING_SNAKE_CASE   ( )    ->  List[str]:
    lowerCamelCase__					:   str      				=	torch.cuda.is_available() and torch_device == 'cuda'
    return is_using_cuda and is_apex_available()


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


class      lowerCAmelCase							(      __UpperCamelCase    ):
 @classmethod
 def   A_			(   cls						:   Union[str, Any]			)    ->			Tuple:
     # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
     lowerCamelCase__					:   Dict      				=	tempfile.mkdtemp()
     lowerCamelCase__					:   Optional[Any]      				=	os.path.join(cls.tmpdir    ,  'default_config.yml'			)
     write_basic_config(save_location=cls.configPath			)
     lowerCamelCase__					:   Optional[Any]      				=	['accelerate', 'launch', '--config_file', cls.configPath]
 @classmethod
 def   A_			(   cls						:   Optional[int]			)    ->			Union[str, Any]:
     shutil.rmtree(cls.tmpdir			)
 @mock.patch.dict(os.environ    ,  {'WANDB_MODE': 'offline'}			)
 def   A_			(   self						:   List[str]			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Any      				=	self.get_auto_remove_tmp_dir()
     lowerCamelCase__					:   List[str]      				=	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__					:   str      				=	get_results(UpperCAmelCase			)
     self.assertGreaterEqual(result['eval_accuracy']    ,  0.7_5			)
     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   A_			(   self						:   Tuple			)    ->			List[str]:
     lowerCamelCase__					:   List[Any]      				=	self.get_auto_remove_tmp_dir()
     lowerCamelCase__					:   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			)
     lowerCamelCase__					:   Optional[Any]      				=	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   A_			(   self						:   Any			)    ->			Optional[int]:
     lowerCamelCase__					:   List[str]      				=	self.get_auto_remove_tmp_dir()
     lowerCamelCase__					:   str      				=	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__					:   List[Any]      				=	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   A_			(   self						:   Optional[Any]			)    ->			int:
     # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
     lowerCamelCase__					:   List[Any]      				=	7 if get_gpu_count() > 1 else 2

     lowerCamelCase__					:   Dict      				=	self.get_auto_remove_tmp_dir()
     lowerCamelCase__					:   List[str]      				=	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__					:   List[str]      				=	get_results(UpperCAmelCase			)
     self.assertGreaterEqual(result['eval_accuracy']    ,  0.7_5			)
     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   A_			(   self						:   Dict			)    ->			Optional[int]:
     lowerCamelCase__					:   int      				=	self.get_auto_remove_tmp_dir()
     lowerCamelCase__					:   Dict      				=	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__					:   Union[str, Any]      				=	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   A_			(   self						:   List[Any]			)    ->			Dict:
     lowerCamelCase__					:   Dict      				=	self.get_auto_remove_tmp_dir()
     lowerCamelCase__					:   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			)
     lowerCamelCase__					:   Optional[Any]      				=	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   A_			(   self						:   int			)    ->			Optional[int]:
     lowerCamelCase__					:   Union[str, Any]      				=	self.get_auto_remove_tmp_dir()
     lowerCamelCase__					:   Union[str, 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			)
     lowerCamelCase__					:   Optional[int]      				=	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   A_			(   self						:   Dict			)    ->			Tuple:
     lowerCamelCase__					:   List[str]      				=	self.get_auto_remove_tmp_dir()
     lowerCamelCase__					:   List[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			)
     lowerCamelCase__					:   int      				=	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   A_			(   self						:   Optional[Any]			)    ->			int:
     lowerCamelCase__					:   int      				=	logging.StreamHandler(sys.stdout			)
     logger.addHandler(UpperCAmelCase			)

     lowerCamelCase__					:   str      				=	self.get_auto_remove_tmp_dir()
     lowerCamelCase__					:   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			)
     lowerCamelCase__					:   Dict      				=	get_results(UpperCAmelCase			)
     self.assertGreaterEqual(result['eval_overall_accuracy']    ,  0.1_0			)







 @mock.patch.dict(os.environ    ,  {'WANDB_MODE': 'offline'}			)
 def   A_			(   self						:   Dict			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	self.get_auto_remove_tmp_dir()
     lowerCamelCase__					:   Optional[int]      				=	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__					:   List[str]      				=	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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase       : Dict			     =       logging.get_logger(__name__)

_UpperCAmelCase       : Union[str, Any]			     =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	"""donut-swin"""

 UpperCAmelCase__									=	{
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }
 def __init__(   self						:   Tuple    ,  UpperCAmelCase						:   List[str]=224    ,  UpperCAmelCase						:   Optional[Any]=4    ,  UpperCAmelCase						:   Union[str, Any]=3    ,  UpperCAmelCase						:   Union[str, Any]=96    ,  UpperCAmelCase						:   int=[2, 2, 6, 2]    ,  UpperCAmelCase						:   Dict=[3, 6, 12, 24]    ,  UpperCAmelCase						:   Any=7    ,  UpperCAmelCase						:   Optional[int]=4.0    ,  UpperCAmelCase						:   List[Any]=True    ,  UpperCAmelCase						:   Any=0.0    ,  UpperCAmelCase						:   Optional[Any]=0.0    ,  UpperCAmelCase						:   int=0.1    ,  UpperCAmelCase						:   List[str]="gelu"    ,  UpperCAmelCase						:   List[str]=False    ,  UpperCAmelCase						:   str=0.0_2    ,  UpperCAmelCase						:   str=1e-5    ,  **UpperCAmelCase						:   Tuple    ,  )    ->			int:
     super().__init__(**UpperCAmelCase			)

     lowerCamelCase__					:   Optional[Any]      				=	image_size
     lowerCamelCase__					:   Optional[int]      				=	patch_size
     lowerCamelCase__					:   int      				=	num_channels
     lowerCamelCase__					:   int      				=	embed_dim
     lowerCamelCase__					:   Optional[Any]      				=	depths
     lowerCamelCase__					:   Optional[int]      				=	len(UpperCAmelCase			)
     lowerCamelCase__					:   Optional[int]      				=	num_heads
     lowerCamelCase__					:   Tuple      				=	window_size
     lowerCamelCase__					:   Dict      				=	mlp_ratio
     lowerCamelCase__					:   str      				=	qkv_bias
     lowerCamelCase__					:   Tuple      				=	hidden_dropout_prob
     lowerCamelCase__					:   str      				=	attention_probs_dropout_prob
     lowerCamelCase__					:   str      				=	drop_path_rate
     lowerCamelCase__					:   int      				=	hidden_act
     lowerCamelCase__					:   str      				=	use_absolute_embeddings
     lowerCamelCase__					:   List[Any]      				=	layer_norm_eps
     lowerCamelCase__					:   Optional[Any]      				=	initializer_range
     # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     lowerCamelCase__					:   Optional[int]      				=	int(embed_dim * 2 ** (len(UpperCAmelCase			) - 1)			)





