




from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    LayoutLMvaConfig,
		    TFLayoutLMvaForQuestionAnswering,
		    TFLayoutLMvaForSequenceClassification,
		    TFLayoutLMvaForTokenClassification,
		    TFLayoutLMvaModel,
		)

if is_vision_available():
		from PIL import Image

		from transformers import LayoutLMvaImageProcessor
class 						UpperCAmelCase  :







						'''simple docstring'''





						def __init__(		self				:	int			,      __lowercase				:	int			,      __lowercase				:	int=2			,      __lowercase				:	int=3			,      __lowercase				:	Union[str, Any]=4			,      __lowercase				:	int=2			,      __lowercase				:	List[Any]=7			,      __lowercase				:	List[Any]=True			,      __lowercase				:	Optional[int]=True			,      __lowercase				:	int=True			,      __lowercase				:	Optional[int]=True			,      __lowercase				:	Tuple=99			,      __lowercase				:	Optional[Any]=36			,      __lowercase				:	str=2			,      __lowercase				:	Dict=4			,      __lowercase				:	str=37			,      __lowercase				:	str="gelu"			,      __lowercase				:	Optional[int]=0.1			,      __lowercase				:	List[str]=0.1			,      __lowercase				:	Optional[int]=5_12			,      __lowercase				:	Optional[Any]=16			,      __lowercase				:	Optional[int]=2			,      __lowercase				:	List[str]=0.02			,      __lowercase				:	Union[str, Any]=6			,      __lowercase				:	int=6			,      __lowercase				:	Optional[int]=3			,      __lowercase				:	List[str]=4			,      __lowercase				:	Any=None			,      __lowercase				:	Dict=10_00			,      ):






							"""simple docstring"""






							snake_case_						       =	parent
							snake_case_						       =	batch_size
							snake_case_						       =	num_channels
							snake_case_						       =	image_size
							snake_case_						       =	patch_size
							snake_case_						       =	is_training
							snake_case_						       =	use_input_mask
							snake_case_						       =	use_token_type_ids
							snake_case_						       =	use_labels
							snake_case_						       =	vocab_size
							snake_case_						       =	hidden_size
							snake_case_						       =	num_hidden_layers
							snake_case_						       =	num_attention_heads
							snake_case_						       =	intermediate_size
							snake_case_						       =	hidden_act
							snake_case_						       =	hidden_dropout_prob
							snake_case_						       =	attention_probs_dropout_prob
							snake_case_						       =	max_position_embeddings
							snake_case_						       =	type_vocab_size
							snake_case_						       =	type_sequence_label_size
							snake_case_						       =	initializer_range
							snake_case_						       =	coordinate_size
							snake_case_						       =	shape_size
							snake_case_						       =	num_labels
							snake_case_						       =	num_choices
							snake_case_						       =	scope
							snake_case_						       =	range_bbox

							# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
							snake_case_						       =	text_seq_length
							snake_case_						       =	(image_size // patch_size) ** 2 + 1
							snake_case_						       =	self.text_seq_length + self.image_seq_length






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	ids_tensor([self.batch_size, self.text_seq_length]			,      self.vocab_size		)

							snake_case_						       =	ids_tensor([self.batch_size, self.text_seq_length, 4]			,      self.range_bbox		)
							snake_case_						       =	bbox.numpy()
							# Ensure that bbox is legal
							for i in range(bbox.shape[0]		):
								for j in range(bbox.shape[1]		):
									if bbox[i, j, 3] < bbox[i, j, 1]:
										snake_case_						       =	bbox[i, j, 3]
										snake_case_						       =	bbox[i, j, 1]
										snake_case_						       =	tmp_coordinate
									if bbox[i, j, 2] < bbox[i, j, 0]:
										snake_case_						       =	bbox[i, j, 2]
										snake_case_						       =	bbox[i, j, 0]
										snake_case_						       =	tmp_coordinate
							snake_case_						       =	tf.constant(__lowercase		)

							snake_case_						       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							snake_case_						       =	None
							if self.use_input_mask:
								snake_case_						       =	random_attention_mask([self.batch_size, self.text_seq_length]		)

							snake_case_						       =	None
							if self.use_token_type_ids:
								snake_case_						       =	ids_tensor([self.batch_size, self.text_seq_length]			,      self.type_vocab_size		)

							snake_case_						       =	None
							snake_case_						       =	None
							if self.use_labels:
								snake_case_						       =	ids_tensor([self.batch_size]			,      self.type_sequence_label_size		)
								snake_case_						       =	ids_tensor([self.batch_size, self.text_seq_length]			,      self.num_labels		)

							snake_case_						       =	LayoutLMvaConfig(
							    vocab_size=self.vocab_size			,      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			,      max_position_embeddings=self.max_position_embeddings			,      type_vocab_size=self.type_vocab_size			,      initializer_range=self.initializer_range			,      coordinate_size=self.coordinate_size			,      shape_size=self.shape_size			,      input_size=self.image_size			,      patch_size=self.patch_size			,      )

							return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels






						def 			snake_case__		(		self				:	Tuple			,      __lowercase				:	int			,      __lowercase				:	Optional[int]			,      __lowercase				:	int			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	List[str]			,      __lowercase				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	TFLayoutLMvaModel(config=__lowercase		)

							# text + image
							snake_case_						       =	model(__lowercase			,      pixel_values=__lowercase			,      training=__lowercase		)
							snake_case_						       =	model(
							    __lowercase			,      bbox=__lowercase			,      pixel_values=__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      training=__lowercase			,      )
							snake_case_						       =	model(__lowercase			,      bbox=__lowercase			,      pixel_values=__lowercase			,      training=__lowercase		)

							self.parent.assertEqual(result.last_hidden_state.shape			,      (self.batch_size, self.seq_length, self.hidden_size)		)

							# text only
							snake_case_						       =	model(__lowercase			,      training=__lowercase		)
							self.parent.assertEqual(
							    result.last_hidden_state.shape			,      (self.batch_size, self.text_seq_length, self.hidden_size)		)

							# image only
							snake_case_						       =	model({"pixel_values": pixel_values}			,      training=__lowercase		)
							self.parent.assertEqual(
							    result.last_hidden_state.shape			,      (self.batch_size, self.image_seq_length, self.hidden_size)		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	List[Any]			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	Tuple			,      __lowercase				:	Tuple			,      __lowercase				:	int			,      __lowercase				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	self.num_labels
							snake_case_						       =	TFLayoutLMvaForSequenceClassification(config=__lowercase		)
							snake_case_						       =	model(
							    __lowercase			,      bbox=__lowercase			,      pixel_values=__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      labels=__lowercase			,      training=__lowercase			,      )
							self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.num_labels)		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	List[str]			,      __lowercase				:	Dict			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	str			,      __lowercase				:	str			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	self.num_labels
							snake_case_						       =	TFLayoutLMvaForTokenClassification(config=__lowercase		)
							snake_case_						       =	model(
							    __lowercase			,      bbox=__lowercase			,      pixel_values=__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      labels=__lowercase			,      training=__lowercase			,      )
							self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.text_seq_length, self.num_labels)		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	List[str]			,      __lowercase				:	Optional[int]			,      __lowercase				:	List[str]			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	List[str]			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	2
							snake_case_						       =	TFLayoutLMvaForQuestionAnswering(config=__lowercase		)
							snake_case_						       =	model(
							    __lowercase			,      bbox=__lowercase			,      pixel_values=__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      start_positions=__lowercase			,      end_positions=__lowercase			,      training=__lowercase			,      )
							self.parent.assertEqual(result.start_logits.shape			,      (self.batch_size, self.seq_length)		)
							self.parent.assertEqual(result.end_logits.shape			,      (self.batch_size, self.seq_length)		)






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.prepare_config_and_inputs()
							((snake_case_)       , (snake_case_)       , (snake_case_)       , (snake_case_)       , (snake_case_)       , (snake_case_)       , (snake_case_)       , (snake_case_))						       =	config_and_inputs
							snake_case_						       =	{
							    "input_ids": input_ids,
							    "bbox": bbox,
							    "pixel_values": pixel_values,
							    "token_type_ids": token_type_ids,
							    "attention_mask": input_mask,
							}
							return config, inputs_dict







@require_tf
class 						UpperCAmelCase  (							UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase     ):







						'''simple docstring'''





						lowerCAmelCase_							=  (
						    (
						        TFLayoutLMvaModel,
						        TFLayoutLMvaForQuestionAnswering,
						        TFLayoutLMvaForSequenceClassification,
						        TFLayoutLMvaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						lowerCAmelCase_							=  (
						    {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
						    if is_tf_available()
						    else {}
						)

						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  False






						def 			snake_case__		(		self				:	Dict			,      __lowercase				:	Optional[int]			,      __lowercase				:	str			,      __lowercase				:	str			,      __lowercase				:	int			,      __lowercase				:	str		):






							"""simple docstring"""






							return True






						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	int			,      __lowercase				:	Optional[int]			,      __lowercase				:	List[str]=False		):






							"""simple docstring"""






							snake_case_						       =	copy.deepcopy(__lowercase		)

							if model_class in get_values(__lowercase		):
								snake_case_						       =	{
								    k: tf.tile(tf.expand_dims(__lowercase			,      1		)			,      (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)		)
								    if isinstance(__lowercase			,      tf.Tensor		) and v.ndim > 0
								    else v
								    for k, v in inputs_dict.items()
								}

							if return_labels:
								if model_class in get_values(__lowercase		):
									snake_case_						       =	tf.ones(self.model_tester.batch_size			,      dtype=tf.intaa		)
								elif model_class in get_values(__lowercase		):
									snake_case_						       =	tf.zeros(self.model_tester.batch_size			,      dtype=tf.intaa		)
									snake_case_						       =	tf.zeros(self.model_tester.batch_size			,      dtype=tf.intaa		)
								elif model_class in get_values(__lowercase		):
									snake_case_						       =	tf.zeros(self.model_tester.batch_size			,      dtype=tf.intaa		)
								elif model_class in get_values(__lowercase		):
									snake_case_						       =	tf.zeros(
									    (self.model_tester.batch_size, self.model_tester.text_seq_length)			,      dtype=tf.intaa		)

							return inputs_dict






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	TFLayoutLMvaModelTester(self		)
							snake_case_						       =	ConfigTester(self			,      config_class=__lowercase			,      hidden_size=37		)






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							self.config_tester.run_common_tests()






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_       , snake_case_						       =	self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
								snake_case_						       =	model_class(__lowercase		)
								if getattr(__lowercase			,      "hf_compute_loss"			,      __lowercase		):
									# The number of elements in the loss should be the same as the number of elements in the label
									snake_case_						       =	self._prepare_for_class(inputs_dict.copy()			,      __lowercase			,      return_labels=__lowercase		)
									snake_case_						       =	prepared_for_class[
									    sorted(prepared_for_class.keys() - inputs_dict.keys()			,      reverse=__lowercase		)[0]
									]
									snake_case_						       =	added_label.shape.as_list()[:1]

									# Test that model correctly compute the loss with kwargs
									snake_case_						       =	self._prepare_for_class(inputs_dict.copy()			,      __lowercase			,      return_labels=__lowercase		)
									snake_case_						       =	prepared_for_class.pop("input_ids"		)

									snake_case_						       =	model(__lowercase			,      **__lowercase		)[0]
									self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)

									# Test that model correctly compute the loss when we mask some positions
									snake_case_						       =	self._prepare_for_class(inputs_dict.copy()			,      __lowercase			,      return_labels=__lowercase		)
									snake_case_						       =	prepared_for_class.pop("input_ids"		)
									if "labels" in prepared_for_class:
										snake_case_						       =	prepared_for_class["labels"].numpy()
										if len(labels.shape		) > 1 and labels.shape[1] != 1:
											snake_case_						       =	-1_00
											snake_case_						       =	tf.convert_to_tensor(__lowercase		)
											snake_case_						       =	model(__lowercase			,      **__lowercase		)[0]
											self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)
											self.assertTrue(not np.any(np.isnan(loss.numpy()		)		)		)

                # Test that model correctly compute the loss with a dict
									snake_case_						       =	self._prepare_for_class(inputs_dict.copy()			,      __lowercase			,      return_labels=__lowercase		)
									snake_case_						       =	model(__lowercase		)[0]
									self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)

									# Test that model correctly compute the loss with a tuple
									snake_case_						       =	self._prepare_for_class(inputs_dict.copy()			,      __lowercase			,      return_labels=__lowercase		)

									# Get keys that were added with the _prepare_for_class function
									snake_case_						       =	prepared_for_class.keys() - inputs_dict.keys()
									snake_case_						       =	inspect.signature(model.call		).parameters
									snake_case_						       =	list(signature.keys()		)

									# Create a dictionary holding the location of the tensors in the tuple
									snake_case_						       =	{0: "input_ids"}
									for label_key in label_keys:
										snake_case_						       =	signature_names.index(__lowercase		)
										snake_case_						       =	label_key
									snake_case_						       =	sorted(tuple_index_mapping.items()		)
									# Initialize a list with their default values, update the values and convert to a tuple
									snake_case_						       =	[]

									for name in signature_names:
										if name != "kwargs":
											list_input.append(signature[name].default		)

									for index, value in sorted_tuple_index_mapping:
										snake_case_						       =	prepared_for_class[value]

									snake_case_						       =	tuple(__lowercase		)

									# Send to model
									snake_case_						       =	model(tuple_input[:-1]		)[0]

									self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							(
							    (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , 
							)						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(__lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase		)






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							(
							    (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , 
							)						       =	self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
								snake_case_						       =	type
								self.model_tester.create_and_check_model(__lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase		)






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							(
							    (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , 
							)						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(
							    __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							(
							    (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , 
							)						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(
							    __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase		)






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							(
							    (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , 
							)						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(
							    __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase		)






						@slow
						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_						       =	TFLayoutLMvaModel.from_pretrained(__lowercase		)
								self.assertIsNotNone(__lowercase		)







def 		lowerCamelCase__       (						):







	'''simple docstring'''





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






@require_tf
class 						UpperCAmelCase  (							unittest.TestCase     ):







						'''simple docstring'''





						@cached_property
						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							return LayoutLMvaImageProcessor(apply_ocr=__lowercase		) if is_vision_available() else None






						@slow
						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							snake_case_						       =	TFLayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"		)

							snake_case_						       =	self.default_image_processor
							snake_case_						       =	prepare_img()
							snake_case_						       =	image_processor(images=__lowercase			,      return_tensors="tf"		).pixel_values

							snake_case_						       =	tf.constant([[1, 2]]		)
							snake_case_						       =	tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]		)			,      axis=0		)

							# forward pass
							snake_case_						       =	model(input_ids=__lowercase			,      bbox=__lowercase			,      pixel_values=__lowercase			,      training=__lowercase		)

							# verify the logits
							snake_case_						       =	(1, 1_99, 7_68)
							self.assertEqual(outputs.last_hidden_state.shape			,      __lowercase		)

							snake_case_						       =	tf.constant(
							    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]		)

							self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]			,      __lowercase			,      atol=1E-4		)		)






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np
class 						UpperCAmelCase  :







						'''simple docstring'''





						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							torch.manual_seed(0		)
							snake_case_						       =	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	UNetaDConditionModel(
							    sample_size=32			,      layers_per_block=1			,      block_out_channels=[32, 64]			,      down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]			,      mid_block_type="UNetMidBlock2DSimpleCrossAttn"			,      up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]			,      in_channels=3			,      out_channels=6			,      cross_attention_dim=32			,      encoder_hid_dim=32			,      attention_head_dim=8			,      addition_embed_type="text"			,      addition_embed_type_num_heads=2			,      cross_attention_norm="group_norm"			,      resnet_time_scale_shift="scale_shift"			,      act_fn="gelu"			,      )
							unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							torch.manual_seed(0		)
							snake_case_						       =	DDPMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="squaredcos_cap_v2"			,      beta_start=0.0001			,      beta_end=0.02			,      thresholding=__lowercase			,      dynamic_thresholding_ratio=0.95			,      sample_max_value=1.0			,      prediction_type="epsilon"			,      variance_type="learned_range"			,      )

							torch.manual_seed(0		)
							snake_case_						       =	IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							torch.manual_seed(0		)
							snake_case_						       =	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	UNetaDConditionModel(
							    sample_size=32			,      layers_per_block=[1, 2]			,      block_out_channels=[32, 64]			,      down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]			,      mid_block_type="UNetMidBlock2DSimpleCrossAttn"			,      up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]			,      in_channels=6			,      out_channels=6			,      cross_attention_dim=32			,      encoder_hid_dim=32			,      attention_head_dim=8			,      addition_embed_type="text"			,      addition_embed_type_num_heads=2			,      cross_attention_norm="group_norm"			,      resnet_time_scale_shift="scale_shift"			,      act_fn="gelu"			,      class_embed_type="timestep"			,      mid_block_scale_factor=1.414			,      time_embedding_act_fn="gelu"			,      time_embedding_dim=32			,      )
							unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							torch.manual_seed(0		)
							snake_case_						       =	DDPMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="squaredcos_cap_v2"			,      beta_start=0.0001			,      beta_end=0.02			,      thresholding=__lowercase			,      dynamic_thresholding_ratio=0.95			,      sample_max_value=1.0			,      prediction_type="epsilon"			,      variance_type="learned_range"			,      )

							torch.manual_seed(0		)
							snake_case_						       =	DDPMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="squaredcos_cap_v2"			,      beta_start=0.0001			,      beta_end=0.02			,      )

							torch.manual_seed(0		)
							snake_case_						       =	IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	self.get_dummy_components()
							snake_case_						       =	self.pipeline_class(**__lowercase		)
							pipe.to(__lowercase		)
							pipe.set_progress_bar_config(disable=__lowercase		)

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)

							snake_case_						       =	inputs["prompt"]
							snake_case_						       =	inputs["generator"]
							snake_case_						       =	inputs["num_inference_steps"]
							snake_case_						       =	inputs["output_type"]

							if "image" in inputs:
								snake_case_						       =	inputs["image"]
							else:
								snake_case_						       =	None

							if "mask_image" in inputs:
								snake_case_						       =	inputs["mask_image"]
							else:
								snake_case_						       =	None

							if "original_image" in inputs:
								snake_case_						       =	inputs["original_image"]
							else:
								snake_case_						       =	None

							snake_case_       , snake_case_						       =	pipe.encode_prompt(__lowercase		)

							# inputs with prompt converted to embeddings
							snake_case_						       =	{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
								snake_case_						       =	image

							if mask_image is not None:
								snake_case_						       =	mask_image

							if original_image is not None:
								snake_case_						       =	original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
								setattr(__lowercase			,      __lowercase			,      __lowercase		)

							snake_case_						       =	pipe(**__lowercase		)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(__lowercase		)
								snake_case_						       =	self.pipeline_class.from_pretrained(__lowercase		)
								pipe_loaded.to(__lowercase		)
								pipe_loaded.set_progress_bar_config(disable=__lowercase		)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							for optional_component in pipe._optional_components:
								self.assertTrue(
								    getattr(__lowercase			,      __lowercase		) is None			,      f"`{optional_component}` did not stay set to None after loading."			,      )

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)

							snake_case_						       =	inputs["generator"]
							snake_case_						       =	inputs["num_inference_steps"]
							snake_case_						       =	inputs["output_type"]

							# inputs with prompt converted to embeddings
							snake_case_						       =	{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
								snake_case_						       =	image

							if mask_image is not None:
								snake_case_						       =	mask_image

							if original_image is not None:
								snake_case_						       =	original_image

							snake_case_						       =	pipe_loaded(**__lowercase		)[0]

							snake_case_						       =	np.abs(to_np(__lowercase		) - to_np(__lowercase		)		).max()
							self.assertLess(__lowercase			,      1E-4		)






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	self.get_dummy_components()
							snake_case_						       =	self.pipeline_class(**__lowercase		)
							pipe.to(__lowercase		)
							pipe.set_progress_bar_config(disable=__lowercase		)

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)
							snake_case_						       =	pipe(**__lowercase		)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(__lowercase		)
								snake_case_						       =	self.pipeline_class.from_pretrained(__lowercase		)
								pipe_loaded.to(__lowercase		)
								pipe_loaded.set_progress_bar_config(disable=__lowercase		)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)
							snake_case_						       =	pipe_loaded(**__lowercase		)[0]

							snake_case_						       =	np.abs(to_np(__lowercase		) - to_np(__lowercase		)		).max()
							self.assertLess(__lowercase			,      1E-4		)
