


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 _lowerCamelCase:
       def __init__(      self,   lowerCamelCase,   lowerCamelCase=2,   lowerCamelCase=3,   lowerCamelCase=4,   lowerCamelCase=2,   lowerCamelCase=7,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=99,   lowerCamelCase=36,   lowerCamelCase=2,   lowerCamelCase=4,   lowerCamelCase=37,   lowerCamelCase="gelu",   lowerCamelCase=0.1,   lowerCamelCase=0.1,   lowerCamelCase=5_12,   lowerCamelCase=16,   lowerCamelCase=2,   lowerCamelCase=0.0_2,   lowerCamelCase=6,   lowerCamelCase=6,   lowerCamelCase=3,   lowerCamelCase=4,   lowerCamelCase=None,   lowerCamelCase=10_00,   ) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							int   	=	parent
           _lowercase :							Dict   	=	batch_size
           _lowercase :							List[Any]   	=	num_channels
           _lowercase :							str   	=	image_size
           _lowercase :							int   	=	patch_size
           _lowercase :							Optional[Any]   	=	is_training
           _lowercase :							Optional[Any]   	=	use_input_mask
           _lowercase :							Optional[int]   	=	use_token_type_ids
           _lowercase :							int   	=	use_labels
           _lowercase :							Union[str, Any]   	=	vocab_size
           _lowercase :							Any   	=	hidden_size
           _lowercase :							List[Any]   	=	num_hidden_layers
           _lowercase :							Optional[Any]   	=	num_attention_heads
           _lowercase :							str   	=	intermediate_size
           _lowercase :							Union[str, Any]   	=	hidden_act
           _lowercase :							str   	=	hidden_dropout_prob
           _lowercase :							Dict   	=	attention_probs_dropout_prob
           _lowercase :							int   	=	max_position_embeddings
           _lowercase :							Tuple   	=	type_vocab_size
           _lowercase :							str   	=	type_sequence_label_size
           _lowercase :							Tuple   	=	initializer_range
           _lowercase :							Dict   	=	coordinate_size
           _lowercase :							List[str]   	=	shape_size
           _lowercase :							Optional[int]   	=	num_labels
           _lowercase :							str   	=	num_choices
           _lowercase :							Tuple   	=	scope
           _lowercase :							Optional[int]   	=	range_bbox

           # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
           _lowercase :							Optional[int]   	=	text_seq_length
           _lowercase :							str   	=	(image_size // patch_size) ** 2 + 1
           _lowercase :							List[str]   	=	self.text_seq_length + self.image_seq_length
       def     UpperCamelCase  (      self) ->     Any:




           """simple docstring"""

           _lowercase :							List[Any]   	=	ids_tensor([self.batch_size, self.text_seq_length],   self.vocab_size)

           _lowercase :							List[str]   	=	ids_tensor([self.batch_size, self.text_seq_length, 4],   self.range_bbox)
           _lowercase :							List[Any]   	=	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]:
                       _lowercase :							Any   	=	bbox[i, j, 3]
                       _lowercase :							Union[str, Any]   	=	bbox[i, j, 1]
                       _lowercase :							Dict   	=	tmp_coordinate
                   if bbox[i, j, 2] < bbox[i, j, 0]:
                       _lowercase :							Optional[int]   	=	bbox[i, j, 2]
                       _lowercase :							List[str]   	=	bbox[i, j, 0]
                       _lowercase :							int   	=	tmp_coordinate
           _lowercase :							Any   	=	tf.constant(lowerCamelCase)

           _lowercase :							int   	=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _lowercase :							Any   	=	None
           if self.use_input_mask:
               _lowercase :							int   	=	random_attention_mask([self.batch_size, self.text_seq_length])

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

           _lowercase :							Dict   	=	None
           _lowercase :							List[str]   	=	None
           if self.use_labels:
               _lowercase :							List[Any]   	=	ids_tensor([self.batch_size],   self.type_sequence_label_size)
               _lowercase :							List[str]   	=	ids_tensor([self.batch_size, self.text_seq_length],   self.num_labels)

           _lowercase :							Dict   	=	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     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     int:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	TFLayoutLMvaModel(config=lowerCamelCase)

           # text + image
           _lowercase :							Dict   	=	model(lowerCamelCase,   pixel_values=lowerCamelCase,   training=lowerCamelCase)
           _lowercase :							Union[str, Any]   	=	model(
               lowerCamelCase,   bbox=lowerCamelCase,   pixel_values=lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   training=lowerCamelCase,   )
           _lowercase :							Union[str, Any]   	=	model(lowerCamelCase,   bbox=lowerCamelCase,   pixel_values=lowerCamelCase,   training=lowerCamelCase)

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

           # text only
           _lowercase :							Optional[Any]   	=	model(lowerCamelCase,   training=lowerCamelCase)
           self.parent.assertEqual(
               result.last_hidden_state.shape,   (self.batch_size, self.text_seq_length, self.hidden_size))

           # image only
           _lowercase :							List[str]   	=	model({'pixel_values': pixel_values},   training=lowerCamelCase)
           self.parent.assertEqual(
               result.last_hidden_state.shape,   (self.batch_size, self.image_seq_length, self.hidden_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     List[Any]:




           """simple docstring"""

           _lowercase :							int   	=	self.num_labels
           _lowercase :							Union[str, Any]   	=	TFLayoutLMvaForSequenceClassification(config=lowerCamelCase)
           _lowercase :							Union[str, Any]   	=	model(
               lowerCamelCase,   bbox=lowerCamelCase,   pixel_values=lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase,   training=lowerCamelCase,   )
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     int:




           """simple docstring"""

           _lowercase :							List[str]   	=	self.num_labels
           _lowercase :							List[str]   	=	TFLayoutLMvaForTokenClassification(config=lowerCamelCase)
           _lowercase :							int   	=	model(
               lowerCamelCase,   bbox=lowerCamelCase,   pixel_values=lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase,   training=lowerCamelCase,   )
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.text_seq_length, self.num_labels))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     int:




           """simple docstring"""

           _lowercase :							int   	=	2
           _lowercase :							Any   	=	TFLayoutLMvaForQuestionAnswering(config=lowerCamelCase)
           _lowercase :							str   	=	model(
               lowerCamelCase,   bbox=lowerCamelCase,   pixel_values=lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   start_positions=lowerCamelCase,   end_positions=lowerCamelCase,   training=lowerCamelCase,   )
           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     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.prepare_config_and_inputs()
           ((_lowercase)	,       (_lowercase)	,       (_lowercase)	,       (_lowercase)	,       (_lowercase)	,       (_lowercase)	,       (_lowercase)	,       (_lowercase)) :							List[str]   	=	config_and_inputs
           _lowercase :							int   	=	{
               '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 _lowerCamelCase(		_a,				_a,				unittest.TestCase     ):
       lowercase_				:			Optional[int]									=    (
           (
               TFLayoutLMvaModel,
               TFLayoutLMvaForQuestionAnswering,
               TFLayoutLMvaForSequenceClassification,
               TFLayoutLMvaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       lowercase_				:			Any									=    (
           {"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
           if is_tf_available()
           else {}
       )

       lowercase_				:			str									=    False
       lowercase_				:			Union[str, Any]									=    False
       lowercase_				:			List[Any]									=    False
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Dict:




           """simple docstring"""

           return True
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase=False) ->     dict:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	copy.deepcopy(lowerCamelCase)

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

           if return_labels:
               if model_class in get_values(lowerCamelCase):
                   _lowercase :							Dict   	=	tf.ones(self.model_tester.batch_size,   dtype=tf.intaa)
               elif model_class in get_values(lowerCamelCase):
                   _lowercase :							Optional[int]   	=	tf.zeros(self.model_tester.batch_size,   dtype=tf.intaa)
                   _lowercase :							int   	=	tf.zeros(self.model_tester.batch_size,   dtype=tf.intaa)
               elif model_class in get_values(lowerCamelCase):
                   _lowercase :							Dict   	=	tf.zeros(self.model_tester.batch_size,   dtype=tf.intaa)
               elif model_class in get_values(lowerCamelCase):
                   _lowercase :							str   	=	tf.zeros(
                       (self.model_tester.batch_size, self.model_tester.text_seq_length),   dtype=tf.intaa)

           return inputs_dict
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							str   	=	TFLayoutLMvaModelTester(self)
           _lowercase :							Tuple   	=	ConfigTester(self,   config_class=lowerCamelCase,   hidden_size=37)
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase	,       _lowercase :							List[Any]   	=	self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
               _lowercase :							str   	=	model_class(lowerCamelCase)
               if getattr(lowerCamelCase,   'hf_compute_loss',   lowerCamelCase):
                   # The number of elements in the loss should be the same as the number of elements in the label
                   _lowercase :							Optional[Any]   	=	self._prepare_for_class(inputs_dict.copy(),   lowerCamelCase,   return_labels=lowerCamelCase)
                   _lowercase :							Dict   	=	prepared_for_class[
                       sorted(prepared_for_class.keys() - inputs_dict.keys(),   reverse=lowerCamelCase)[0]
                   ]
                   _lowercase :							Tuple   	=	added_label.shape.as_list()[:1]

                   # Test that model correctly compute the loss with kwargs
                   _lowercase :							int   	=	self._prepare_for_class(inputs_dict.copy(),   lowerCamelCase,   return_labels=lowerCamelCase)
                   _lowercase :							Dict   	=	prepared_for_class.pop('input_ids')

                   _lowercase :							Any   	=	model(lowerCamelCase,   **lowerCamelCase)[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
                   _lowercase :							Any   	=	self._prepare_for_class(inputs_dict.copy(),   lowerCamelCase,   return_labels=lowerCamelCase)
                   _lowercase :							List[Any]   	=	prepared_for_class.pop('input_ids')
                   if "labels" in prepared_for_class:
                       _lowercase :							Tuple   	=	prepared_for_class['labels'].numpy()
                       if len(labels.shape) > 1 and labels.shape[1] != 1:
                           _lowercase :							Optional[int]   	=	-1_00
                           _lowercase :							int   	=	tf.convert_to_tensor(lowerCamelCase)
                           _lowercase :							Optional[Any]   	=	model(lowerCamelCase,   **lowerCamelCase)[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
                   _lowercase :							List[str]   	=	self._prepare_for_class(inputs_dict.copy(),   lowerCamelCase,   return_labels=lowerCamelCase)
                   _lowercase :							List[Any]   	=	model(lowerCamelCase)[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
                   _lowercase :							Optional[int]   	=	self._prepare_for_class(inputs_dict.copy(),   lowerCamelCase,   return_labels=lowerCamelCase)

                   # Get keys that were added with the _prepare_for_class function
                   _lowercase :							List[str]   	=	prepared_for_class.keys() - inputs_dict.keys()
                   _lowercase :							int   	=	inspect.signature(model.call).parameters
                   _lowercase :							Optional[int]   	=	list(signature.keys())

                   # Create a dictionary holding the location of the tensors in the tuple
                   _lowercase :							str   	=	{0: 'input_ids'}
                   for label_key in label_keys:
                       _lowercase :							List[str]   	=	signature_names.index(lowerCamelCase)
                       _lowercase :							Tuple   	=	label_key
                   _lowercase :							Tuple   	=	sorted(tuple_index_mapping.items())
                   # Initialize a list with their default values, update the values and convert to a tuple
                   _lowercase :							Optional[int]   	=	[]

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

                   for index, value in sorted_tuple_index_mapping:
                       _lowercase :							Dict   	=	prepared_for_class[value]

                   _lowercase :							Any   	=	tuple(lowerCamelCase)

                   # Send to model
                   _lowercase :							Union[str, Any]   	=	model(tuple_input[:-1])[0]

                   self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							Optional[Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase)
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							Optional[Any]   	=	self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
               _lowercase :							Tuple   	=	type
               self.model_tester.create_and_check_model(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							str   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(
               lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							Any   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(
               lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase)
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							str   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(
               lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase)




       @slow
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase :							str   	=	TFLayoutLMvaModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)



def        UpperCamelCase_(   )					->		Tuple:
    _lowercase :							Optional[Any]   	=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
    return image






@require_tf
class _lowerCamelCase(		unittest.TestCase     ):
       @cached_property
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

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




       @slow
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							str   	=	TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base')

           _lowercase :							Optional[Any]   	=	self.default_image_processor
           _lowercase :							Union[str, Any]   	=	prepare_img()
           _lowercase :							Optional[Any]   	=	image_processor(images=lowerCamelCase,   return_tensors='tf').pixel_values

           _lowercase :							List[Any]   	=	tf.constant([[1, 2]])
           _lowercase :							Optional[Any]   	=	tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]),   axis=0)

           # forward pass
           _lowercase :							Union[str, Any]   	=	model(input_ids=lowerCamelCase,   bbox=lowerCamelCase,   pixel_values=lowerCamelCase,   training=lowerCamelCase)

           # verify the logits
           _lowercase :							str   	=	(1, 1_99, 7_68)
           self.assertEqual(outputs.last_hidden_state.shape,   lowerCamelCase)

           _lowercase :							Dict   	=	tf.constant(
               [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]])

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






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class _lowerCamelCase(		_a     ):
       lowercase_				:			List[Any]									=    (DPMSolverSinglestepScheduler,)
       lowercase_				:			List[str]									=    (("""num_inference_steps""", 25),)
       def     UpperCamelCase  (      self,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	{
               'num_train_timesteps': 10_00,
               'beta_start': 0.0_0_0_1,
               'beta_end': 0.0_2,
               'beta_schedule': 'linear',
               'solver_order': 2,
               'prediction_type': 'epsilon',
               'thresholding': False,
               'sample_max_value': 1.0,
               'algorithm_type': 'dpmsolver++',
               'solver_type': 'midpoint',
               'lambda_min_clipped': -float('inf'),
               'variance_type': None,
           }

           config.update(**lowerCamelCase)
           return config
       def     UpperCamelCase  (      self,   lowerCamelCase=0,   **lowerCamelCase) ->     Any:




           """simple docstring"""

           _lowercase :							Dict   	=	dict(self.forward_default_kwargs)
           _lowercase :							Union[str, Any]   	=	kwargs.pop('num_inference_steps',   lowerCamelCase)
           _lowercase :							Optional[int]   	=	self.dummy_sample
           _lowercase :							Optional[int]   	=	0.1 * sample
           _lowercase :							Optional[int]   	=	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

               _lowercase	,       _lowercase :							List[Any]   	=	sample, sample
               for t in range(lowerCamelCase,   time_step + scheduler.config.solver_order + 1):
                   _lowercase :							Dict   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase).prev_sample
                   _lowercase :							int   	=	new_scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase).prev_sample

                   assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           pass
       def     UpperCamelCase  (      self,   lowerCamelCase=0,   **lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	dict(self.forward_default_kwargs)
           _lowercase :							List[str]   	=	kwargs.pop('num_inference_steps',   lowerCamelCase)
           _lowercase :							List[str]   	=	self.dummy_sample
           _lowercase :							str   	=	0.1 * sample
           _lowercase :							Optional[Any]   	=	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowerCamelCase)
                   _lowercase :							List[Any]   	=	scheduler_class.from_pretrained(lowerCamelCase)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(lowerCamelCase)

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

               _lowercase :							Optional[int]   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase).prev_sample
               _lowercase :							List[Any]   	=	new_scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase).prev_sample

               assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
       def     UpperCamelCase  (      self,   lowerCamelCase=None,   **lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           if scheduler is None:
               _lowercase :							str   	=	self.scheduler_classes[0]
               _lowercase :							int   	=	self.get_scheduler_config(**lowerCamelCase)
               _lowercase :							Optional[Any]   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							List[Any]   	=	self.scheduler_classes[0]
           _lowercase :							Optional[int]   	=	self.get_scheduler_config(**lowerCamelCase)
           _lowercase :							Optional[Any]   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							List[Any]   	=	10
           _lowercase :							List[str]   	=	self.dummy_model()
           _lowercase :							Dict   	=	self.dummy_sample_deter
           scheduler.set_timesteps(lowerCamelCase)

           for i, t in enumerate(scheduler.timesteps):
               _lowercase :							Optional[int]   	=	model(lowerCamelCase,   lowerCamelCase)
               _lowercase :							Optional[int]   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           return sample
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Dict   	=	DPMSolverSinglestepScheduler(**self.get_scheduler_config())
           _lowercase :							Optional[int]   	=	50
           _lowercase :							Union[str, Any]   	=	self.dummy_model()
           _lowercase :							Tuple   	=	self.dummy_sample_deter
           scheduler.set_timesteps(lowerCamelCase)

           # make sure that the first t is uneven
           for i, t in enumerate(scheduler.timesteps[3:]):
               _lowercase :							Optional[Any]   	=	model(lowerCamelCase,   lowerCamelCase)
               _lowercase :							int   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           _lowercase :							Optional[int]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_5_7_4) < 1E-3
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           for timesteps in [25, 50, 1_00, 9_99, 10_00]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							List[str]   	=	DPMSolverSinglestepScheduler(**self.get_scheduler_config())
           _lowercase :							List[str]   	=	self.full_loop(scheduler=lowerCamelCase)
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3

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

           _lowercase :							Any   	=	self.full_loop(scheduler=lowerCamelCase)
           _lowercase :							Optional[int]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           self.check_over_configs(thresholding=lowerCamelCase)
           for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                   for threshold in [0.5, 1.0, 2.0]:
                       for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               thresholding=lowerCamelCase,   prediction_type=lowerCamelCase,   sample_max_value=lowerCamelCase,   algorithm_type='dpmsolver++',   solver_order=lowerCamelCase,   solver_type=lowerCamelCase,   )
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           for algorithm_type in ["dpmsolver", "dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                   for order in [1, 2, 3]:
                       for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               solver_order=lowerCamelCase,   solver_type=lowerCamelCase,   prediction_type=lowerCamelCase,   algorithm_type=lowerCamelCase,   )
                           _lowercase :							Optional[Any]   	=	self.full_loop(
                               solver_order=lowerCamelCase,   solver_type=lowerCamelCase,   prediction_type=lowerCamelCase,   algorithm_type=lowerCamelCase,   )
                           assert not torch.isnan(lowerCamelCase).any(), "Samples have nan numbers"
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           self.check_over_configs(lower_order_final=lowerCamelCase)
           self.check_over_configs(lower_order_final=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           self.check_over_configs(lambda_min_clipped=-float('inf'))
           self.check_over_configs(lambda_min_clipped=-5.1)
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           self.check_over_configs(variance_type=lowerCamelCase)
           self.check_over_configs(variance_type='learned_range')
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
               self.check_over_forward(num_inference_steps=lowerCamelCase,   time_step=0)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.full_loop()
           _lowercase :							Union[str, Any]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(use_karras_sigmas=lowerCamelCase)
           _lowercase :							List[str]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_2_4_8) < 1E-3
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(prediction_type='v_prediction')
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.1_4_5_3) < 1E-3
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(prediction_type='v_prediction',   use_karras_sigmas=lowerCamelCase)
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.0_6_4_9) < 1E-3




       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							List[Any]   	=	self.scheduler_classes[0]
           _lowercase :							Optional[int]   	=	self.get_scheduler_config(thresholding=lowerCamelCase,   dynamic_thresholding_ratio=0)
           _lowercase :							Any   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							str   	=	10
           _lowercase :							List[str]   	=	self.dummy_model()
           _lowercase :							Tuple   	=	self.dummy_sample_deter.half()
           scheduler.set_timesteps(lowerCamelCase)

           for i, t in enumerate(scheduler.timesteps):
               _lowercase :							Tuple   	=	model(lowerCamelCase,   lowerCamelCase)
               _lowercase :							Dict   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           assert sample.dtype == torch.floataa


