
import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import CvtForImageClassification, CvtModel
							from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor




class   __lowercase						(						__snake_case       ):






	def       _a(self				: Dict    ) ->			List[str]:
							_lowercase       :	Optional[int]           =   self.config_class(**self.inputs_dict    )
							self.parent.assertTrue(hasattr(snake_case      ,	"embed_dim"    )    )
							self.parent.assertTrue(hasattr(snake_case      ,	"num_heads"    )    )




class   __lowercase						:






	def __init__(self				: Optional[int]      ,	snake_case				: List[str]      ,	snake_case				: List[str]=13      ,	snake_case				: List[Any]=64      ,	snake_case				: Union[str, Any]=3      ,	snake_case				: Dict=[16, 48, 96]      ,	snake_case				: Dict=[1, 3, 6]      ,	snake_case				: Tuple=[1, 2, 10]      ,	snake_case				: List[Any]=[7, 3, 3]      ,	snake_case				: List[str]=[4, 2, 2]      ,	snake_case				: Optional[int]=[2, 1, 1]      ,	snake_case				: str=[2, 2, 2]      ,	snake_case				: Dict=[False, False, True]      ,	snake_case				: Optional[int]=[0.0, 0.0, 0.0]      ,	snake_case				: Dict=0.02      ,	snake_case				: List[str]=1e-12      ,	snake_case				: Union[str, Any]=True      ,	snake_case				: List[Any]=True      ,	snake_case				: Dict=2      ,	) ->			List[Any]:
							_lowercase       :	Union[str, Any]           =   parent
							_lowercase       :	Optional[int]           =   batch_size
							_lowercase       :	List[Any]           =   image_size
							_lowercase       :	int           =   patch_sizes
							_lowercase       :	Union[str, Any]           =   patch_stride
							_lowercase       :	List[Any]           =   patch_padding
							_lowercase       :	str           =   is_training
							_lowercase       :	List[str]           =   use_labels
							_lowercase       :	Any           =   num_labels
							_lowercase       :	Optional[int]           =   num_channels
							_lowercase       :	Tuple           =   embed_dim
							_lowercase       :	Any           =   num_heads
							_lowercase       :	int           =   stride_kv
							_lowercase       :	Tuple           =   depth
							_lowercase       :	Optional[int]           =   cls_token
							_lowercase       :	Optional[Any]           =   attention_drop_rate
							_lowercase       :	int           =   initializer_range
							_lowercase       :	int           =   layer_norm_eps






	def       _a(self				: Any    ) ->			List[Any]:
							_lowercase       :	List[str]           =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

							_lowercase       :	Tuple           =   None
							if self.use_labels:
													_lowercase       :	Union[str, Any]           =   ids_tensor([self.batch_size]      ,	self.num_labels    )

							_lowercase       :	List[Any]           =   self.get_config()
							return config, pixel_values, labels






	def       _a(self				: int    ) ->			Union[str, Any]:
							return CvtConfig(
							    image_size=self.image_size      ,	num_labels=self.num_labels      ,	num_channels=self.num_channels      ,	embed_dim=self.embed_dim      ,	num_heads=self.num_heads      ,	patch_sizes=self.patch_sizes      ,	patch_padding=self.patch_padding      ,	patch_stride=self.patch_stride      ,	stride_kv=self.stride_kv      ,	depth=self.depth      ,	cls_token=self.cls_token      ,	attention_drop_rate=self.attention_drop_rate      ,	initializer_range=self.initializer_range      ,	)






	def       _a(self				: List[str]      ,	snake_case				: int      ,	snake_case				: Any      ,	snake_case				: Optional[int]    ) ->			str:
							_lowercase       :	Tuple           =   CvtModel(config=snake_case    )
							model.to(snake_case    )
							model.eval()
							_lowercase       :	Any           =   model(snake_case    )
							_lowercase       :	str           =   (self.image_size, self.image_size)
							_lowercase      ,    _lowercase       :	str           =   image_size[0], image_size[1]
							for i in range(len(self.depth    )    ):
													_lowercase       :	int           =   floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1    )
													_lowercase       :	Tuple           =   floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1    )
							self.parent.assertEqual(result.last_hidden_state.shape      ,	(self.batch_size, self.embed_dim[-1], height, width)    )






	def       _a(self				: int      ,	snake_case				: List[Any]      ,	snake_case				: Union[str, Any]      ,	snake_case				: List[Any]    ) ->			Dict:
							_lowercase       :	List[Any]           =   self.num_labels
							_lowercase       :	str           =   CvtForImageClassification(snake_case    )
							model.to(snake_case    )
							model.eval()
							_lowercase       :	Optional[Any]           =   model(snake_case      ,	labels=snake_case    )
							self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.num_labels)    )






	def       _a(self				: List[Any]    ) ->			str:
							_lowercase       :	str           =   self.prepare_config_and_inputs()
							_lowercase      ,    _lowercase      ,    _lowercase       :	Optional[int]           =   config_and_inputs
							_lowercase       :	Optional[int]           =   {"pixel_values": pixel_values}
							return config, inputs_dict




@require_torch
class   __lowercase						(						__snake_case   , __snake_case   , unittest.TestCase       ):
	_A       =   (CvtModel, CvtForImageClassification) if is_torch_available() else ()
	_A       =   (
	    {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   False






	def       _a(self				: str    ) ->			Optional[int]:
							_lowercase       :	Union[str, Any]           =   CvtModelTester(self    )
							_lowercase       :	List[Any]           =   ConfigTester(self      ,	config_class=snake_case      ,	has_text_modality=snake_case      ,	hidden_size=37    )






	def       _a(self				: List[str]    ) ->			int:
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()






	def       _a(self				: Optional[int]    ) ->			Any:
							return






	@unittest.skip(reason="Cvt does not output attentions"    )
	def       _a(self				: List[Any]    ) ->			List[Any]:
							pass






	@unittest.skip(reason="Cvt does not use inputs_embeds"    )
	def       _a(self				: List[str]    ) ->			Union[str, Any]:
							pass






	@unittest.skip(reason="Cvt does not support input and output embeddings"    )
	def       _a(self				: Tuple    ) ->			Dict:
							pass






	def       _a(self				: List[Any]    ) ->			List[Any]:
							_lowercase      ,    _lowercase       :	Optional[Any]           =   self.model_tester.prepare_config_and_inputs_for_common()

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

													_lowercase       :	Optional[Any]           =   ["pixel_values"]
													self.assertListEqual(arg_names[:1]      ,	snake_case    )






	def       _a(self				: Union[str, Any]    ) ->			Tuple:
							_lowercase       :	List[str]           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case    )






	def       _a(self				: List[Any]    ) ->			Optional[int]:
							def check_hidden_states_output(snake_case				: int      ,	snake_case				: Optional[Any]      ,	snake_case				: str    ):
													_lowercase       :	str           =   model_class(snake_case    )
													model.to(snake_case    )
													model.eval()

													with torch.no_grad():
																			_lowercase       :	str           =   model(**self._prepare_for_class(snake_case      ,	snake_case    )    )

													_lowercase       :	List[str]           =   outputs.hidden_states

													_lowercase       :	Any           =   len(self.model_tester.depth    )
													self.assertEqual(len(snake_case    )      ,	snake_case    )

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]    )      ,	[
													        self.model_tester.embed_dim[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ]      ,	)

							_lowercase      ,    _lowercase       :	List[str]           =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													_lowercase       :	int           =   True
													check_hidden_states_output(snake_case      ,	snake_case      ,	snake_case    )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													_lowercase       :	List[str]           =   True

													check_hidden_states_output(snake_case      ,	snake_case      ,	snake_case    )






	def       _a(self				: Optional[Any]    ) ->			int:
							_lowercase       :	Any           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*snake_case    )






	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
	def       _a(self				: Any    ) ->			Optional[int]:
							pass






	@slow
	def       _a(self				: Optional[int]    ) ->			Optional[Any]:
							for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowercase       :	Dict           =   CvtModel.from_pretrained(snake_case    )
													self.assertIsNotNone(snake_case    )



def 		UpperCamelCase     (       )   ->			List[str]:






						'''simple docstring'''

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




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






	@cached_property
	def       _a(self				: Optional[int]    ) ->			Tuple:
							return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )






	@slow
	def       _a(self				: Dict    ) ->			List[Any]:
							_lowercase       :	Optional[Any]           =   CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(snake_case    )

							_lowercase       :	Optional[int]           =   self.default_image_processor
							_lowercase       :	int           =   prepare_img()
							_lowercase       :	Tuple           =   image_processor(images=snake_case      ,	return_tensors="pt"    ).to(snake_case    )

							# forward pass
							with torch.no_grad():
													_lowercase       :	List[str]           =   model(**snake_case    )

							# verify the logits
							_lowercase       :	int           =   torch.Size((1, 1000)    )
							self.assertEqual(outputs.logits.shape      ,	snake_case    )

							_lowercase       :	Optional[int]           =   torch.tensor([0.92_85, 0.90_15, -0.31_50]    ).to(snake_case    )

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






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class   __lowercase						(						__snake_case       ):
	_A       =   (DEISMultistepScheduler,)
	_A       =   (("num_inference_steps", 25),)






	def       _a(self				: Optional[int]      ,	**snake_case				: str    ) ->			Optional[Any]:
							_lowercase       :	str           =   {
							    "num_train_timesteps": 1000,
							    "beta_start": 0.00_01,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "solver_order": 2,
							}

							config.update(**snake_case    )
							return config






	def       _a(self				: Optional[int]      ,	snake_case				: Optional[int]=0      ,	**snake_case				: List[Any]    ) ->			Dict:
							_lowercase       :	Dict           =   dict(self.forward_default_kwargs    )
							_lowercase       :	Optional[Any]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )
							_lowercase       :	Optional[int]           =   self.dummy_sample
							_lowercase       :	Tuple           =   0.1 * sample
							_lowercase       :	Optional[Any]           =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	Dict           =   self.get_scheduler_config(**snake_case    )
													_lowercase       :	int           =   scheduler_class(**snake_case    )
													scheduler.set_timesteps(snake_case    )
													# copy over dummy past residuals
													_lowercase       :	Any           =   dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(snake_case    )
																			_lowercase       :	Any           =   scheduler_class.from_pretrained(snake_case    )
																			new_scheduler.set_timesteps(snake_case    )
																			# copy over dummy past residuals
																			_lowercase       :	int           =   dummy_past_residuals[: new_scheduler.config.solver_order]

													_lowercase      ,    _lowercase       :	Optional[Any]           =   sample, sample
													for t in range(snake_case      ,	time_step + scheduler.config.solver_order + 1    ):
																			_lowercase       :	List[Any]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
																			_lowercase       :	Union[str, Any]           =   new_scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

																			assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"






	def       _a(self				: str    ) ->			List[str]:
							pass






	def       _a(self				: Optional[Any]      ,	snake_case				: List[Any]=0      ,	**snake_case				: str    ) ->			Union[str, Any]:
							_lowercase       :	List[str]           =   dict(self.forward_default_kwargs    )
							_lowercase       :	Optional[int]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )
							_lowercase       :	Dict           =   self.dummy_sample
							_lowercase       :	Union[str, Any]           =   0.1 * sample
							_lowercase       :	List[str]           =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	List[Any]           =   self.get_scheduler_config()
													_lowercase       :	int           =   scheduler_class(**snake_case    )
													scheduler.set_timesteps(snake_case    )

													# copy over dummy past residuals (must be after setting timesteps)
													_lowercase       :	Union[str, Any]           =   dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(snake_case    )
																			_lowercase       :	str           =   scheduler_class.from_pretrained(snake_case    )
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(snake_case    )

																			# copy over dummy past residual (must be after setting timesteps)
																			_lowercase       :	int           =   dummy_past_residuals[: new_scheduler.config.solver_order]

													_lowercase       :	Any           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
													_lowercase       :	Dict           =   new_scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

													assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"






	def       _a(self				: Dict      ,	snake_case				: str=None      ,	**snake_case				: Dict    ) ->			Optional[Any]:
							if scheduler is None:
													_lowercase       :	Optional[Any]           =   self.scheduler_classes[0]
													_lowercase       :	Dict           =   self.get_scheduler_config(**snake_case    )
													_lowercase       :	Optional[int]           =   scheduler_class(**snake_case    )

							_lowercase       :	Tuple           =   self.scheduler_classes[0]
							_lowercase       :	str           =   self.get_scheduler_config(**snake_case    )
							_lowercase       :	Tuple           =   scheduler_class(**snake_case    )

							_lowercase       :	Optional[int]           =   10
							_lowercase       :	List[str]           =   self.dummy_model()
							_lowercase       :	List[Any]           =   self.dummy_sample_deter
							scheduler.set_timesteps(snake_case    )

							for i, t in enumerate(scheduler.timesteps    ):
													_lowercase       :	str           =   model(snake_case      ,	snake_case    )
													_lowercase       :	int           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case    ).prev_sample

							return sample






	def       _a(self				: Any    ) ->			Tuple:
							_lowercase       :	Union[str, Any]           =   dict(self.forward_default_kwargs    )

							_lowercase       :	Optional[int]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	List[Any]           =   self.get_scheduler_config()
													_lowercase       :	Optional[Any]           =   scheduler_class(**snake_case    )

													_lowercase       :	Tuple           =   self.dummy_sample
													_lowercase       :	List[Any]           =   0.1 * sample

													if num_inference_steps is not None and hasattr(snake_case      ,	"set_timesteps"    ):
																			scheduler.set_timesteps(snake_case    )
													elif num_inference_steps is not None and not hasattr(snake_case      ,	"set_timesteps"    ):
																			_lowercase       :	str           =   num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													_lowercase       :	int           =   [residual + 0.2, residual + 0.15, residual + 0.10]
													_lowercase       :	Dict           =   dummy_past_residuals[: scheduler.config.solver_order]

													_lowercase       :	Tuple           =   scheduler.timesteps[5]
													_lowercase       :	Dict           =   scheduler.timesteps[6]

													_lowercase       :	Tuple           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
													_lowercase       :	Union[str, Any]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

													self.assertEqual(output_a.shape      ,	sample.shape    )
													self.assertEqual(output_a.shape      ,	output_a.shape    )






	def       _a(self				: Optional[int]    ) ->			List[Any]:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							_lowercase       :	Any           =   DEISMultistepScheduler(**self.get_scheduler_config()    )
							_lowercase       :	List[Any]           =   self.full_loop(scheduler=snake_case    )
							_lowercase       :	Tuple           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3

							_lowercase       :	str           =   DPMSolverSinglestepScheduler.from_config(scheduler.config    )
							_lowercase       :	Optional[int]           =   DPMSolverMultistepScheduler.from_config(scheduler.config    )
							_lowercase       :	List[Any]           =   UniPCMultistepScheduler.from_config(scheduler.config    )
							_lowercase       :	int           =   DEISMultistepScheduler.from_config(scheduler.config    )

							_lowercase       :	Union[str, Any]           =   self.full_loop(scheduler=snake_case    )
							_lowercase       :	Dict           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3






	def       _a(self				: Union[str, Any]    ) ->			Optional[int]:
							for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=snake_case    )






	def       _a(self				: Optional[Any]    ) ->			List[str]:
							self.check_over_configs(thresholding=snake_case    )
							for order in [1, 2, 3]:
													for solver_type in ["logrho"]:
																			for threshold in [0.5, 1.0, 2.0]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    thresholding=snake_case      ,	prediction_type=snake_case      ,	sample_max_value=snake_case      ,	algorithm_type="deis"      ,	solver_order=snake_case      ,	solver_type=snake_case      ,	)






	def       _a(self				: int    ) ->			Tuple:
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=snake_case    )






	def       _a(self				: Tuple    ) ->			List[Any]:
							for algorithm_type in ["deis"]:
													for solver_type in ["logrho"]:
																			for order in [1, 2, 3]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    solver_order=snake_case      ,	solver_type=snake_case      ,	prediction_type=snake_case      ,	algorithm_type=snake_case      ,	)
																															_lowercase       :	Any           =   self.full_loop(
																															    solver_order=snake_case      ,	solver_type=snake_case      ,	prediction_type=snake_case      ,	algorithm_type=snake_case      ,	)
																															assert not torch.isnan(snake_case    ).any(), "Samples have nan numbers"






	def       _a(self				: int    ) ->			List[str]:
							self.check_over_configs(lower_order_final=snake_case    )
							self.check_over_configs(lower_order_final=snake_case    )






	def       _a(self				: Any    ) ->			List[Any]:
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=snake_case      ,	time_step=0    )






	def       _a(self				: Tuple    ) ->			Optional[Any]:
							_lowercase       :	Optional[Any]           =   self.full_loop()
							_lowercase       :	Tuple           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3






	def       _a(self				: Optional[int]    ) ->			Dict:
							_lowercase       :	int           =   self.full_loop(prediction_type="v_prediction"    )
							_lowercase       :	str           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.0_91    ) < 1e-3






	def       _a(self				: int    ) ->			Optional[int]:
							_lowercase       :	int           =   self.scheduler_classes[0]
							_lowercase       :	Tuple           =   self.get_scheduler_config(thresholding=snake_case      ,	dynamic_thresholding_ratio=0    )
							_lowercase       :	int           =   scheduler_class(**snake_case    )

							_lowercase       :	Optional[int]           =   10
							_lowercase       :	Union[str, Any]           =   self.dummy_model()
							_lowercase       :	Dict           =   self.dummy_sample_deter.half()
							scheduler.set_timesteps(snake_case    )

							for i, t in enumerate(scheduler.timesteps    ):
													_lowercase       :	Tuple           =   model(snake_case      ,	snake_case    )
													_lowercase       :	List[str]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case    ).prev_sample

							assert sample.dtype == torch.floataa




