


"""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     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a=13       ,  __a=30       ,  __a=2       ,  __a=3       ,  __a=True       ,  __a=True       ,  __a=32       ,  __a=5       ,  __a=4       ,  __a=37       ,  __a="gelu"       ,  __a=0.1       ,  __a=0.1       ,  __a=10       ,  __a=0.0_2       ,  __a=3       ,  __a=None       ,  __a=2       ,  ):
										__lowerCAmelCase							       =     parent
										__lowerCAmelCase							       =     batch_size
										__lowerCAmelCase							       =     image_size
										__lowerCAmelCase							       =     patch_size
										__lowerCAmelCase							       =     num_channels
										__lowerCAmelCase							       =     is_training
										__lowerCAmelCase							       =     use_labels
										__lowerCAmelCase							       =     hidden_size
										__lowerCAmelCase							       =     num_hidden_layers
										__lowerCAmelCase							       =     num_attention_heads
										__lowerCAmelCase							       =     intermediate_size
										__lowerCAmelCase							       =     hidden_act
										__lowerCAmelCase							       =     hidden_dropout_prob
										__lowerCAmelCase							       =     attention_probs_dropout_prob
										__lowerCAmelCase							       =     type_sequence_label_size
										__lowerCAmelCase							       =     initializer_range
										__lowerCAmelCase							       =     scope
										__lowerCAmelCase							       =     encoder_stride

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

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

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

										__lowerCAmelCase							       =     self.get_config()

										return config, pixel_values, labels

							def      snake_case							(	self      ):
										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=__a       ,  initializer_range=self.initializer_range       ,  encoder_stride=self.encoder_stride       ,  )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     DeiTModel(config=__a      )
										model.to(__a      )
										model.eval()
										__lowerCAmelCase							       =     model(__a      )
										self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     DeiTForMaskedImageModeling(config=__a      )
										model.to(__a      )
										model.eval()
										__lowerCAmelCase							       =     model(__a      )
										self.parent.assertEqual(
										    result.reconstruction.shape       ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

										# test greyscale images
										__lowerCAmelCase							       =     1
										__lowerCAmelCase							       =     DeiTForMaskedImageModeling(__a      )
										model.to(__a      )
										model.eval()

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

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     self.type_sequence_label_size
										__lowerCAmelCase							       =     DeiTForImageClassification(__a      )
										model.to(__a      )
										model.eval()
										__lowerCAmelCase							       =     model(__a       ,  labels=__a      )
										self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)      )

										# test greyscale images
										__lowerCAmelCase							       =     1
										__lowerCAmelCase							       =     DeiTForImageClassification(__a      )
										model.to(__a      )
										model.eval()

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



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.prepare_config_and_inputs()
										(
										    (
										    __lowerCAmelCase
										)       ,      (
										    __lowerCAmelCase
										)       ,      (
										    __lowerCAmelCase
										)       ,      
										)							       =     config_and_inputs
										__lowerCAmelCase							       =     {"pixel_values": pixel_values}
										return config, inputs_dict
@require_torch
class     _UpperCamelCase    (  lowerCAmelCase__			,lowerCAmelCase__			,unittest.TestCase						):
							'''simple docstring'''







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

							__UpperCAmelCase		:       Optional[Any]							 =False
							__UpperCAmelCase		:       Tuple							 =False
							__UpperCAmelCase		:       Union[str, Any]							 =False

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     DeiTModelTester(self      )
										__lowerCAmelCase							       =     ConfigTester(self       ,  config_class=__a       ,  has_text_modality=__a       ,  hidden_size=37      )

							def      snake_case							(	self      ):
										self.config_tester.run_common_tests()

							@unittest.skip(reason="DeiT does not use inputs_embeds"      )
							def      snake_case							(	self      ):
										pass

							def      snake_case							(	self      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs_for_common()

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

							def      snake_case							(	self      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs_for_common()

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

													__lowerCAmelCase							       =     ["pixel_values"]
													self.assertListEqual(arg_names[:1]       ,  __a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*__a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__a      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a=False      ):
										__lowerCAmelCase							       =     super()._prepare_for_class(__a       ,  __a       ,  return_labels=__a      )

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

										return inputs_dict

							def      snake_case							(	self      ):
										if not self.model_tester.is_training:
													return

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

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

							def      snake_case							(	self      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs_for_common()
										if not self.model_tester.is_training:
													return

										__lowerCAmelCase							       =     False
										__lowerCAmelCase							       =     True

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

							def      snake_case							(	self      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs_for_common()

										__lowerCAmelCase							       =     [
										    {"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(__a      ),
													        *get_values(__a      ),
													    ]
													    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
													):
																continue

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

																			__lowerCAmelCase							       =     model_class(__a      )
																			model.to(__a      )
																			model.train()

																			__lowerCAmelCase							       =     self._prepare_for_class(__a       ,  __a       ,  return_labels=__a      )

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

																			__lowerCAmelCase							       =     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=__a      ) as warning_list:
																						__lowerCAmelCase							       =     model(**__a      ).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      snake_case							(	self      ):
										for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowerCAmelCase							       =     DeiTModel.from_pretrained(__a      )
													self.assertIsNotNone(__a      )


def _lowerCamelCase      (						):

			'''simple docstring'''




			__lowerCAmelCase							       =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
			return image


@require_torch
@require_vision
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







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

							@slow
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"      ).to(
										    __a      )

										__lowerCAmelCase							       =     self.default_image_processor
										__lowerCAmelCase							       =     prepare_img()
										__lowerCAmelCase							       =     image_processor(images=__a       ,  return_tensors="pt"      ).to(__a      )

										# forward pass
										with torch.no_grad():
													__lowerCAmelCase							       =     model(**__a      )

										# verify the logits
										__lowerCAmelCase							       =     torch.Size((1, 10_00)      )
										self.assertEqual(outputs.logits.shape       ,  __a      )

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

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



							@slow
							@require_accelerate
							@require_torch_gpu
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     DeiTModel.from_pretrained(
										    "facebook/deit-base-distilled-patch16-224"       ,  torch_dtype=torch.floataa       ,  device_map="auto"      )
										__lowerCAmelCase							       =     self.default_image_processor

										__lowerCAmelCase							       =     prepare_img()
										__lowerCAmelCase							       =     image_processor(images=__a       ,  return_tensors="pt"      )
										__lowerCAmelCase							       =     inputs.pixel_values.to(__a      )

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








"""simple docstring"""


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__						)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =field(default="""image-classification"""			,metadata={"""include_in_asdict_even_if_is_default""": True}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""image""": Image()}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""labels""": ClassLabel}						)
							__UpperCAmelCase		:       str							 ="image"
							__UpperCAmelCase		:       str							 ="labels"

							def      snake_case							(	self       ,  __a      ):
										if self.label_column not in features:
													raise ValueError(f"Column {self.label_column} is not present in features."      )
										if not isinstance(features[self.label_column]       ,  __a      ):
													raise ValueError(f"Column {self.label_column} is not a ClassLabel."      )
										__lowerCAmelCase							       =     copy.deepcopy(self      )
										__lowerCAmelCase							       =     self.label_schema.copy()
										__lowerCAmelCase							       =     features[self.label_column]
										__lowerCAmelCase							       =     label_schema
										return task_template



							@property
							def      snake_case							(	self      ):
										return {
										    self.image_column: "image",
										    self.label_column: "labels",
										}




