

"""simple docstring"""

import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
				import torch

				from transformers import (
				    LayoutLMForMaskedLM,
				    LayoutLMForQuestionAnswering,
				    LayoutLMForSequenceClassification,
				    LayoutLMForTokenClassification,
				    LayoutLMModel,
				)






class  __a				:





							"""simple docstring"""


							def __init__(      self ,				snake_case ,				snake_case=13 ,				snake_case=7 ,				snake_case=True ,				snake_case=True ,				snake_case=True ,				snake_case=True ,				snake_case=99 ,				snake_case=32 ,				snake_case=5 ,				snake_case=4 ,				snake_case=37 ,				snake_case="gelu" ,				snake_case=0.1 ,				snake_case=0.1 ,				snake_case=512 ,				snake_case=16 ,				snake_case=2 ,				snake_case=0.02 ,				snake_case=3 ,				snake_case=4 ,				snake_case=None ,				snake_case=1_000 ,				):





								"""simple docstring"""

								lowerCAmelCase__      :							Union[str, Any]    =							parent
								lowerCAmelCase__      :							str    =							batch_size
								lowerCAmelCase__      :							List[Any]    =							seq_length
								lowerCAmelCase__      :							Tuple    =							is_training
								lowerCAmelCase__      :							int    =							use_input_mask
								lowerCAmelCase__      :							Tuple    =							use_token_type_ids
								lowerCAmelCase__      :							str    =							use_labels
								lowerCAmelCase__      :							Any    =							vocab_size
								lowerCAmelCase__      :							List[str]    =							hidden_size
								lowerCAmelCase__      :							str    =							num_hidden_layers
								lowerCAmelCase__      :							Tuple    =							num_attention_heads
								lowerCAmelCase__      :							List[str]    =							intermediate_size
								lowerCAmelCase__      :							Tuple    =							hidden_act
								lowerCAmelCase__      :							str    =							hidden_dropout_prob
								lowerCAmelCase__      :							List[str]    =							attention_probs_dropout_prob
								lowerCAmelCase__      :							Union[str, Any]    =							max_position_embeddings
								lowerCAmelCase__      :							str    =							type_vocab_size
								lowerCAmelCase__      :							Optional[int]    =							type_sequence_label_size
								lowerCAmelCase__      :							str    =							initializer_range
								lowerCAmelCase__      :							List[str]    =							num_labels
								lowerCAmelCase__      :							str    =							num_choices
								lowerCAmelCase__      :							Any    =							scope
								lowerCAmelCase__      :							int    =							range_bbox






							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								lowerCAmelCase__      :							Optional[int]    =							ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size      )

								lowerCAmelCase__      :							Optional[Any]    =							ids_tensor([self.batch_size, self.seq_length, 4] ,				self.range_bbox      )
								# 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]:
											lowerCAmelCase__      :							str    =							bbox[i, j, 3]
											lowerCAmelCase__      :							List[Any]    =							bbox[i, j, 1]
											lowerCAmelCase__      :							Optional[Any]    =							t
										if bbox[i, j, 2] < bbox[i, j, 0]:
											lowerCAmelCase__      :							str    =							bbox[i, j, 2]
											lowerCAmelCase__      :							Optional[int]    =							bbox[i, j, 0]
											lowerCAmelCase__      :							Union[str, Any]    =							t

								lowerCAmelCase__      :							List[Any]    =							None
								if self.use_input_mask:
									lowerCAmelCase__      :							List[Any]    =							ids_tensor([self.batch_size, self.seq_length] ,				vocab_size=2      )

								lowerCAmelCase__      :							int    =							None
								if self.use_token_type_ids:
									lowerCAmelCase__      :							int    =							ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size      )

								lowerCAmelCase__      :							Dict    =							None
								lowerCAmelCase__      :							int    =							None
								lowerCAmelCase__      :							List[Any]    =							None
								if self.use_labels:
									lowerCAmelCase__      :							int    =							ids_tensor([self.batch_size] ,				self.type_sequence_label_size      )
									lowerCAmelCase__      :							Any    =							ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels      )
									lowerCAmelCase__      :							Dict    =							ids_tensor([self.batch_size] ,				self.num_choices      )

								lowerCAmelCase__      :							Any    =							self.get_config()

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






							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								return LayoutLMConfig(
								    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 ,				)






							def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





								"""simple docstring"""

								lowerCAmelCase__      :							Optional[int]    =							LayoutLMModel(config=snake_case      )
								model.to(snake_case      )
								model.eval()
								lowerCAmelCase__      :							Any    =							model(snake_case ,				snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case      )
								lowerCAmelCase__      :							str    =							model(snake_case ,				snake_case ,				token_type_ids=snake_case      )
								lowerCAmelCase__      :							Dict    =							model(snake_case ,				snake_case      )
								self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)      )
								self.parent.assertEqual(result.pooler_output.shape ,				(self.batch_size, self.hidden_size)      )






							def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





								"""simple docstring"""

								lowerCAmelCase__      :							Tuple    =							LayoutLMForMaskedLM(config=snake_case      )
								model.to(snake_case      )
								model.eval()
								lowerCAmelCase__      :							str    =							model(snake_case ,				snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				labels=snake_case      )
								self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)      )






							def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





								"""simple docstring"""

								lowerCAmelCase__      :							Optional[Any]    =							self.num_labels
								lowerCAmelCase__      :							List[str]    =							LayoutLMForSequenceClassification(snake_case      )
								model.to(snake_case      )
								model.eval()
								lowerCAmelCase__      :							Tuple    =							model(
								    snake_case ,				snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				labels=snake_case      )
								self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )






							def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





								"""simple docstring"""

								lowerCAmelCase__      :							int    =							self.num_labels
								lowerCAmelCase__      :							int    =							LayoutLMForTokenClassification(config=snake_case      )
								model.to(snake_case      )
								model.eval()
								lowerCAmelCase__      :							Union[str, Any]    =							model(snake_case ,				snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				labels=snake_case      )
								self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)      )






							def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





								"""simple docstring"""

								lowerCAmelCase__      :							Optional[int]    =							LayoutLMForQuestionAnswering(config=snake_case      )
								model.to(snake_case      )
								model.eval()
								lowerCAmelCase__      :							Optional[int]    =							model(
								    snake_case ,				bbox=snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				start_positions=snake_case ,				end_positions=snake_case ,				)
								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  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								lowerCAmelCase__      :							Any    =							self.prepare_config_and_inputs()
								(
								    lowerCAmelCase__	
								)      :							Optional[int]    =							config_and_inputs
								lowerCAmelCase__      :							Any    =							{
								    "input_ids": input_ids,
								    "bbox": bbox,
								    "token_type_ids": token_type_ids,
								    "attention_mask": input_mask,
								}
								return config, inputs_dict






@require_torch
class  __a				(	__magic_name__					,  __magic_name__					,  unittest.TestCase					):





							"""simple docstring"""


							__UpperCamelCase				:			List[Any]          =	(
							    (
							        LayoutLMModel,
							        LayoutLMForMaskedLM,
							        LayoutLMForSequenceClassification,
							        LayoutLMForTokenClassification,
							        LayoutLMForQuestionAnswering,
							    )
							    if is_torch_available()
							    else None
							)
							__UpperCamelCase				:			str          =	(
							    {
							        'document-question-answering': LayoutLMForQuestionAnswering,
							        'feature-extraction': LayoutLMModel,
							        'fill-mask': LayoutLMForMaskedLM,
							        'text-classification': LayoutLMForSequenceClassification,
							        'token-classification': LayoutLMForTokenClassification,
							        'zero-shot': LayoutLMForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__UpperCamelCase				:			List[Any]          =	True






							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								lowerCAmelCase__      :							List[Any]    =							LayoutLMModelTester(self      )
								lowerCAmelCase__      :							Optional[Any]    =							ConfigTester(self ,				config_class=snake_case ,				hidden_size=37      )






							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								self.config_tester.run_common_tests()






							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								lowerCAmelCase__      :							Dict    =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case      )






							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								lowerCAmelCase__      :							Optional[int]    =							self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
									lowerCAmelCase__      :							List[Any]    =							type
									self.model_tester.create_and_check_model(*snake_case      )






							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								lowerCAmelCase__      :							Union[str, Any]    =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*snake_case      )






							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								lowerCAmelCase__      :							Optional[Any]    =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*snake_case      )






							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								lowerCAmelCase__      :							List[str]    =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*snake_case      )






							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								lowerCAmelCase__      :							int    =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*snake_case      )






							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."      )
							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								pass




def 			SCREAMING_SNAKE_CASE       (   ) ->	List[str]:
	# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
	# fmt: off
	lowerCAmelCase__      :							Dict    =							torch.tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]				,				device=lowercase__      )  # noqa: E231
	lowerCAmelCase__      :							Dict    =							torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]				,				device=lowercase__      )  # noqa: E231
	lowerCAmelCase__      :							Optional[Any]    =							torch.tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]				,				device=lowercase__      )  # noqa: E231
	lowerCAmelCase__      :							str    =							torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]				,				device=lowercase__      )  # noqa: E231
	# these are sequence labels (i.e. at the token level)
	lowerCAmelCase__      :							Dict    =							torch.tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]				,				device=lowercase__      )  # noqa: E231
	# fmt: on

	return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class  __a				(	unittest.TestCase					):





							"""simple docstring"""


							@slow
							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								lowerCAmelCase__      :							List[Any]    =							LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"      ).to(snake_case      )

								lowerCAmelCase__      :							Dict    =							prepare_layoutlm_batch_inputs()

								# forward pass
								lowerCAmelCase__      :							str    =							model(input_ids=snake_case ,				bbox=snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case      )

								# test the sequence output on [0, :3, :3]
								lowerCAmelCase__      :							Optional[Any]    =							torch.tensor(
								    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]] ,				device=snake_case ,				)

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

								# test the pooled output on [1, :3]
								lowerCAmelCase__      :							Optional[Any]    =							torch.tensor([-0.6_580, -0.0_214, 0.8_552] ,				device=snake_case      )

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






							@slow
							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								lowerCAmelCase__      :							Optional[Any]    =							LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased" ,				num_labels=2      ).to(
								    snake_case      )

								lowerCAmelCase__      :							Union[str, Any]    =							prepare_layoutlm_batch_inputs()

								# forward pass
								lowerCAmelCase__      :							List[str]    =							model(
								    input_ids=snake_case ,				bbox=snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				labels=torch.tensor([1, 1] ,				device=snake_case      ) ,				)

								# test whether we get a loss as a scalar
								lowerCAmelCase__      :							Tuple    =							outputs.loss
								lowerCAmelCase__      :							List[str]    =							torch.Size([]      )
								self.assertEqual(loss.shape ,				snake_case      )

								# test the shape of the logits
								lowerCAmelCase__      :							List[str]    =							outputs.logits
								lowerCAmelCase__      :							List[str]    =							torch.Size((2, 2)      )
								self.assertEqual(logits.shape ,				snake_case      )






							@slow
							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								lowerCAmelCase__      :							List[str]    =							LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased" ,				num_labels=13      ).to(
								    snake_case      )

								lowerCAmelCase__      :							List[Any]    =							prepare_layoutlm_batch_inputs()

								# forward pass
								lowerCAmelCase__      :							List[Any]    =							model(
								    input_ids=snake_case ,				bbox=snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				labels=snake_case      )

								# test the loss calculation to be around 2.65
								# expected_loss = torch.tensor(2.65, device=torch_device)

								# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
								# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

								# test the shape of the logits
								lowerCAmelCase__      :							Dict    =							outputs.logits
								lowerCAmelCase__      :							Optional[Any]    =							torch.Size((2, 25, 13)      )
								self.assertEqual(logits.shape ,				snake_case      )






							@slow
							def  SCREAMING_SNAKE_CASE_  (      self      ):





								"""simple docstring"""

								lowerCAmelCase__      :							List[Any]    =							LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"      ).to(snake_case      )

								lowerCAmelCase__      :							Dict    =							prepare_layoutlm_batch_inputs()

								# forward pass
								lowerCAmelCase__      :							Any    =							model(input_ids=snake_case ,				bbox=snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case      )

								# test the shape of the logits
								lowerCAmelCase__      :							Any    =							torch.Size((2, 25)      )
								self.assertEqual(outputs.start_logits.shape ,				snake_case      )
								self.assertEqual(outputs.end_logits.shape ,				snake_case      )









"""simple docstring"""

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Optional[int]          =	(VQDiffusionScheduler,)






       def  SCREAMING_SNAKE_CASE_  (      self ,				**snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							{
            "num_vec_classes": 4_097,
            "num_train_timesteps": 100,
        }

        config.update(**snake_case      )
        return config






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							4
        lowerCAmelCase__      :							Optional[Any]    =							8
        lowerCAmelCase__      :							Union[str, Any]    =							8

        lowerCAmelCase__      :							Optional[Any]    =							torch.randint(0 ,				snake_case ,				(batch_size, height * width)      )

        return sample






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        assert False






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        def model(snake_case ,				snake_case ,				*snake_case      ):
         lowerCAmelCase__ ,	lowerCAmelCase__      :							str    =							sample.shape
         lowerCAmelCase__      :							Dict    =							torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)      )
         lowerCAmelCase__      :							int    =							F.log_softmax(logits.double() ,				dim=1      ).float()
         return return_value

        return model






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for timesteps in [2, 5, 100, 1_000]:
         self.check_over_configs(num_train_timesteps=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for num_vec_classes in [5, 100, 1_000, 4_000]:
         self.check_over_configs(num_vec_classes=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for t in [0, 50, 99]:
         self.check_over_forward(time_step=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






