"""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      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=13 ,				_SCREAMING_SNAKE_CASE=7 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=99 ,				_SCREAMING_SNAKE_CASE=32 ,				_SCREAMING_SNAKE_CASE=5 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=37 ,				_SCREAMING_SNAKE_CASE="gelu" ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=512 ,				_SCREAMING_SNAKE_CASE=16 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=1000 ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					batch_size
      __lowerCamelCase    		=					seq_length
      __lowerCamelCase    		=					is_training
      __lowerCamelCase    		=					use_input_mask
      __lowerCamelCase    		=					use_token_type_ids
      __lowerCamelCase    		=					use_labels
      __lowerCamelCase    		=					vocab_size
      __lowerCamelCase    		=					hidden_size
      __lowerCamelCase    		=					num_hidden_layers
      __lowerCamelCase    		=					num_attention_heads
      __lowerCamelCase    		=					intermediate_size
      __lowerCamelCase    		=					hidden_act
      __lowerCamelCase    		=					hidden_dropout_prob
      __lowerCamelCase    		=					attention_probs_dropout_prob
      __lowerCamelCase    		=					max_position_embeddings
      __lowerCamelCase    		=					type_vocab_size
      __lowerCamelCase    		=					type_sequence_label_size
      __lowerCamelCase    		=					initializer_range
      __lowerCamelCase    		=					num_labels
      __lowerCamelCase    		=					num_choices
      __lowerCamelCase    		=					scope
      __lowerCamelCase    		=					range_bbox







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size	)

      __lowerCamelCase    		=					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    		=					bbox[i, j, 3]
               __lowerCamelCase    		=					bbox[i, j, 1]
               __lowerCamelCase    		=					t
            if bbox[i, j, 2] < bbox[i, j, 0]:
               __lowerCamelCase    		=					bbox[i, j, 2]
               __lowerCamelCase    		=					bbox[i, j, 0]
               __lowerCamelCase    		=					t

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

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

      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      if self.use_labels:
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.num_choices	)

      __lowerCamelCase    		=					self.get_config()

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







   def    _A				(	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    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMModel(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_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    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForMaskedLM(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					LayoutLMForSequenceClassification(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(
          _SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					LayoutLMForTokenClassification(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForQuestionAnswering(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(
          _SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				start_positions=_SCREAMING_SNAKE_CASE ,				end_positions=_SCREAMING_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    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.prepare_config_and_inputs()
      (
          (
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,
      )    		=					config_and_inputs
      __lowerCamelCase    		=					{
          'input_ids': input_ids,
          'bbox': bbox,
          'token_type_ids': token_type_ids,
          'attention_mask': input_mask,
      }
      return config, inputs_dict






@require_torch
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							List[str]    =    (
       (
           LayoutLMModel,
           LayoutLMForMaskedLM,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMForQuestionAnswering,
       )
       if is_torch_available()
       else None
   )
   __UpperCAmelCase	:							List[Any]    =    (
       {
           "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	:							int    =    True







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMModelTester(self	)
      __lowerCamelCase    		=					ConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE ,				hidden_size=37	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         __lowerCamelCase    		=					type
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE	)







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


      '''simple docstring'''

      pass



def       A__    (	)  ->							Optional[int]:
   # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
   # fmt: off
   __lowerCamelCase    		=					torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]      , device=lowercase_						)  # noqa: E231
   __lowerCamelCase    		=					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    		=					torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]      , device=lowercase_						)  # noqa: E231
   __lowerCamelCase    		=					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    		=					torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]      , device=lowercase_						)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)

      # test the sequence output on [0, :3, :3]
      __lowerCamelCase    		=					torch.tensor(
          [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]] ,				device=_SCREAMING_SNAKE_CASE ,				)

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

      # test the pooled output on [1, :3]
      __lowerCamelCase    		=					torch.tensor([-0.6580, -0.0214, 0.8552] ,				device=_SCREAMING_SNAKE_CASE	)

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







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased' ,				num_labels=2	).to(
          _SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(
          input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=torch.tensor([1, 1] ,				device=_SCREAMING_SNAKE_CASE	) ,				)

      # test whether we get a loss as a scalar
      __lowerCamelCase    		=					outputs.loss
      __lowerCamelCase    		=					torch.Size([]	)
      self.assertEqual(loss.shape ,				_SCREAMING_SNAKE_CASE	)

      # test the shape of the logits
      __lowerCamelCase    		=					outputs.logits
      __lowerCamelCase    		=					torch.Size((2, 2)	)
      self.assertEqual(logits.shape ,				_SCREAMING_SNAKE_CASE	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased' ,				num_labels=13	).to(
          _SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(
          input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_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    		=					outputs.logits
      __lowerCamelCase    		=					torch.Size((2, 25, 13)	)
      self.assertEqual(logits.shape ,				_SCREAMING_SNAKE_CASE	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)

      # test the shape of the logits
      __lowerCamelCase    		=					torch.Size((2, 25)	)
      self.assertEqual(outputs.start_logits.shape ,				_SCREAMING_SNAKE_CASE	)
      self.assertEqual(outputs.end_logits.shape ,				_SCREAMING_SNAKE_CASE	)









"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


__lowerCAmelCase				      =   logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
class 	_lowerCAmelCase    (      __snake_case       ):
							__lowerCAmelCase					:    torch.FloatTensor




class 	_lowerCAmelCase    (      __snake_case       ,       __snake_case       ):
							__lowerCAmelCase					:    int     =  1

							@register_to_config
							def __init__( self       :    Union[str, Any] ,		a       :    int = 40 ,		a       :    float = 0.002 ,		a       :    float = 80.0 ,		a       :    float = 0.5 ,		a       :    float = 1.0 ,		a       :    float = 7.0 ,		a       :    bool = True ,		)   ->    List[Any]:

									"""simple docstring"""
									# standard deviation of the initial noise distribution
									lowercase										=							sigma_max

									lowercase										=							np.linspace(0 ,		1 ,		a    )
									lowercase										=							self._convert_to_karras(a    )
									lowercase										=							self.sigma_to_t(a    )

									# setable values
									lowercase										=							None
									lowercase										=							torch.from_numpy(a    )
									lowercase										=							torch.from_numpy(a    )
									lowercase										=							False
									lowercase										=							False

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    Optional[int] ,		a       :    str=None    )   ->    Any:

									"""simple docstring"""
									if schedule_timesteps is None:
											lowercase										=							self.timesteps

									lowercase										=							(schedule_timesteps == timestep).nonzero()
									return indices.item()

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    torch.FloatTensor ,		a       :    Union[float, torch.FloatTensor]    )   ->    torch.FloatTensor:

									"""simple docstring"""
									# Get sigma corresponding to timestep
									if isinstance(a ,		torch.Tensor    ):
											lowercase										=							timestep.to(self.timesteps.device    )
									lowercase										=							self.index_for_timestep(a    )
									lowercase										=							self.sigmas[step_idx]

									lowercase										=							sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

									lowercase										=							True
									return sample

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Union[float, np.ndarray]    )   ->    str:

									"""simple docstring"""
									if not isinstance(a ,		np.ndarray    ):
											lowercase										=							np.array(a ,		dtype=np.floataa    )

									lowercase										=							1000 * 0.25 * np.log(sigmas + 1E-44    )

									return timesteps

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Optional[int] = None ,		a       :    Union[str, torch.device] = None ,		a       :    Optional[List[int]] = None ,		)   ->    Dict:

									"""simple docstring"""
									if num_inference_steps is None and timesteps is None:
											raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''    )

									if num_inference_steps is not None and timesteps is not None:
											raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''    )

									# Follow DDPMScheduler custom timesteps logic
									if timesteps is not None:
											for i in range(1 ,		len(a    )    ):
													if timesteps[i] >= timesteps[i - 1]:
															raise ValueError('''`timesteps` must be in descending order.'''    )

											if timesteps[0] >= self.config.num_train_timesteps:
													raise ValueError(
													    f"""`timesteps` must start before `self.config.train_timesteps`:"""
													    f""" {self.config.num_train_timesteps}."""    )

											lowercase										=							np.array(a ,		dtype=np.intaa    )
											lowercase										=							True
									else:
											if num_inference_steps > self.config.num_train_timesteps:
													raise ValueError(
													    f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
													    f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
													    f""" maximal {self.config.num_train_timesteps} timesteps."""    )

											lowercase										=							num_inference_steps

											lowercase										=							self.config.num_train_timesteps // self.num_inference_steps
											lowercase										=							(np.arange(0 ,		a    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
											lowercase										=							False

									# Map timesteps to Karras sigmas directly for multistep sampling
									# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
									lowercase										=							self.config.num_train_timesteps
									lowercase										=							timesteps[::-1].copy()
									lowercase										=							ramp / (num_train_timesteps - 1)
									lowercase										=							self._convert_to_karras(a    )
									lowercase										=							self.sigma_to_t(a    )

									lowercase										=							np.concatenate([sigmas, [self.sigma_min]]    ).astype(np.floataa    )
									lowercase										=							torch.from_numpy(a    ).to(device=a    )

									if str(a    ).startswith('''mps'''    ):
											# mps does not support float64
											lowercase										=							torch.from_numpy(a    ).to(a ,		dtype=torch.floataa    )
									else:
											lowercase										=							torch.from_numpy(a    ).to(device=a    )

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    int    )   ->    List[Any]:

									"""simple docstring"""
									lowercase										=							self.config.sigma_min
									lowercase										=							self.config.sigma_max

									lowercase										=							self.config.rho
									lowercase										=							sigma_min ** (1 / rho)
									lowercase										=							sigma_max ** (1 / rho)
									lowercase										=							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas

							def 					_lowerCAmelCase  ( self       :    Any ,		a       :    str    )   ->    int:

									"""simple docstring"""
									lowercase										=							self.config.sigma_data

									lowercase										=							sigma_data**2 / (sigma**2 + sigma_data**2)
									lowercase										=							sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Any    )   ->    Optional[int]:

									"""simple docstring"""
									lowercase										=							self.config.sigma_min
									lowercase										=							self.config.sigma_data

									lowercase										=							sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
									lowercase										=							(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out

							def 					_lowerCAmelCase  ( self       :    Dict ,		a       :    torch.FloatTensor ,		a       :    Union[float, torch.FloatTensor] ,		a       :    torch.FloatTensor ,		a       :    Optional[torch.Generator] = None ,		a       :    bool = True ,		)   ->    Union[CMStochasticIterativeSchedulerOutput, Tuple]:

									"""simple docstring"""

									if (
									    isinstance(a ,		a    )
									    or isinstance(a ,		torch.IntTensor    )
									    or isinstance(a ,		torch.LongTensor    )
									):
											raise ValueError(
											    (
											        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
											        f""" `{self.__class__}.step()` is not supported. Make sure to pass"""
											        ''' one of the `scheduler.timesteps` as a timestep.'''
											    ) ,		)

									if not self.is_scale_input_called:
											logger.warning(
											    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
											    '''See `StableDiffusionPipeline` for a usage example.'''    )

									if isinstance(a ,		torch.Tensor    ):
											lowercase										=							timestep.to(self.timesteps.device    )

									lowercase										=							self.config.sigma_min
									lowercase										=							self.config.sigma_max

									lowercase										=							self.index_for_timestep(a    )

									# sigma_next corresponds to next_t in original implementation
									lowercase										=							self.sigmas[step_index]
									if step_index + 1 < self.config.num_train_timesteps:
											lowercase										=							self.sigmas[step_index + 1]
									else:
											# Set sigma_next to sigma_min
											lowercase										=							self.sigmas[-1]

									# Get scalings for boundary conditions
									lowercase					,  lowercase										=							self.get_scalings_for_boundary_condition(a    )

									# 1. Denoise model output using boundary conditions
									lowercase										=							c_out * model_output + c_skip * sample
									if self.config.clip_denoised:
											lowercase										=							denoised.clamp(-1 ,		1    )

									# 2. Sample z ~ N(0, s_noise^2 * I)
									# Noise is not used for onestep sampling.
									if len(self.timesteps    ) > 1:
											lowercase										=							randn_tensor(
											    model_output.shape ,		dtype=model_output.dtype ,		device=model_output.device ,		generator=a    )
									else:
											lowercase										=							torch.zeros_like(a    )
									lowercase										=							noise * self.config.s_noise

									lowercase										=							sigma_next.clamp(min=a ,		max=a    )

									# 3. Return noisy sample
									# tau = sigma_hat, eps = sigma_min
									lowercase										=							denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

									if not return_dict:
											return (prev_sample,)

									return CMStochasticIterativeSchedulerOutput(prev_sample=a    )

							def 					_lowerCAmelCase  ( self       :    Any ,		a       :    torch.FloatTensor ,		a       :    torch.FloatTensor ,		a       :    torch.FloatTensor ,		)   ->    torch.FloatTensor:

									"""simple docstring"""
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									lowercase										=							self.sigmas.to(device=original_samples.device ,		dtype=original_samples.dtype    )
									if original_samples.device.type == "mps" and torch.is_floating_point(a    ):
											# mps does not support float64
											lowercase										=							self.timesteps.to(original_samples.device ,		dtype=torch.floataa    )
											lowercase										=							timesteps.to(original_samples.device ,		dtype=torch.floataa    )
									else:
											lowercase										=							self.timesteps.to(original_samples.device    )
											lowercase										=							timesteps.to(original_samples.device    )

									lowercase										=							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

									lowercase										=							sigmas[step_indices].flatten()
									while len(sigma.shape    ) < len(original_samples.shape    ):
											lowercase										=							sigma.unsqueeze(-1    )

									lowercase										=							original_samples + noise * sigma
									return noisy_samples

							def __len__( self       :    Union[str, Any]    )   ->    Any:

									"""simple docstring"""
									return self.config.num_train_timesteps