"""simple docstring"""
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)




class   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase       ,     )      ->     Tuple:





        '''simple docstring'''





        super().__init__()

        self.register_modules(vae=lowercase       ,     text_encoder=lowercase       ,     tokenizer=lowercase       ,     unet=lowercase       ,     scheduler=lowercase)



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     int:





        '''simple docstring'''





        with torch.no_grad():
          a__:	List[str]     				=      self.vae.encode(tfms.ToTensor()(lowercase).unsqueeze(0).to(self.device) * 2 - 1)
          a__:	Optional[Any]     				=      0.18215 * latent.latent_dist.sample()
        return latent



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      (1 / 0.18215) * latent
        with torch.no_grad():
          a__:	Optional[int]     				=      self.vae.decode(lowercase).sample
        a__:	Dict     				=      (img / 2 + 0.5).clamp(0       ,     1)
        a__:	List[Any]     				=      img.detach().cpu().permute(0       ,     2       ,     3       ,     1).numpy()
        a__:	Tuple     				=      (img * 2_55).round().astype('uint8')
        return Image.fromarray(img[0])



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        a__:	Any     				=      self.tokenizer(
            lowercase       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[Any]     				=      self.text_encoder(text_input.input_ids.to(self.device))[0]

        a__:	str     				=      self.tokenizer(
            ''       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[int]     				=      self.text_encoder(uncond_input.input_ids.to(self.device))[0]

        return torch.cat([uncond_embedding, text_embedding])



      def __call__(		self       ,     lowercase       ,     lowercase       ,     lowercase = 0.3       ,     lowercase = 0.6       ,     lowercase = 0.5       ,     lowercase = 42       ,     lowercase = 50       ,     lowercase = 7.5       ,     )      ->     Image.Image:





        '''simple docstring'''





        a__:	Optional[Any]     				=      steps - int(kmin * steps)
        a__:	Optional[int]     				=      steps - int(kmax * steps)

        a__:	Optional[int]     				=      self.prep_text(lowercase)

        self.scheduler.set_timesteps(lowercase)

        a__				,       a__:	Union[str, Any]     				=      img.size
        a__:	str     				=      self.encode(lowercase)

        torch.manual_seed(lowercase)
        a__:	Optional[int]     				=      torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)       ,     ).to(self.device)

        a__:	Tuple     				=      self.scheduler.add_noise(
            lowercase       ,     lowercase       ,     timesteps=self.scheduler.timesteps[tmax]       ,     )

        a__:	List[Any]     				=      torch.cat([latents] * 2)

        a__:	List[str]     				=      self.scheduler.scale_model_input(lowercase       ,     self.scheduler.timesteps[tmax])

        with torch.no_grad():
          a__:	Optional[Any]     				=      self.unet(
              lowercase       ,     self.scheduler.timesteps[tmax]       ,     encoder_hidden_states=lowercase       ,     ).sample

        a__				,       a__:	List[str]     				=      pred.chunk(2)
        a__:	Union[str, Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

        a__:	int     				=      self.scheduler.step(lowercase       ,     self.scheduler.timesteps[tmax]       ,     lowercase).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps)):
          if i > tmax:
            if i < tmin:  # layout generation phase
              a__:	List[Any]     				=      self.scheduler.add_noise(
                  lowercase       ,     lowercase       ,     timesteps=lowercase       ,     )

              a__:	Dict     				=      (mix_factor * latents) + (
                  1 - mix_factor
              ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
              a__:	List[Any]     				=      torch.cat([input] * 2)

            else:  # content generation phase
              a__:	Dict     				=      torch.cat([latents] * 2)

            a__:	Union[str, Any]     				=      self.scheduler.scale_model_input(lowercase       ,     lowercase)

            with torch.no_grad():
              a__:	Union[str, Any]     				=      self.unet(
                  lowercase       ,     lowercase       ,     encoder_hidden_states=lowercase       ,     ).sample

            a__				,       a__:	Union[str, Any]     				=      pred.chunk(2)
            a__:	Optional[Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

            a__:	List[str]     				=      self.scheduler.step(lowercase       ,     lowercase       ,     lowercase).prev_sample

        return self.decode(lowercase)









import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          YosoForMaskedLM,
          YosoForMultipleChoice,
          YosoForQuestionAnswering,
          YosoForSequenceClassification,
          YosoForTokenClassification,
          YosoModel,
      )
      from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST







class        __snake_case			:





       '''simple docstring'''







       def __init__(    self					:       Union[str, Any]				,							A					:       Optional[Any]				,							A					:       str=13				,							A					:       List[Any]=7				,							A					:       str=True				,							A					:       Optional[int]=True				,							A					:       Any=True				,							A					:       List[str]=True				,							A					:       str=99				,							A					:       str=32				,							A					:       Any=5				,							A					:       Any=4				,							A					:       str=37				,							A					:       List[Any]="gelu"				,							A					:       Union[str, Any]=0.1				,							A					:       str=0.1				,							A					:       List[Any]=512				,							A					:       Optional[int]=16				,							A					:       List[str]=2				,							A					:       Optional[int]=0.02				,							A					:       Optional[int]=3				,							A					:       int=4				,							A					:       List[Any]=None				,							):
         __snake_case:		Any					    =		parent
         __snake_case:		Optional[int]					    =		batch_size
         __snake_case:		Optional[int]					    =		seq_length
         __snake_case:		Any					    =		is_training
         __snake_case:		int					    =		use_input_mask
         __snake_case:		Tuple					    =		use_token_type_ids
         __snake_case:		Optional[Any]					    =		use_labels
         __snake_case:		Optional[Any]					    =		vocab_size
         __snake_case:		str					    =		hidden_size
         __snake_case:		int					    =		num_hidden_layers
         __snake_case:		Optional[int]					    =		num_attention_heads
         __snake_case:		Tuple					    =		intermediate_size
         __snake_case:		Optional[int]					    =		hidden_act
         __snake_case:		Union[str, Any]					    =		hidden_dropout_prob
         __snake_case:		Any					    =		attention_probs_dropout_prob
         __snake_case:		List[str]					    =		max_position_embeddings
         __snake_case:		Any					    =		type_vocab_size
         __snake_case:		Dict					    =		type_sequence_label_size
         __snake_case:		int					    =		initializer_range
         __snake_case:		Union[str, Any]					    =		num_labels
         __snake_case:		int					    =		num_choices
         __snake_case:		List[Any]					    =		scope




       def 				UpperCAmelCase__					(    self					:       List[Any]						):
         __snake_case:		Any					    =		ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size						)

         __snake_case:		Any					    =		None
         if self.use_input_mask:
           __snake_case:		str					    =		random_attention_mask([self.batch_size, self.seq_length]						)

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

         __snake_case:		int					    =		None
         __snake_case:		Union[str, Any]					    =		None
         __snake_case:		str					    =		None
         if self.use_labels:
           __snake_case:		str					    =		ids_tensor([self.batch_size]				,							self.type_sequence_label_size						)
           __snake_case:		int					    =		ids_tensor([self.batch_size, self.seq_length]				,							self.num_labels						)
           __snake_case:		Tuple					    =		ids_tensor([self.batch_size]				,							self.num_choices						)

         __snake_case:		List[str]					    =		self.get_config()

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




       def 				UpperCAmelCase__					(    self					:       Union[str, Any]						):
         return YosoConfig(
             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				,							is_decoder=A				,							initializer_range=self.initializer_range				,							)




       def 				UpperCAmelCase__					(    self					:       Any						):
         __snake_case:		str					    =		self.get_config()
         __snake_case:		Optional[Any]					    =		300
         return config




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         (
             (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  
         ):		List[str]					    =		self.prepare_config_and_inputs()

         __snake_case:		Optional[Any]					    =		True
         __snake_case:		Optional[Any]					    =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
         __snake_case:		Tuple					    =		ids_tensor([self.batch_size, self.seq_length]				,							vocab_size=2						)

         return (
             config,
             input_ids,
             token_type_ids,
             input_mask,
             sequence_labels,
             token_labels,
             choice_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )




       def 				UpperCAmelCase__					(    self					:       Any				,							A					:       Dict				,							A					:       str				,							A					:       Tuple				,							A					:       Dict				,							A					:       List[str]				,							A					:       List[str]				,							A					:       Optional[int]						):
         __snake_case:		Optional[int]					    =		YosoModel(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		List[Any]					    =		model(A				,							attention_mask=A				,							token_type_ids=A						)
         __snake_case:		str					    =		model(A				,							token_type_ids=A						)
         __snake_case:		int					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)						)




       def 				UpperCAmelCase__					(    self					:       Optional[int]				,							A					:       Dict				,							A					:       int				,							A					:       str				,							A					:       List[str]				,							A					:       int				,							A					:       Optional[int]				,							A					:       str				,							A					:       Optional[int]				,							A					:       str				,							):
         __snake_case:		Optional[Any]					    =		True
         __snake_case:		List[str]					    =		YosoModel(A						)
         model.to(A						)
         model.eval()
         __snake_case:		Optional[int]					    =		model(
             A				,							attention_mask=A				,							token_type_ids=A				,							encoder_hidden_states=A				,							encoder_attention_mask=A				,							)
         __snake_case:		Optional[int]					    =		model(
             A				,							attention_mask=A				,							token_type_ids=A				,							encoder_hidden_states=A				,							)
         __snake_case:		Optional[int]					    =		model(A				,							attention_mask=A				,							token_type_ids=A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)						)




       def 				UpperCAmelCase__					(    self					:       Optional[Any]				,							A					:       str				,							A					:       Optional[int]				,							A					:       List[Any]				,							A					:       Optional[Any]				,							A					:       Dict				,							A					:       int				,							A					:       Optional[int]						):
         __snake_case:		List[str]					    =		YosoForMaskedLM(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		int					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.vocab_size)						)




       def 				UpperCAmelCase__					(    self					:       int				,							A					:       Any				,							A					:       Union[str, Any]				,							A					:       Optional[int]				,							A					:       Any				,							A					:       int				,							A					:       Any				,							A					:       Optional[int]						):
         __snake_case:		int					    =		YosoForQuestionAnswering(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Any					    =		model(
             A				,							attention_mask=A				,							token_type_ids=A				,							start_positions=A				,							end_positions=A				,							)
         self.parent.assertEqual(result.start_logits.shape				,							(self.batch_size, self.seq_length)						)
         self.parent.assertEqual(result.end_logits.shape				,							(self.batch_size, self.seq_length)						)




       def 				UpperCAmelCase__					(    self					:       str				,							A					:       Union[str, Any]				,							A					:       Tuple				,							A					:       Union[str, Any]				,							A					:       int				,							A					:       Optional[int]				,							A					:       int				,							A					:       List[Any]						):
         __snake_case:		Union[str, Any]					    =		self.num_labels
         __snake_case:		Union[str, Any]					    =		YosoForSequenceClassification(A						)
         model.to(A						)
         model.eval()
         __snake_case:		Optional[int]					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)						)




       def 				UpperCAmelCase__					(    self					:       Dict				,							A					:       Optional[int]				,							A					:       Dict				,							A					:       Any				,							A					:       Dict				,							A					:       str				,							A					:       int				,							A					:       Optional[int]						):
         __snake_case:		List[Any]					    =		self.num_labels
         __snake_case:		Union[str, Any]					    =		YosoForTokenClassification(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		List[Any]					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.num_labels)						)




       def 				UpperCAmelCase__					(    self					:       List[str]				,							A					:       Union[str, Any]				,							A					:       Tuple				,							A					:       int				,							A					:       Dict				,							A					:       Optional[int]				,							A					:       str				,							A					:       Optional[Any]						):
         __snake_case:		List[Any]					    =		self.num_choices
         __snake_case:		List[Any]					    =		YosoForMultipleChoice(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		str					    =		input_ids.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
         __snake_case:		Any					    =		token_type_ids.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
         __snake_case:		Optional[int]					    =		input_mask.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
         __snake_case:		Any					    =		model(
             A				,							attention_mask=A				,							token_type_ids=A				,							labels=A				,							)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_choices)						)






       def 				UpperCAmelCase__					(    self					:       List[Any]						):
         __snake_case:		List[str]					    =		self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  
         ):		int					    =		config_and_inputs
         __snake_case:		str					    =		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
         return config, inputs_dict







@require_torch
class        __snake_case			(				__lowerCamelCase  ,     __lowerCamelCase  ,     unittest.TestCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	(
           (
               YosoModel,
               YosoForMaskedLM,
               YosoForMultipleChoice,
               YosoForQuestionAnswering,
               YosoForSequenceClassification,
               YosoForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	False

       lowerCAmelCase__ 						=	()
       lowerCAmelCase__ 						=	(
           {
               """feature-extraction""": YosoModel,
               """fill-mask""": YosoForMaskedLM,
               """question-answering""": YosoForQuestionAnswering,
               """text-classification""": YosoForSequenceClassification,
               """token-classification""": YosoForTokenClassification,
               """zero-shot""": YosoForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )




       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		List[str]					    =		YosoModelTester(self						)
         __snake_case:		Optional[int]					    =		ConfigTester(self				,							config_class=A				,							hidden_size=37						)




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         self.config_tester.run_common_tests()




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         __snake_case:		Union[str, Any]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A						)




       def 				UpperCAmelCase__					(    self					:       Union[str, Any]						):
         __snake_case:		Optional[Any]					    =		self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
           __snake_case:		Optional[Any]					    =		type
           self.model_tester.create_and_check_model(*A						)




       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		List[Any]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*A						)




       def 				UpperCAmelCase__					(    self					:       List[Any]						):
         __snake_case:		List[str]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*A						)




       def 				UpperCAmelCase__					(    self					:       int						):
         __snake_case:		Any					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*A						)




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		List[Any]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*A						)




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         __snake_case:		Optional[int]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*A						)




       @slow
       def 				UpperCAmelCase__					(    self					:       Any						):
         for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __snake_case:		str					    =		YosoModel.from_pretrained(A						)
           self.assertIsNotNone(A						)






       def 				UpperCAmelCase__					(    self					:       Tuple						):
         return







@require_torch
class        __snake_case			(				unittest.TestCase     ):





       '''simple docstring'''







       @slow
       def 				UpperCAmelCase__					(    self					:       Tuple						):
         __snake_case:		Any					    =		YosoModel.from_pretrained("""uw-madison/yoso-4096"""						)
         __snake_case:		Union[str, Any]					    =		torch.tensor([[0, 1, 2, 3, 4, 5]]						)

         with torch.no_grad():
           __snake_case:		Optional[Any]					    =		model(A						)[0]

         __snake_case:		List[str]					    =		torch.Size((1, 6, 768)						)
         self.assertEqual(output.shape				,							A						)

         __snake_case:		List[Any]					    =		torch.tensor(
             [[[-0.0611, 0.1242, 0.0840], [0.0280, -0.0048, 0.1125], [0.0106, 0.0226, 0.0751]]]						)

         self.assertTrue(torch.allclose(output[:, :3, :3]				,							A				,							atol=1E-4						)						)




       @slow
       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		Optional[Any]					    =		YosoForMaskedLM.from_pretrained("""uw-madison/yoso-4096"""						)
         __snake_case:		Tuple					    =		torch.tensor([[0, 1, 2, 3, 4, 5]]						)

         with torch.no_grad():
           __snake_case:		Optional[int]					    =		model(A						)[0]

         __snake_case:		Tuple					    =		50_265

         __snake_case:		List[str]					    =		torch.Size((1, 6, vocab_size)						)
         self.assertEqual(output.shape				,							A						)

         __snake_case:		Any					    =		torch.tensor(
             [[[-2.1313, -3.7285, -2.2407], [-2.7047, -3.3314, -2.6408], [0.0629, -2.5166, -0.3356]]]						)

         self.assertTrue(torch.allclose(output[:, :3, :3]				,							A				,							atol=1E-4						)						)






       @slow
       def 				UpperCAmelCase__					(    self					:       Any						):
         __snake_case:		int					    =		YosoForMaskedLM.from_pretrained("""uw-madison/yoso-4096"""						)
         __snake_case:		Optional[Any]					    =		torch.arange(4_096						).unsqueeze(0						)

         with torch.no_grad():
           __snake_case:		Tuple					    =		model(A						)[0]

         __snake_case:		List[str]					    =		50_265

         __snake_case:		Union[str, Any]					    =		torch.Size((1, 4_096, vocab_size)						)
         self.assertEqual(output.shape				,							A						)

         __snake_case:		List[str]					    =		torch.tensor(
             [[[-2.3914, -4.3742, -5.0956], [-4.0988, -4.2384, -7.0406], [-3.1427, -3.7192, -6.6800]]]						)

         self.assertTrue(torch.allclose(output[:, :3, :3]				,							A				,							atol=1E-4						)						)






