






"""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 						a_ :







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_=1_3,	lowerCamelCase_=7,	lowerCamelCase_=True,	lowerCamelCase_=True,	lowerCamelCase_=True,	lowerCamelCase_=True,	lowerCamelCase_=9_9,	lowerCamelCase_=[1, 1, 2],	lowerCamelCase_=1,	lowerCamelCase_=3_2,	lowerCamelCase_=4,	lowerCamelCase_=8,	lowerCamelCase_=3_7,	lowerCamelCase_="gelu_new",	lowerCamelCase_=0.1,	lowerCamelCase_=0.1,	lowerCamelCase_=0.0,	lowerCamelCase_=5_1_2,	lowerCamelCase_=3,	lowerCamelCase_=0.02,	lowerCamelCase_=3,	lowerCamelCase_=4,	lowerCamelCase_=None,	lowerCamelCase_=False,	):

       '''simple docstring'''



       lowerCamelCase__	:     Tuple								=		parent
       lowerCamelCase__	:     Optional[Any]								=		batch_size
       lowerCamelCase__	:     Optional[int]								=		seq_length
       lowerCamelCase__	:     List[str]								=		is_training
       lowerCamelCase__	:     Dict								=		use_input_mask
       lowerCamelCase__	:     Union[str, Any]								=		use_token_type_ids
       lowerCamelCase__	:     Optional[Any]								=		use_labels
       lowerCamelCase__	:     Any								=		vocab_size
       lowerCamelCase__	:     Any								=		block_sizes
       lowerCamelCase__	:     Any								=		num_decoder_layers
       lowerCamelCase__	:     Dict								=		d_model
       lowerCamelCase__	:     str								=		n_head
       lowerCamelCase__	:     Optional[Any]								=		d_head
       lowerCamelCase__	:     int								=		d_inner
       lowerCamelCase__	:     Dict								=		hidden_act
       lowerCamelCase__	:     Any								=		hidden_dropout
       lowerCamelCase__	:     Any								=		attention_dropout
       lowerCamelCase__	:     Union[str, Any]								=		activation_dropout
       lowerCamelCase__	:     List[str]								=		max_position_embeddings
       lowerCamelCase__	:     Any								=		type_vocab_size
       lowerCamelCase__	:     List[str]								=		2
       lowerCamelCase__	:     Dict								=		num_labels
       lowerCamelCase__	:     List[Any]								=		num_choices
       lowerCamelCase__	:     Optional[Any]								=		scope
       lowerCamelCase__	:     List[Any]								=		initializer_std

       # Used in the tests to check the size of the first attention layer
       lowerCamelCase__	:     Tuple								=		n_head
       # Used in the tests to check the size of the first hidden state
       lowerCamelCase__	:     str								=		self.d_model
       # Used in the tests to check the number of output hidden states/attentions
       lowerCamelCase__	:     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:
        lowerCamelCase__	:     List[Any]								=		self.num_hidden_layers + 2




      def      a__				(self   ):

       '''simple docstring'''



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

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

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

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

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

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




      def      a__				(self   ):

       '''simple docstring'''



       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      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		FunnelModel(config=lowerCamelCase_   )
       model.to(lowerCamelCase_   )
       model.eval()
       lowerCamelCase__	:     Optional[int]								=		model(lowerCamelCase_,	attention_mask=lowerCamelCase_,	token_type_ids=lowerCamelCase_   )
       lowerCamelCase__	:     str								=		model(lowerCamelCase_,	token_type_ids=lowerCamelCase_   )
       lowerCamelCase__	:     str								=		model(lowerCamelCase_   )
       self.parent.assertEqual(result.last_hidden_state.shape,	(self.batch_size, self.seq_length, self.d_model)   )

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

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




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	):

       '''simple docstring'''



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

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

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




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		self.num_labels
       lowerCamelCase__	:     str								=		FunnelForPreTraining(config=lowerCamelCase_   )
       model.to(lowerCamelCase_   )
       model.eval()
       lowerCamelCase__	:     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      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	):

       '''simple docstring'''



       lowerCamelCase__	:     Union[str, Any]								=		FunnelForMaskedLM(config=lowerCamelCase_   )
       model.to(lowerCamelCase_   )
       model.eval()
       lowerCamelCase__	:     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      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	):

       '''simple docstring'''



       lowerCamelCase__	:     List[str]								=		self.num_labels
       lowerCamelCase__	:     Tuple								=		FunnelForSequenceClassification(lowerCamelCase_   )
       model.to(lowerCamelCase_   )
       model.eval()
       lowerCamelCase__	:     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      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		self.num_choices
       lowerCamelCase__	:     Union[str, Any]								=		FunnelForMultipleChoice(config=lowerCamelCase_   )
       model.to(lowerCamelCase_   )
       model.eval()
       lowerCamelCase__	:     List[Any]								=		input_ids.unsqueeze(1   ).expand(-1,	self.num_choices,	-1   ).contiguous()
       lowerCamelCase__	:     List[Any]								=		token_type_ids.unsqueeze(1   ).expand(-1,	self.num_choices,	-1   ).contiguous()
       lowerCamelCase__	:     Tuple								=		input_mask.unsqueeze(1   ).expand(-1,	self.num_choices,	-1   ).contiguous()
       lowerCamelCase__	:     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      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	):

       '''simple docstring'''



       lowerCamelCase__	:     List[Any]								=		self.num_labels
       lowerCamelCase__	:     str								=		FunnelForTokenClassification(config=lowerCamelCase_   )
       model.to(lowerCamelCase_   )
       model.eval()
       lowerCamelCase__	:     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      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	):

       '''simple docstring'''



       lowerCamelCase__	:     Tuple								=		FunnelForQuestionAnswering(config=lowerCamelCase_   )
       model.to(lowerCamelCase_   )
       model.eval()
       lowerCamelCase__	:     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      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		self.prepare_config_and_inputs()
       (
           (
           lowerCamelCase__
       )							,	(
           lowerCamelCase__
       )							,	(
           lowerCamelCase__
       )							,	(
           lowerCamelCase__
       )							,	(
           lowerCamelCase__
       )							,	(
           lowerCamelCase__
       )							,	(
           lowerCamelCase__
       )							,	(
           lowerCamelCase__
       )							,	
       )	:     Union[str, Any]								=		config_and_inputs
       lowerCamelCase__	:     str								=		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
       return config, inputs_dict



@require_torch
class 						a_ (      snake_case_    ,		snake_case_    ,		unittest.TestCase					):







      '''simple docstring'''

      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      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_=False   ):

       '''simple docstring'''



       lowerCamelCase__	:     Tuple								=		super()._prepare_for_class(lowerCamelCase_,	lowerCamelCase_,	return_labels=lowerCamelCase_   )

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




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Union[str, Any]								=		FunnelModelTester(self   )
       lowerCamelCase__	:     List[str]								=		ConfigTester(self,	config_class=lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       self.config_tester.run_common_tests()




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     int								=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[str]								=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[str]								=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Tuple								=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     int								=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_   )





      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       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:
         lowerCamelCase__	:     int								=		getattr(lowerCamelCase_,	lowerCamelCase_   )
         weight.data.fill_(3   )



@require_torch
class 						a_ (      snake_case_    ,		unittest.TestCase					):







      '''simple docstring'''

      lowerCamelCase__			:							Dict					=      False
      lowerCamelCase__			:							Tuple					=      False
      lowerCamelCase__			:							List[str]					=      (
          (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
      )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[Any]								=		FunnelModelTester(self,	base=lowerCamelCase_   )
       lowerCamelCase__	:     Dict								=		ConfigTester(self,	config_class=lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       self.config_tester.run_common_tests()




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[int]								=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_base_model(*lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     int								=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[int]								=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__							,	lowerCamelCase__	:     Union[str, Any]								=		self.model_tester.prepare_config_and_inputs_for_common()
       lowerCamelCase__	:     Union[str, Any]								=		True

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





      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       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:
         lowerCamelCase__	:     int								=		getattr(lowerCamelCase_,	lowerCamelCase_   )
         weight.data.fill_(3   )



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







      '''simple docstring'''

      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		1_3
       lowerCamelCase__	:     Union[str, Any]								=		7
       lowerCamelCase__	:     Optional[Any]								=		torch.arange(0,	batch_size * sequence_length   ).long().reshape(lowerCamelCase_,	lowerCamelCase_   )
       lowerCamelCase__	:     Optional[int]								=		[0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
       lowerCamelCase__	:     Tuple								=		torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]   )

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

       lowerCamelCase__	:     Tuple								=		torch.tensor(2_344.8_352   )
       lowerCamelCase__	:     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   )   )

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

       lowerCamelCase__	:     Any								=		torch.tensor(2_343.8_425   )
       lowerCamelCase__	:     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      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Tuple								=		FunnelTokenizer.from_pretrained('huggingface/funnel-small'   )
       lowerCamelCase__	:     int								=		FunnelModel.from_pretrained('huggingface/funnel-small'   )
       lowerCamelCase__	:     List[str]								=		tokenizer('Hello! I am the Funnel Transformer model.',	return_tensors='pt'   )
       lowerCamelCase__	:     Tuple								=		model(**lowerCamelCase_   )[0]

       lowerCamelCase__	:     List[Any]								=		torch.tensor(235.7_246   )
       lowerCamelCase__	:     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_   )

