"""simple docstring"""






import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    MODEL_MAPPING,
						    DeiTForImageClassification,
						    DeiTForImageClassificationWithTeacher,
						    DeiTForMaskedImageModeling,
						    DeiTModel,
						)
						from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import DeiTImageProcessor
class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	List[str]    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	Optional[Any]=13    ,lowerCamelCase__   :	int=30    ,lowerCamelCase__   :	Optional[int]=2    ,lowerCamelCase__   :	Union[str, Any]=3    ,lowerCamelCase__   :	Optional[int]=True    ,lowerCamelCase__   :	Dict=True    ,lowerCamelCase__   :	Tuple=32    ,lowerCamelCase__   :	Union[str, Any]=5    ,lowerCamelCase__   :	Tuple=4    ,lowerCamelCase__   :	str=37    ,lowerCamelCase__   :	Union[str, Any]="gelu"    ,lowerCamelCase__   :	Optional[int]=0.1    ,lowerCamelCase__   :	Dict=0.1    ,lowerCamelCase__   :	List[str]=10    ,lowerCamelCase__   :	int=0.0_2    ,lowerCamelCase__   :	Dict=3    ,lowerCamelCase__   :	int=None    ,lowerCamelCase__   :	str=2    ,):
									UpperCAmelCase__             =							parent
									UpperCAmelCase__             =							batch_size
									UpperCAmelCase__             =							image_size
									UpperCAmelCase__             =							patch_size
									UpperCAmelCase__             =							num_channels
									UpperCAmelCase__             =							is_training
									UpperCAmelCase__             =							use_labels
									UpperCAmelCase__             =							hidden_size
									UpperCAmelCase__             =							num_hidden_layers
									UpperCAmelCase__             =							num_attention_heads
									UpperCAmelCase__             =							intermediate_size
									UpperCAmelCase__             =							hidden_act
									UpperCAmelCase__             =							hidden_dropout_prob
									UpperCAmelCase__             =							attention_probs_dropout_prob
									UpperCAmelCase__             =							type_sequence_label_size
									UpperCAmelCase__             =							initializer_range
									UpperCAmelCase__             =							scope
									UpperCAmelCase__             =							encoder_stride

									# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
									UpperCAmelCase__             =							(image_size // patch_size) ** 2
									UpperCAmelCase__             =							num_patches + 2







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

									UpperCAmelCase__             =							None
									if self.use_labels:
													UpperCAmelCase__             =							ids_tensor([self.batch_size]    ,self.type_sequence_label_size			)

									UpperCAmelCase__             =							self.get_config()

									return config, pixel_values, labels







					def 		__lowerCAmelCase						(				self   :	Any			):
									return DeiTConfig(
									    image_size=self.image_size    ,patch_size=self.patch_size    ,num_channels=self.num_channels    ,hidden_size=self.hidden_size    ,num_hidden_layers=self.num_hidden_layers    ,num_attention_heads=self.num_attention_heads    ,intermediate_size=self.intermediate_size    ,hidden_act=self.hidden_act    ,hidden_dropout_prob=self.hidden_dropout_prob    ,attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,is_decoder=lowerCamelCase__    ,initializer_range=self.initializer_range    ,encoder_stride=self.encoder_stride    ,)







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	List[Any]    ,lowerCamelCase__   :	Optional[Any]			):
									UpperCAmelCase__             =							DeiTModel(config=lowerCamelCase__			)
									model.to(lowerCamelCase__			)
									model.eval()
									UpperCAmelCase__             =							model(lowerCamelCase__			)
									self.parent.assertEqual(result.last_hidden_state.shape    ,(self.batch_size, self.seq_length, self.hidden_size)			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	Dict    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	Any			):
									UpperCAmelCase__             =							DeiTForMaskedImageModeling(config=lowerCamelCase__			)
									model.to(lowerCamelCase__			)
									model.eval()
									UpperCAmelCase__             =							model(lowerCamelCase__			)
									self.parent.assertEqual(
									    result.reconstruction.shape    ,(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

									# test greyscale images
									UpperCAmelCase__             =							1
									UpperCAmelCase__             =							DeiTForMaskedImageModeling(lowerCamelCase__			)
									model.to(lowerCamelCase__			)
									model.eval()

									UpperCAmelCase__             =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
									UpperCAmelCase__             =							model(lowerCamelCase__			)
									self.parent.assertEqual(result.reconstruction.shape    ,(self.batch_size, 1, self.image_size, self.image_size)			)







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	List[Any]    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	List[str]			):
									UpperCAmelCase__             =							self.type_sequence_label_size
									UpperCAmelCase__             =							DeiTForImageClassification(lowerCamelCase__			)
									model.to(lowerCamelCase__			)
									model.eval()
									UpperCAmelCase__             =							model(lowerCamelCase__    ,labels=lowerCamelCase__			)
									self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.type_sequence_label_size)			)

									# test greyscale images
									UpperCAmelCase__             =							1
									UpperCAmelCase__             =							DeiTForImageClassification(lowerCamelCase__			)
									model.to(lowerCamelCase__			)
									model.eval()

									UpperCAmelCase__             =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
									UpperCAmelCase__             =							model(lowerCamelCase__    ,labels=lowerCamelCase__			)
									self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.type_sequence_label_size)			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							self.prepare_config_and_inputs()
									(
									    (
									    UpperCAmelCase__
									)      ,	(
									    UpperCAmelCase__
									)      ,	(
									    UpperCAmelCase__
									)      ,	
									)             =							config_and_inputs
									UpperCAmelCase__             =							{'pixel_values': pixel_values}
									return config, inputs_dict






@require_torch
class 				snake_case       ( __UpperCAmelCase      ,    __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = (
					    (
					        DeiTModel,
					        DeiTForImageClassification,
					        DeiTForImageClassificationWithTeacher,
					        DeiTForMaskedImageModeling,
					    )
					    if is_torch_available()
					    else ()
					)
					snake_case__				    = (
					    {
					        "feature-extraction": DeiTModel,
					        "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
					    }
					    if is_torch_available()
					    else {}
					)

					snake_case__				    = False
					snake_case__				    = False
					snake_case__				    = False







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							DeiTModelTester(self			)
									UpperCAmelCase__             =							ConfigTester(self    ,config_class=lowerCamelCase__    ,has_text_modality=lowerCamelCase__    ,hidden_size=37			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									self.config_tester.run_common_tests()







					@unittest.skip(reason='DeiT does not use inputs_embeds'			)
					def 		__lowerCAmelCase						(				self   :	str			):
									pass







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													UpperCAmelCase__             =							model_class(lowerCamelCase__			)
													self.assertIsInstance(model.get_input_embeddings()    ,(nn.Module)			)
													UpperCAmelCase__             =							model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCamelCase__    ,nn.Linear			)			)







					def 		__lowerCAmelCase						(				self   :	str			):
									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													UpperCAmelCase__             =							model_class(lowerCamelCase__			)
													UpperCAmelCase__             =							inspect.signature(model.forward			)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													UpperCAmelCase__             =							[*signature.parameters.keys()]

													UpperCAmelCase__             =							['pixel_values']
													self.assertListEqual(arg_names[:1]    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	int    ,lowerCamelCase__   :	List[str]=False			):
									UpperCAmelCase__             =							super()._prepare_for_class(lowerCamelCase__    ,lowerCamelCase__    ,return_labels=lowerCamelCase__			)

									if return_labels:
													if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																	del inputs_dict["labels"]

									return inputs_dict







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									if not self.model_tester.is_training:
													return

									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()
									UpperCAmelCase__             =							True

									for model_class in self.all_model_classes:
													# DeiTForImageClassificationWithTeacher supports inference-only
													if (
													    model_class in get_values(lowerCamelCase__			)
													    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
													):
																	continue
													UpperCAmelCase__             =							model_class(lowerCamelCase__			)
													model.to(lowerCamelCase__			)
													model.train()
													UpperCAmelCase__             =							self._prepare_for_class(lowerCamelCase__    ,lowerCamelCase__    ,return_labels=lowerCamelCase__			)
													UpperCAmelCase__             =							model(**lowerCamelCase__			).loss
													loss.backward()







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
													return

									UpperCAmelCase__             =							False
									UpperCAmelCase__             =							True

									for model_class in self.all_model_classes:
													if model_class in get_values(lowerCamelCase__			) or not model_class.supports_gradient_checkpointing:
																	continue
													# DeiTForImageClassificationWithTeacher supports inference-only
													if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																	continue
													UpperCAmelCase__             =							model_class(lowerCamelCase__			)
													model.gradient_checkpointing_enable()
													model.to(lowerCamelCase__			)
													model.train()
													UpperCAmelCase__             =							self._prepare_for_class(lowerCamelCase__    ,lowerCamelCase__    ,return_labels=lowerCamelCase__			)
													UpperCAmelCase__             =							model(**lowerCamelCase__			).loss
													loss.backward()







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()

									UpperCAmelCase__             =							[
									    {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
									    {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
									    {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
									]

									for model_class in self.all_model_classes:
													if (
													    model_class
													    not in [
													        *get_values(lowerCamelCase__			),
													        *get_values(lowerCamelCase__			),
													    ]
													    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
													):
																	continue

													for problem_type in problem_types:
																	with self.subTest(msg=f'''Testing {model_class} with {problem_type["title"]}'''			):
																					UpperCAmelCase__             =							problem_type['title']
																					UpperCAmelCase__             =							problem_type['num_labels']

																					UpperCAmelCase__             =							model_class(lowerCamelCase__			)
																					model.to(lowerCamelCase__			)
																					model.train()

																					UpperCAmelCase__             =							self._prepare_for_class(lowerCamelCase__    ,lowerCamelCase__    ,return_labels=lowerCamelCase__			)

																					if problem_type["num_labels"] > 1:
																									UpperCAmelCase__             =							inputs['labels'].unsqueeze(1			).repeat(1    ,problem_type['num_labels']			)

																					UpperCAmelCase__             =							inputs['labels'].to(problem_type['dtype']			)

																					# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																					# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																					# they have the same size." which is a symptom something in wrong for the regression problem.
																					# See https://github.com/huggingface/transformers/issues/11780
																					with warnings.catch_warnings(record=lowerCamelCase__			) as warning_list:
																									UpperCAmelCase__             =							model(**lowerCamelCase__			).loss
																					for w in warning_list:
																									if "Using a target size that is different to the input size" in str(w.message			):
																													raise ValueError(
																													    f'''Something is going wrong in the regression problem: intercepted {w.message}'''			)

																					loss.backward()







					@slow
					def 		__lowerCAmelCase						(				self   :	int			):
									for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCAmelCase__             =							DeiTModel.from_pretrained(lowerCamelCase__			)
													self.assertIsNotNone(lowerCamelCase__			)






def   a_						(					):
				UpperCAmelCase__             =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
				return image







@require_torch
@require_vision
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					@cached_property
					def 		__lowerCAmelCase						(				self   :	Tuple			):
									return (
									    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'			)
									    if is_vision_available()
									    else None
									)







					@slow
					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'			).to(
									    lowerCamelCase__			)

									UpperCAmelCase__             =							self.default_image_processor
									UpperCAmelCase__             =							prepare_img()
									UpperCAmelCase__             =							image_processor(images=lowerCamelCase__    ,return_tensors='pt'			).to(lowerCamelCase__			)

									# forward pass
									with torch.no_grad():
													UpperCAmelCase__             =							model(**lowerCamelCase__			)

									# verify the logits
									UpperCAmelCase__             =							torch.Size((1, 1_000)			)
									self.assertEqual(outputs.logits.shape    ,lowerCamelCase__			)

									UpperCAmelCase__             =							torch.tensor([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]			).to(lowerCamelCase__			)

									self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,lowerCamelCase__    ,atol=1e-4			)			)







					@slow
					@require_accelerate
					@require_torch_gpu
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							DeiTModel.from_pretrained(
									    'facebook/deit-base-distilled-patch16-224'    ,torch_dtype=torch.floataa    ,device_map='auto'			)
									UpperCAmelCase__             =							self.default_image_processor

									UpperCAmelCase__             =							prepare_img()
									UpperCAmelCase__             =							image_processor(images=lowerCamelCase__    ,return_tensors='pt'			)
									UpperCAmelCase__             =							inputs.pixel_values.to(lowerCamelCase__			)

									# forward pass to make sure inference works in fp16
									with torch.no_grad():
													UpperCAmelCase__             =							model(lowerCamelCase__			)




"""simple docstring"""






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__  :  Optional[Any]						     =	logging.getLogger()



def   a_						(					):
				UpperCAmelCase__             =							argparse.ArgumentParser()
				parser.add_argument('-f'       )
				UpperCAmelCase__             =							parser.parse_args()
				return args.f







class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							logging.StreamHandler(sys.stdout			)
									logger.addHandler(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							get_gpu_count()

									if n_gpu > 1:
													pass
													# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
													# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
													# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
													# cmd = [sys.executable] + distributed_args + args
													# execute_subprocess_async(cmd, env=self.get_env())
													# XXX: test the results - need to save them first into .json file
									else:
													args.insert(0    ,'run_glue_deebert.py'			)
													with patch.object(lowerCamelCase__    ,'argv'    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							run_glue_deebert.main()
																	for value in result.values():
																					self.assertGreaterEqual(lowerCamelCase__    ,0.6_6_6			)







					@slow
					@require_torch_non_multi_gpu
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)



