
"""simple docstring"""


from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
      import tensorflow as tf

      from transformers.models.layoutlm.modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMModel,
      )



class   __a  :






       def __init__(  self :							Dict       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							Union[str, Any]=13       ,      UpperCAmelCase_ :							Optional[Any]=7       ,      UpperCAmelCase_ :							Optional[int]=True       ,      UpperCAmelCase_ :							Tuple=True       ,      UpperCAmelCase_ :							Optional[Any]=True       ,      UpperCAmelCase_ :							int=True       ,      UpperCAmelCase_ :							Optional[Any]=99       ,      UpperCAmelCase_ :							Tuple=32       ,      UpperCAmelCase_ :							List[str]=2       ,      UpperCAmelCase_ :							List[str]=4       ,      UpperCAmelCase_ :							Dict=37       ,      UpperCAmelCase_ :							List[str]="gelu"       ,      UpperCAmelCase_ :							List[Any]=0.1       ,      UpperCAmelCase_ :							List[Any]=0.1       ,      UpperCAmelCase_ :							Optional[int]=512       ,      UpperCAmelCase_ :							List[Any]=16       ,      UpperCAmelCase_ :							Tuple=2       ,      UpperCAmelCase_ :							List[str]=0.02       ,      UpperCAmelCase_ :							int=3       ,      UpperCAmelCase_ :							str=4       ,      UpperCAmelCase_ :							str=None       ,      UpperCAmelCase_ :							Optional[int]=1_000       ,      )->  List[Any]:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size			)

              # convert bbox to numpy since TF does not support item assignment
              UpperCamelCase					 =		ids_tensor([self.batch_size, self.seq_length, 4]       ,      self.range_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]:
                                   UpperCamelCase					 =		bbox[i, j, 3]
                                   UpperCamelCase					 =		bbox[i, j, 1]
                                   UpperCamelCase					 =		t
                            if bbox[i, j, 2] < bbox[i, j, 0]:
                                   UpperCamelCase					 =		bbox[i, j, 2]
                                   UpperCamelCase					 =		bbox[i, j, 0]
                                   UpperCamelCase					 =		t
              UpperCamelCase					 =		tf.convert_to_tensor(UpperCAmelCase_			)

              UpperCamelCase					 =		None
              if self.use_input_mask:
                     UpperCamelCase					 =		random_attention_mask([self.batch_size, self.seq_length]			)

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

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

              UpperCamelCase					 =		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       ,      )

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






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							Union[str, Any]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							Optional[Any]       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							List[Any]       ,      UpperCAmelCase_ :							Tuple       ,      UpperCAmelCase_ :							List[Any]			)->  Union[str, Any]:







              """simple docstring"""
              UpperCamelCase					 =		TFLayoutLMModel(config=UpperCAmelCase_			)

              UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_       ,      attention_mask=UpperCAmelCase_       ,      token_type_ids=UpperCAmelCase_			)
              UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_       ,      token_type_ids=UpperCAmelCase_			)
              UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			)
              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 :							List[str]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							Tuple       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      UpperCAmelCase_ :							Union[str, Any]       ,      UpperCAmelCase_ :							str       ,      UpperCAmelCase_ :							Union[str, Any]			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		TFLayoutLMForMaskedLM(config=UpperCAmelCase_			)

              UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_       ,      attention_mask=UpperCAmelCase_       ,      token_type_ids=UpperCAmelCase_       ,      labels=UpperCAmelCase_			)
              self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.vocab_size)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							Dict       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							Tuple       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							Dict			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		self.num_labels
              UpperCamelCase					 =		TFLayoutLMForSequenceClassification(config=UpperCAmelCase_			)

              UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_       ,      attention_mask=UpperCAmelCase_       ,      token_type_ids=UpperCAmelCase_			)
              self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							Optional[Any]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							Union[str, Any]       ,      UpperCAmelCase_ :							Optional[Any]       ,      UpperCAmelCase_ :							Dict			)->  Union[str, Any]:







              """simple docstring"""
              UpperCamelCase					 =		self.num_labels
              UpperCamelCase					 =		TFLayoutLMForTokenClassification(config=UpperCAmelCase_			)

              UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_       ,      attention_mask=UpperCAmelCase_       ,      token_type_ids=UpperCAmelCase_       ,      labels=UpperCAmelCase_			)
              self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.num_labels)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							Dict       ,      UpperCAmelCase_ :							Dict       ,      UpperCAmelCase_ :							Dict       ,      UpperCAmelCase_ :							int			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		TFLayoutLMForQuestionAnswering(config=UpperCAmelCase_			)

              UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_       ,      attention_mask=UpperCAmelCase_       ,      token_type_ids=UpperCAmelCase_			)
              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 :							Optional[Any]			)->  List[Any]:







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





@require_tf
class   __a  (      _lowerCAmelCase    ,     _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					Union[str, Any]   =			(
           (
               TFLayoutLMModel,
               TFLayoutLMForMaskedLM,
               TFLayoutLMForTokenClassification,
               TFLayoutLMForSequenceClassification,
               TFLayoutLMForQuestionAnswering,
           )
           if is_tf_available()
           else ()
       )
       UpperCamelCase_		:					str   =			(
           {
               '''feature-extraction''': TFLayoutLMModel,
               '''fill-mask''': TFLayoutLMForMaskedLM,
               '''text-classification''': TFLayoutLMForSequenceClassification,
               '''token-classification''': TFLayoutLMForTokenClassification,
               '''zero-shot''': TFLayoutLMForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       UpperCamelCase_		:					Union[str, Any]   =			False
       UpperCamelCase_		:					List[Any]   =			True
       UpperCamelCase_		:					str   =			10






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		TFLayoutLMModelTester(self			)
              UpperCamelCase					 =		ConfigTester(self       ,      config_class=UpperCAmelCase_       ,      hidden_size=37			)






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







              """simple docstring"""
              self.config_tester.run_common_tests()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Any:







              """simple docstring"""
              UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_			)






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







              """simple docstring"""
              UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  List[Any]:







              """simple docstring"""
              for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCamelCase					 =		TFLayoutLMModel.from_pretrained(UpperCAmelCase_			)
                     self.assertIsNotNone(UpperCAmelCase_			)






       @unittest.skip("Onnx compliancy broke with TF 2.10"			)
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  str:







              """simple docstring"""
              pass





def 	lowerCamelCase__			(							)->							Tuple:







       """simple docstring"""
       # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
       # fmt: off
       UpperCamelCase					 =		tf.convert_to_tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]				)  # noqa: E231
       UpperCamelCase					 =		tf.convert_to_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],]				)  # noqa: E231
       UpperCamelCase					 =		tf.convert_to_tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]				)  # noqa: E231
       UpperCamelCase					 =		tf.convert_to_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]]				)  # noqa: E231
       # these are sequence labels (i.e. at the token level)
       UpperCamelCase					 =		tf.convert_to_tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]				)  # noqa: E231
       # fmt: on

       return input_ids, attention_mask, bbox, token_type_ids, labels



@require_tf
class   __a  (      unittest.TestCase	):






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"			)

              UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase					 =		prepare_layoutlm_batch_inputs()

              # forward pass
              UpperCamelCase					 =		model(input_ids=UpperCAmelCase_       ,      bbox=UpperCAmelCase_       ,      attention_mask=UpperCAmelCase_       ,      token_type_ids=UpperCAmelCase_			)

              # test the sequence output on [0, :3, :3]
              UpperCamelCase					 =		tf.convert_to_tensor(
                  [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]       ,      )

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

              # test the pooled output on [1, :3]
              UpperCamelCase					 =		tf.convert_to_tensor([-0.6580, -0.0214, 0.8552]			)

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






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Any:







              """simple docstring"""
              # initialize model with randomly initialized sequence classification head
              UpperCamelCase					 =		TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"       ,      num_labels=2			)

              UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase					 =		prepare_layoutlm_batch_inputs()

              # forward pass
              UpperCamelCase					 =		model(
                  input_ids=UpperCAmelCase_       ,      bbox=UpperCAmelCase_       ,      attention_mask=UpperCAmelCase_       ,      token_type_ids=UpperCAmelCase_       ,      labels=tf.convert_to_tensor([1, 1]			)       ,      )

              # test whether we get a loss as a scalar
              UpperCamelCase					 =		outputs.loss
              UpperCamelCase					 =		(2,)
              self.assertEqual(loss.shape       ,      UpperCAmelCase_			)

              # test the shape of the logits
              UpperCamelCase					 =		outputs.logits
              UpperCamelCase					 =		(2, 2)
              self.assertEqual(logits.shape       ,      UpperCAmelCase_			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  Union[str, Any]:







              """simple docstring"""
              # initialize model with randomly initialized token classification head
              UpperCamelCase					 =		TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"       ,      num_labels=13			)

              UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase					 =		prepare_layoutlm_batch_inputs()

              # forward pass
              UpperCamelCase					 =		model(
                  input_ids=UpperCAmelCase_       ,      bbox=UpperCAmelCase_       ,      attention_mask=UpperCAmelCase_       ,      token_type_ids=UpperCAmelCase_       ,      labels=UpperCAmelCase_			)

              # test the shape of the logits
              UpperCamelCase					 =		outputs.logits
              UpperCamelCase					 =		tf.convert_to_tensor((2, 25, 13)			)
              self.assertEqual(logits.shape       ,      UpperCAmelCase_			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Optional[Any]:







              """simple docstring"""
              # initialize model with randomly initialized token classification head
              UpperCamelCase					 =		TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"			)

              UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase					 =		prepare_layoutlm_batch_inputs()

              # forward pass
              UpperCamelCase					 =		model(input_ids=UpperCAmelCase_       ,      bbox=UpperCAmelCase_       ,      attention_mask=UpperCAmelCase_       ,      token_type_ids=UpperCAmelCase_			)

              # test the shape of the logits
              UpperCamelCase					 =		tf.convert_to_tensor((2, 25)			)
              self.assertEqual(outputs.start_logits.shape       ,      UpperCAmelCase_			)
              self.assertEqual(outputs.end_logits.shape       ,      UpperCAmelCase_			)







"""simple docstring"""


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

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)



@dataclass
class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Union[List[PIL.Image.Image], np.ndarray]
       UpperCamelCase_		:					Optional[List[bool]]


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
      from .pipeline_cycle_diffusion import CycleDiffusionPipeline
      from .pipeline_stable_diffusion import StableDiffusionPipeline
      from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
      from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
      from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
      from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
      from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
      from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
      from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
      from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
      from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
      from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
      from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
      from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
      from .pipeline_stable_unclip import StableUnCLIPPipeline
      from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
      from .safety_checker import StableDiffusionSafetyChecker
      from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
      if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
      from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
      if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          StableDiffusionDepthaImgPipeline,
          StableDiffusionDiffEditPipeline,
          StableDiffusionPixaPixZeroPipeline,
      )
else:
      from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
      from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
      from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
      if not (
          is_torch_available()
          and is_transformers_available()
          and is_k_diffusion_available()
          and is_k_diffusion_version(""">=""", """0.0.12""")
      ):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
      from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
      if not (is_transformers_available() and is_onnx_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_onnx_objects import *  # noqa F403
else:
      from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
      from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
      from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
      from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
      from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
      import flax



      @flax.struct.dataclass
      class   __a  (      _lowerCAmelCase	):
             UpperCamelCase_		:					np.ndarray
             UpperCamelCase_		:					List[bool]





      from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
      from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
      from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
      from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
      from .safety_checker_flax import FlaxStableDiffusionSafetyChecker





