

'''simple docstring'''


import unittest

from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
						    MODEL_FOR_PRETRAINING_MAPPING,
						    FunnelBaseModel,
						    FunnelForMaskedLM,
						    FunnelForMultipleChoice,
						    FunnelForPreTraining,
						    FunnelForQuestionAnswering,
						    FunnelForSequenceClassification,
						    FunnelForTokenClassification,
						    FunnelModel,
						)



class        lowercase    :


							def __init__(      self     ,      _snake_case     ,      _snake_case=13     ,      _snake_case=7     ,      _snake_case=True     ,      _snake_case=True     ,      _snake_case=True     ,      _snake_case=True     ,      _snake_case=99     ,      _snake_case=[1, 1, 2]     ,      _snake_case=1     ,      _snake_case=32     ,      _snake_case=4     ,      _snake_case=8     ,      _snake_case=37     ,      _snake_case="gelu_new"     ,      _snake_case=0.1     ,      _snake_case=0.1     ,      _snake_case=0.0     ,      _snake_case=512     ,      _snake_case=3     ,      _snake_case=0.02     ,      _snake_case=3     ,      _snake_case=4     ,      _snake_case=None     ,      _snake_case=False     ,      )				->					List[str]:
								UpperCAmelCase_		:   Tuple													=       parent
								UpperCAmelCase_		:   Optional[Any]													=       batch_size
								UpperCAmelCase_		:   Optional[int]													=       seq_length
								UpperCAmelCase_		:   List[str]													=       is_training
								UpperCAmelCase_		:   Dict													=       use_input_mask
								UpperCAmelCase_		:   Union[str, Any]													=       use_token_type_ids
								UpperCAmelCase_		:   Optional[Any]													=       use_labels
								UpperCAmelCase_		:   Any													=       vocab_size
								UpperCAmelCase_		:   Any													=       block_sizes
								UpperCAmelCase_		:   Any													=       num_decoder_layers
								UpperCAmelCase_		:   Dict													=       d_model
								UpperCAmelCase_		:   str													=       n_head
								UpperCAmelCase_		:   Optional[Any]													=       d_head
								UpperCAmelCase_		:   int													=       d_inner
								UpperCAmelCase_		:   Dict													=       hidden_act
								UpperCAmelCase_		:   Any													=       hidden_dropout
								UpperCAmelCase_		:   Any													=       attention_dropout
								UpperCAmelCase_		:   Union[str, Any]													=       activation_dropout
								UpperCAmelCase_		:   List[str]													=       max_position_embeddings
								UpperCAmelCase_		:   Any													=       type_vocab_size
								UpperCAmelCase_		:   List[str]													=       2
								UpperCAmelCase_		:   Dict													=       num_labels
								UpperCAmelCase_		:   List[Any]													=       num_choices
								UpperCAmelCase_		:   Optional[Any]													=       scope
								UpperCAmelCase_		:   List[Any]													=       initializer_std

								# Used in the tests to check the size of the first attention layer
								UpperCAmelCase_		:   Tuple													=       n_head
								# Used in the tests to check the size of the first hidden state
								UpperCAmelCase_		:   str													=       self.d_model
								# Used in the tests to check the number of output hidden states/attentions
								UpperCAmelCase_		:   Union[str, Any]													=       sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
								# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
								# the last hidden state of the first block (which is the first hidden state of the decoder).
								if not base:
									UpperCAmelCase_		:   List[Any]													=       self.num_hidden_layers + 2


							def     _snake_case				(      self)				->					Union[str, Any]:
								UpperCAmelCase_		:   Optional[int]													=       ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size)

								UpperCAmelCase_		:   Tuple													=       None
								if self.use_input_mask:
									UpperCAmelCase_		:   List[str]													=       ids_tensor([self.batch_size, self.seq_length]     ,      vocab_size=2)

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

								UpperCAmelCase_		:   Dict													=       None
								UpperCAmelCase_		:   Union[str, Any]													=       None
								UpperCAmelCase_		:   Any													=       None
								if self.use_labels:
									UpperCAmelCase_		:   str													=       ids_tensor([self.batch_size]     ,      self.type_sequence_label_size)
									UpperCAmelCase_		:   Optional[Any]													=       ids_tensor([self.batch_size, self.seq_length]     ,      self.num_labels)
									UpperCAmelCase_		:   Tuple													=       ids_tensor([self.batch_size]     ,      self.num_choices)
									UpperCAmelCase_		:   Optional[Any]													=       ids_tensor([self.batch_size, self.seq_length]     ,      1)

								UpperCAmelCase_		:   List[Any]													=       self.get_config()

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_mask,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								    fake_token_labels,
								)


							def     _snake_case				(      self)				->					str:
								return FunnelConfig(
								    vocab_size=self.vocab_size     ,      block_sizes=self.block_sizes     ,      num_decoder_layers=self.num_decoder_layers     ,      d_model=self.d_model     ,      n_head=self.n_head     ,      d_head=self.d_head     ,      d_inner=self.d_inner     ,      hidden_act=self.hidden_act     ,      hidden_dropout=self.hidden_dropout     ,      attention_dropout=self.attention_dropout     ,      activation_dropout=self.activation_dropout     ,      max_position_embeddings=self.max_position_embeddings     ,      type_vocab_size=self.type_vocab_size     ,      initializer_std=self.initializer_std     ,      )


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					Union[str, Any]:
								UpperCAmelCase_		:   str													=       FunnelModel(config=lowerCamelCase_)
								model.to(lowerCamelCase_)
								model.eval()
								UpperCAmelCase_		:   Optional[int]													=       model(lowerCamelCase_     ,      attention_mask=lowerCamelCase_     ,      token_type_ids=lowerCamelCase_)
								UpperCAmelCase_		:   str													=       model(lowerCamelCase_     ,      token_type_ids=lowerCamelCase_)
								UpperCAmelCase_		:   str													=       model(lowerCamelCase_)
								self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.d_model))

								UpperCAmelCase_		:   Dict													=       False
								UpperCAmelCase_		:   Dict													=       model(lowerCamelCase_)
								self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.d_model))

								UpperCAmelCase_		:   str													=       False
								UpperCAmelCase_		:   List[Any]													=       model(lowerCamelCase_)
								self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.d_model))


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					Any:
								UpperCAmelCase_		:   Optional[int]													=       FunnelBaseModel(config=lowerCamelCase_)
								model.to(lowerCamelCase_)
								model.eval()
								UpperCAmelCase_		:   Optional[Any]													=       model(lowerCamelCase_     ,      attention_mask=lowerCamelCase_     ,      token_type_ids=lowerCamelCase_)
								UpperCAmelCase_		:   List[Any]													=       model(lowerCamelCase_     ,      token_type_ids=lowerCamelCase_)
								UpperCAmelCase_		:   Any													=       model(lowerCamelCase_)
								self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, 2, self.d_model))

								UpperCAmelCase_		:   int													=       False
								UpperCAmelCase_		:   Dict													=       model(lowerCamelCase_)
								self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, 3, self.d_model))

								UpperCAmelCase_		:   Optional[Any]													=       False
								UpperCAmelCase_		:   Optional[int]													=       model(lowerCamelCase_)
								self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, 2, self.d_model))


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					str:
								UpperCAmelCase_		:   str													=       self.num_labels
								UpperCAmelCase_		:   str													=       FunnelForPreTraining(config=lowerCamelCase_)
								model.to(lowerCamelCase_)
								model.eval()
								UpperCAmelCase_		:   List[Any]													=       model(lowerCamelCase_     ,      attention_mask=lowerCamelCase_     ,      token_type_ids=lowerCamelCase_     ,      labels=lowerCamelCase_)
								self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length))


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					Tuple:
								UpperCAmelCase_		:   Union[str, Any]													=       FunnelForMaskedLM(config=lowerCamelCase_)
								model.to(lowerCamelCase_)
								model.eval()
								UpperCAmelCase_		:   Any													=       model(lowerCamelCase_     ,      attention_mask=lowerCamelCase_     ,      token_type_ids=lowerCamelCase_     ,      labels=lowerCamelCase_)
								self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size))


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					List[str]:
								UpperCAmelCase_		:   List[str]													=       self.num_labels
								UpperCAmelCase_		:   Tuple													=       FunnelForSequenceClassification(lowerCamelCase_)
								model.to(lowerCamelCase_)
								model.eval()
								UpperCAmelCase_		:   Any													=       model(lowerCamelCase_     ,      attention_mask=lowerCamelCase_     ,      token_type_ids=lowerCamelCase_     ,      labels=lowerCamelCase_)
								self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels))


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					Dict:
								UpperCAmelCase_		:   Dict													=       self.num_choices
								UpperCAmelCase_		:   Union[str, Any]													=       FunnelForMultipleChoice(config=lowerCamelCase_)
								model.to(lowerCamelCase_)
								model.eval()
								UpperCAmelCase_		:   List[Any]													=       input_ids.unsqueeze(1).expand(-1     ,      self.num_choices     ,      -1).contiguous()
								UpperCAmelCase_		:   List[Any]													=       token_type_ids.unsqueeze(1).expand(-1     ,      self.num_choices     ,      -1).contiguous()
								UpperCAmelCase_		:   Tuple													=       input_mask.unsqueeze(1).expand(-1     ,      self.num_choices     ,      -1).contiguous()
								UpperCAmelCase_		:   List[Any]													=       model(
								    lowerCamelCase_     ,      attention_mask=lowerCamelCase_     ,      token_type_ids=lowerCamelCase_     ,      labels=lowerCamelCase_     ,      )
								self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices))


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					Dict:
								UpperCAmelCase_		:   List[Any]													=       self.num_labels
								UpperCAmelCase_		:   str													=       FunnelForTokenClassification(config=lowerCamelCase_)
								model.to(lowerCamelCase_)
								model.eval()
								UpperCAmelCase_		:   List[str]													=       model(lowerCamelCase_     ,      attention_mask=lowerCamelCase_     ,      token_type_ids=lowerCamelCase_     ,      labels=lowerCamelCase_)
								self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels))


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					int:
								UpperCAmelCase_		:   Tuple													=       FunnelForQuestionAnswering(config=lowerCamelCase_)
								model.to(lowerCamelCase_)
								model.eval()
								UpperCAmelCase_		:   Dict													=       model(
								    lowerCamelCase_     ,      attention_mask=lowerCamelCase_     ,      token_type_ids=lowerCamelCase_     ,      start_positions=lowerCamelCase_     ,      end_positions=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     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_		:   str													=       self.prepare_config_and_inputs()
								(
								    UpperCAmelCase_	
								)		:   Union[str, Any]													=       config_and_inputs
								UpperCAmelCase_		:   str													=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
								return config, inputs_dict



@require_torch
class        lowercase    (	snake_case_,       snake_case_,       unittest.TestCase			):
							_lowerCamelCase		:   str= False
							_lowerCamelCase		:   Tuple= False
							_lowerCamelCase		:   List[Any]= (
							    (
							        FunnelModel,
							        FunnelForMaskedLM,
							        FunnelForPreTraining,
							        FunnelForQuestionAnswering,
							        FunnelForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							_lowerCamelCase		:   int= (
							    {
							        'feature-extraction': (FunnelBaseModel, FunnelModel),
							        'fill-mask': FunnelForMaskedLM,
							        'question-answering': FunnelForQuestionAnswering,
							        'text-classification': FunnelForSequenceClassification,
							        'token-classification': FunnelForTokenClassification,
							        'zero-shot': FunnelForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case=False)				->					Optional[int]:
								UpperCAmelCase_		:   Tuple													=       super()._prepare_for_class(lowerCamelCase_     ,      lowerCamelCase_     ,      return_labels=lowerCamelCase_)

								if return_labels:
									if model_class in get_values(lowerCamelCase_):
										UpperCAmelCase_		:   Optional[Any]													=       torch.zeros(
										    (self.model_tester.batch_size, self.model_tester.seq_length)     ,      dtype=torch.long     ,      device=lowerCamelCase_)
								return inputs_dict


							def     _snake_case				(      self)				->					Union[str, Any]:
								UpperCAmelCase_		:   Union[str, Any]													=       FunnelModelTester(self)
								UpperCAmelCase_		:   List[str]													=       ConfigTester(self     ,      config_class=lowerCamelCase_)


							def     _snake_case				(      self)				->					Optional[int]:
								self.config_tester.run_common_tests()


							def     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_		:   int													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase_)


							def     _snake_case				(      self)				->					str:
								UpperCAmelCase_		:   List[str]													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*lowerCamelCase_)


							def     _snake_case				(      self)				->					Union[str, Any]:
								UpperCAmelCase_		:   List[str]													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_)


							def     _snake_case				(      self)				->					List[Any]:
								UpperCAmelCase_		:   Tuple													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_)


							def     _snake_case				(      self)				->					List[Any]:
								UpperCAmelCase_		:   int													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_)





							def     _snake_case				(      self     ,      _snake_case)				->					List[Any]:



								if hasattr(lowerCamelCase_     ,      'weight') and module.weight is not None:
									module.weight.data.fill_(3)
								if hasattr(lowerCamelCase_     ,      'bias') and module.bias is not None:
									module.bias.data.fill_(3)

								for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
									if hasattr(lowerCamelCase_     ,      lowerCamelCase_) and getattr(lowerCamelCase_     ,      lowerCamelCase_) is not None:
										UpperCAmelCase_		:   int													=       getattr(lowerCamelCase_     ,      lowerCamelCase_)
										weight.data.fill_(3)



@require_torch
class        lowercase    (	snake_case_,       unittest.TestCase			):
							_lowerCamelCase		:   Dict= False
							_lowerCamelCase		:   Tuple= False
							_lowerCamelCase		:   List[str]= (
							    (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
							)


							def     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_		:   List[Any]													=       FunnelModelTester(self     ,      base=lowerCamelCase_)
								UpperCAmelCase_		:   Dict													=       ConfigTester(self     ,      config_class=lowerCamelCase_)


							def     _snake_case				(      self)				->					Any:
								self.config_tester.run_common_tests()


							def     _snake_case				(      self)				->					int:
								UpperCAmelCase_		:   Optional[int]													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_base_model(*lowerCamelCase_)


							def     _snake_case				(      self)				->					List[str]:
								UpperCAmelCase_		:   int													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_)


							def     _snake_case				(      self)				->					Any:
								UpperCAmelCase_		:   Optional[int]													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase_)


							def     _snake_case				(      self)				->					Optional[int]:
								UpperCAmelCase_		:   Union[str, Any]													=       self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:   Union[str, Any]													=       True

								for model_class in self.all_model_classes:
									if model_class.__name__ == "FunnelBaseModel":
										continue
									UpperCAmelCase_		:   int													=       model_class(lowerCamelCase_)
									model.to(lowerCamelCase_)
									model.train()
									UpperCAmelCase_		:   Optional[Any]													=       self._prepare_for_class(lowerCamelCase_     ,      lowerCamelCase_     ,      return_labels=lowerCamelCase_)
									UpperCAmelCase_		:   Optional[int]													=       model(**lowerCamelCase_).loss
									loss.backward()





							def     _snake_case				(      self     ,      _snake_case)				->					Optional[int]:



								if hasattr(lowerCamelCase_     ,      'weight') and module.weight is not None:
									module.weight.data.fill_(3)
								if hasattr(lowerCamelCase_     ,      'bias') and module.bias is not None:
									module.bias.data.fill_(3)

								for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
									if hasattr(lowerCamelCase_     ,      lowerCamelCase_) and getattr(lowerCamelCase_     ,      lowerCamelCase_) is not None:
										UpperCAmelCase_		:   int													=       getattr(lowerCamelCase_     ,      lowerCamelCase_)
										weight.data.fill_(3)



@require_torch
@require_sentencepiece
@require_tokenizers
class        lowercase    (	unittest.TestCase			):


							def     _snake_case				(      self)				->					int:
								UpperCAmelCase_		:   Any													=       13
								UpperCAmelCase_		:   Union[str, Any]													=       7
								UpperCAmelCase_		:   Optional[Any]													=       torch.arange(0     ,      batch_size * sequence_length).long().reshape(lowerCamelCase_     ,      lowerCamelCase_)
								UpperCAmelCase_		:   Optional[int]													=       [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
								UpperCAmelCase_		:   Tuple													=       torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths])

								UpperCAmelCase_		:   int													=       FunnelModel.from_pretrained('sgugger/funnel-random-tiny')
								UpperCAmelCase_		:   Optional[int]													=       model(lowerCamelCase_     ,      token_type_ids=lowerCamelCase_)[0].abs()

								UpperCAmelCase_		:   Tuple													=       torch.tensor(2344.8352)
								UpperCAmelCase_		:   Any													=       torch.tensor(0.8_052)
								self.assertTrue(torch.allclose(output.sum()     ,      lowerCamelCase_     ,      atol=1e-4))
								self.assertTrue(torch.allclose(output.mean()     ,      lowerCamelCase_     ,      atol=1e-4))

								UpperCAmelCase_		:   List[Any]													=       torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]])
								UpperCAmelCase_		:   List[Any]													=       model(lowerCamelCase_     ,      attention_mask=lowerCamelCase_     ,      token_type_ids=lowerCamelCase_)[0].abs()

								UpperCAmelCase_		:   Any													=       torch.tensor(2343.8425)
								UpperCAmelCase_		:   Optional[Any]													=       torch.tensor(0.8_049)
								self.assertTrue(torch.allclose(output.sum()     ,      lowerCamelCase_     ,      atol=1e-4))
								self.assertTrue(torch.allclose(output.mean()     ,      lowerCamelCase_     ,      atol=1e-4))





							@slow
							def     _snake_case				(      self)				->					Optional[int]:
								UpperCAmelCase_		:   Tuple													=       FunnelTokenizer.from_pretrained('huggingface/funnel-small')
								UpperCAmelCase_		:   int													=       FunnelModel.from_pretrained('huggingface/funnel-small')
								UpperCAmelCase_		:   List[str]													=       tokenizer('Hello! I am the Funnel Transformer model.'     ,      return_tensors='pt')
								UpperCAmelCase_		:   Tuple													=       model(**lowerCamelCase_)[0]

								UpperCAmelCase_		:   List[Any]													=       torch.tensor(235.7_246)
								UpperCAmelCase_		:   Union[str, Any]													=       torch.tensor(0.0_256)
								self.assertTrue(torch.allclose(output.sum()     ,      lowerCamelCase_     ,      atol=1e-4))
								self.assertTrue(torch.allclose(output.mean()     ,      lowerCamelCase_     ,      atol=1e-4))














"""simple docstring"""
import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_=1.0   ):

       '''simple docstring'''



       super().__init__()

       lowerCamelCase__	:     Union[str, Any]								=		cut_size
       lowerCamelCase__	:     Optional[int]								=		cut_power





      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__							,	lowerCamelCase__	:     Union[str, Any]								=		pixel_values.shape[2:4]
       lowerCamelCase__	:     str								=		min(lowerCamelCase_,	lowerCamelCase_   )
       lowerCamelCase__	:     List[Any]								=		min(lowerCamelCase_,	lowerCamelCase_,	self.cut_size   )
       lowerCamelCase__	:     int								=		[]
       for _ in range(lowerCamelCase_   ):
        lowerCamelCase__	:     int								=		int(torch.rand([]   ) ** self.cut_power * (max_size - min_size) + min_size   )
        lowerCamelCase__	:     List[str]								=		torch.randint(0,	sideX - size + 1,	()   )
        lowerCamelCase__	:     Union[str, Any]								=		torch.randint(0,	sideY - size + 1,	()   )
        lowerCamelCase__	:     Tuple								=		pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
        cutouts.append(F.adaptive_avg_poolad(lowerCamelCase_,	self.cut_size   )   )
       return torch.cat(lowerCamelCase_   )






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 lowerCamelCase__	:     List[str]								=		F.normalize(_lowerCamelCase     ,  dim=-1							)
 lowerCamelCase__	:     Optional[int]								=		F.normalize(_lowerCamelCase     ,  dim=-1							)
 return (x - y).norm(dim=-1							).div(2							).arcsin().pow(2							).mul(2							)






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 for param in model.parameters():
  lowerCamelCase__	:     Dict								=		value





class 						a_ (      snake_case_					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	):

       '''simple docstring'''



       super().__init__()
       self.register_modules(
           vae=lowerCamelCase_,	text_encoder=lowerCamelCase_,	clip_model=lowerCamelCase_,	tokenizer=lowerCamelCase_,	unet=lowerCamelCase_,	scheduler=lowerCamelCase_,	feature_extractor=lowerCamelCase_,	)

       lowerCamelCase__	:     Union[str, Any]								=		transforms.Normalize(mean=feature_extractor.image_mean,	std=feature_extractor.image_std   )
       lowerCamelCase__	:     Dict								=		(
           feature_extractor.size
           if isinstance(feature_extractor.size,	lowerCamelCase_   )
           else feature_extractor.size['shortest_edge']
       )
       lowerCamelCase__	:     Any								=		MakeCutouts(self.cut_out_size   )

       set_requires_grad(self.text_encoder,	lowerCamelCase_   )
       set_requires_grad(self.clip_model,	lowerCamelCase_   )




      def      a__				(self,	lowerCamelCase_ = "auto"   ):

       '''simple docstring'''



       if slice_size == "auto":
        # half the attention head size is usually a good trade-off between
        # speed and memory
        lowerCamelCase__	:     Tuple								=		self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       self.enable_attention_slicing(lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       set_requires_grad(self.vae,	lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       set_requires_grad(self.vae,	lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       set_requires_grad(self.unet,	lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       set_requires_grad(self.unet,	lowerCamelCase_   )




      @torch.enable_grad()
      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_=True,	):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		latents.detach().requires_grad_()

       lowerCamelCase__	:     int								=		self.scheduler.scale_model_input(lowerCamelCase_,	lowerCamelCase_   )

       # predict the noise residual
       lowerCamelCase__	:     Tuple								=		self.unet(lowerCamelCase_,	lowerCamelCase_,	encoder_hidden_states=lowerCamelCase_   ).sample

       if isinstance(self.scheduler,	(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
        lowerCamelCase__	:     int								=		self.scheduler.alphas_cumprod[timestep]
        lowerCamelCase__	:     Union[str, Any]								=		1 - alpha_prod_t
        # compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        lowerCamelCase__	:     int								=		(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

        lowerCamelCase__	:     Optional[int]								=		torch.sqrt(lowerCamelCase_   )
        lowerCamelCase__	:     Optional[Any]								=		pred_original_sample * (fac) + latents * (1 - fac)
       elif isinstance(self.scheduler,	lowerCamelCase_   ):
        lowerCamelCase__	:     Optional[Any]								=		self.scheduler.sigmas[index]
        lowerCamelCase__	:     List[Any]								=		latents - sigma * noise_pred
       else:
        raise ValueError(f'''scheduler type {type(self.scheduler   )} not supported'''   )

       lowerCamelCase__	:     Tuple								=		1 / self.vae.config.scaling_factor * sample
       lowerCamelCase__	:     List[str]								=		self.vae.decode(lowerCamelCase_   ).sample
       lowerCamelCase__	:     Union[str, Any]								=		(image / 2 + 0.5).clamp(0,	1   )

       if use_cutouts:
        lowerCamelCase__	:     int								=		self.make_cutouts(lowerCamelCase_,	lowerCamelCase_   )
       else:
        lowerCamelCase__	:     Dict								=		transforms.Resize(self.cut_out_size   )(lowerCamelCase_   )
       lowerCamelCase__	:     Union[str, Any]								=		self.normalize(lowerCamelCase_   ).to(latents.dtype   )

       lowerCamelCase__	:     str								=		self.clip_model.get_image_features(lowerCamelCase_   )
       lowerCamelCase__	:     List[Any]								=		image_embeddings_clip / image_embeddings_clip.norm(p=2,	dim=-1,	keepdim=lowerCamelCase_   )

       if use_cutouts:
        lowerCamelCase__	:     List[Any]								=		spherical_dist_loss(lowerCamelCase_,	lowerCamelCase_   )
        lowerCamelCase__	:     Dict								=		dists.view([num_cutouts, sample.shape[0], -1]   )
        lowerCamelCase__	:     List[Any]								=		dists.sum(2   ).mean(0   ).sum() * clip_guidance_scale
       else:
        lowerCamelCase__	:     str								=		spherical_dist_loss(lowerCamelCase_,	lowerCamelCase_   ).mean() * clip_guidance_scale

       lowerCamelCase__	:     Dict								=		-torch.autograd.grad(lowerCamelCase_,	lowerCamelCase_   )[0]

       if isinstance(self.scheduler,	lowerCamelCase_   ):
        lowerCamelCase__	:     Any								=		latents.detach() + grads * (sigma**2)
        lowerCamelCase__	:     Any								=		noise_pred_original
       else:
        lowerCamelCase__	:     Optional[int]								=		noise_pred_original - torch.sqrt(lowerCamelCase_   ) * grads
       return noise_pred, latents





      @torch.no_grad()
      def __call__(self,	lowerCamelCase_,	lowerCamelCase_ = 5_1_2,	lowerCamelCase_ = 5_1_2,	lowerCamelCase_ = 5_0,	lowerCamelCase_ = 7.5,	lowerCamelCase_ = 1,	lowerCamelCase_ = 0.0,	lowerCamelCase_ = 1_0_0,	lowerCamelCase_ = None,	lowerCamelCase_ = 4,	lowerCamelCase_ = True,	lowerCamelCase_ = None,	lowerCamelCase_ = None,	lowerCamelCase_ = "pil",	lowerCamelCase_ = True,	):

       '''simple docstring'''



       if isinstance(lowerCamelCase_,	lowerCamelCase_   ):
        lowerCamelCase__	:     Any								=		1
       elif isinstance(lowerCamelCase_,	lowerCamelCase_   ):
        lowerCamelCase__	:     Optional[int]								=		len(lowerCamelCase_   )
       else:
        raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase_   )}'''   )

       if height % 8 != 0 or width % 8 != 0:
        raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''   )

       # get prompt text embeddings
       lowerCamelCase__	:     List[Any]								=		self.tokenizer(
           lowerCamelCase_,	padding='max_length',	max_length=self.tokenizer.model_max_length,	truncation=lowerCamelCase_,	return_tensors='pt',	)
       lowerCamelCase__	:     Optional[int]								=		self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
       # duplicate text embeddings for each generation per prompt
       lowerCamelCase__	:     Optional[Any]								=		text_embeddings.repeat_interleave(lowerCamelCase_,	dim=0   )

       if clip_guidance_scale > 0:
        if clip_prompt is not None:
         lowerCamelCase__	:     str								=		self.tokenizer(
             lowerCamelCase_,	padding='max_length',	max_length=self.tokenizer.model_max_length,	truncation=lowerCamelCase_,	return_tensors='pt',	).input_ids.to(self.device   )
        else:
         lowerCamelCase__	:     List[Any]								=		text_input.input_ids.to(self.device   )
        lowerCamelCase__	:     Any								=		self.clip_model.get_text_features(lowerCamelCase_   )
        lowerCamelCase__	:     List[Any]								=		text_embeddings_clip / text_embeddings_clip.norm(p=2,	dim=-1,	keepdim=lowerCamelCase_   )
        # duplicate text embeddings clip for each generation per prompt
        lowerCamelCase__	:     Union[str, Any]								=		text_embeddings_clip.repeat_interleave(lowerCamelCase_,	dim=0   )

       # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
       # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
       # corresponds to doing no classifier free guidance.
       lowerCamelCase__	:     Optional[Any]								=		guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
        lowerCamelCase__	:     str								=		text_input.input_ids.shape[-1]
        lowerCamelCase__	:     Union[str, Any]								=		self.tokenizer([''],	padding='max_length',	max_length=lowerCamelCase_,	return_tensors='pt'   )
        lowerCamelCase__	:     Optional[int]								=		self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
        # duplicate unconditional embeddings for each generation per prompt
        lowerCamelCase__	:     Union[str, Any]								=		uncond_embeddings.repeat_interleave(lowerCamelCase_,	dim=0   )

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        lowerCamelCase__	:     Optional[Any]								=		torch.cat([uncond_embeddings, text_embeddings]   )

       # get the initial random noise unless the user supplied it

       # Unlike in other pipelines, latents need to be generated in the target device
       # for 1-to-1 results reproducibility with the CompVis implementation.
       # However this currently doesn't work in `mps`.
       lowerCamelCase__	:     str								=		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       lowerCamelCase__	:     Any								=		text_embeddings.dtype
       if latents is None:
        if self.device.type == "mps":
         # randn does not work reproducibly on mps
         lowerCamelCase__	:     Optional[Any]								=		torch.randn(lowerCamelCase_,	generator=lowerCamelCase_,	device='cpu',	dtype=lowerCamelCase_   ).to(
             self.device   )
        else:
         lowerCamelCase__	:     Optional[Any]								=		torch.randn(lowerCamelCase_,	generator=lowerCamelCase_,	device=self.device,	dtype=lowerCamelCase_   )
       else:
        if latents.shape != latents_shape:
         raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
        lowerCamelCase__	:     Dict								=		latents.to(self.device   )

       # set timesteps
       lowerCamelCase__	:     int								=		'offset' in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
       lowerCamelCase__	:     int								=		{}
       if accepts_offset:
        lowerCamelCase__	:     Optional[int]								=		1

       self.scheduler.set_timesteps(lowerCamelCase_,	**lowerCamelCase_   )

       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       lowerCamelCase__	:     int								=		self.scheduler.timesteps.to(self.device   )

       # scale the initial noise by the standard deviation required by the scheduler
       lowerCamelCase__	:     Tuple								=		latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       lowerCamelCase__	:     Optional[Any]								=		'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
       lowerCamelCase__	:     Any								=		{}
       if accepts_eta:
        lowerCamelCase__	:     Any								=		eta

       # check if the scheduler accepts generator
       lowerCamelCase__	:     Tuple								=		'generator' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
       if accepts_generator:
        lowerCamelCase__	:     Union[str, Any]								=		generator

       for i, t in enumerate(self.progress_bar(lowerCamelCase_   )   ):
        # expand the latents if we are doing classifier free guidance
        lowerCamelCase__	:     Any								=		torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
        lowerCamelCase__	:     List[Any]								=		self.scheduler.scale_model_input(lowerCamelCase_,	lowerCamelCase_   )

        # predict the noise residual
        lowerCamelCase__	:     Tuple								=		self.unet(lowerCamelCase_,	lowerCamelCase_,	encoder_hidden_states=lowerCamelCase_   ).sample

        # perform classifier free guidance
        if do_classifier_free_guidance:
         lowerCamelCase__							,	lowerCamelCase__	:     int								=		noise_pred.chunk(2   )
         lowerCamelCase__	:     Any								=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # perform clip guidance
        if clip_guidance_scale > 0:
         lowerCamelCase__	:     Optional[Any]								=		(
             text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
         )
         lowerCamelCase__							,	lowerCamelCase__	:     Union[str, Any]								=		self.cond_fn(
             lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	)

        # compute the previous noisy sample x_t -> x_t-1
        lowerCamelCase__	:     Any								=		self.scheduler.step(lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	**lowerCamelCase_   ).prev_sample

       # scale and decode the image latents with vae
       lowerCamelCase__	:     Dict								=		1 / self.vae.config.scaling_factor * latents
       lowerCamelCase__	:     List[Any]								=		self.vae.decode(lowerCamelCase_   ).sample

       lowerCamelCase__	:     List[Any]								=		(image / 2 + 0.5).clamp(0,	1   )
       lowerCamelCase__	:     Any								=		image.cpu().permute(0,	2,	3,	1   ).numpy()

       if output_type == "pil":
        lowerCamelCase__	:     List[str]								=		self.numpy_to_pil(lowerCamelCase_   )

       if not return_dict:
        return (image, None)

       return StableDiffusionPipelineOutput(images=lowerCamelCase_,	nsfw_content_detected=lowerCamelCase_   )

