'''simple docstring'''




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   _lowercase:





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





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

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

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

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

    __UpperCAmelCase         =					self.get_config()

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





   def 			snake_case	(							self:    Optional[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 			snake_case	(							self:    Optional[Any]	):
    __UpperCAmelCase         =					self.get_config()
    __UpperCAmelCase         =					300
    return config





   def 			snake_case	(							self:    Any	):
    (
        (
        __UpperCAmelCase 
    )  ,			(
        __UpperCAmelCase 
    )  ,			(
        __UpperCAmelCase 
    )  ,			(
        __UpperCAmelCase 
    )  ,			(
        __UpperCAmelCase 
    )  ,			(
        __UpperCAmelCase 
    )  ,			(
        __UpperCAmelCase 
    )  ,			 
    )         =					self.prepare_config_and_inputs()

    __UpperCAmelCase         =					True
    __UpperCAmelCase         =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
    __UpperCAmelCase         =					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 			snake_case	(							self:    int			,a:    str			,a:    Any			,a:    Union[str, Any]			,a:    int			,a:    Optional[Any]			,a:    Dict			,a:    Union[str, Any]	):
    __UpperCAmelCase         =					YosoModel(config=a	)
    model.to(a	)
    model.eval()
    __UpperCAmelCase         =					model(a			,attention_mask=a			,token_type_ids=a	)
    __UpperCAmelCase         =					model(a			,token_type_ids=a	)
    __UpperCAmelCase         =					model(a	)
    self.parent.assertEqual(result.last_hidden_state.shape			,(self.batch_size, self.seq_length, self.hidden_size)	)





   def 			snake_case	(							self:    Union[str, Any]			,a:    Optional[int]			,a:    Optional[int]			,a:    Optional[Any]			,a:    Any			,a:    List[Any]			,a:    Union[str, Any]			,a:    Optional[Any]			,a:    Union[str, Any]			,a:    Any			,):
    __UpperCAmelCase         =					True
    __UpperCAmelCase         =					YosoModel(a	)
    model.to(a	)
    model.eval()
    __UpperCAmelCase         =					model(
        a			,attention_mask=a			,token_type_ids=a			,encoder_hidden_states=a			,encoder_attention_mask=a			,)
    __UpperCAmelCase         =					model(
        a			,attention_mask=a			,token_type_ids=a			,encoder_hidden_states=a			,)
    __UpperCAmelCase         =					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 			snake_case	(							self:    str			,a:    Tuple			,a:    Any			,a:    str			,a:    List[str]			,a:    int			,a:    Dict			,a:    Optional[int]	):
    __UpperCAmelCase         =					YosoForMaskedLM(config=a	)
    model.to(a	)
    model.eval()
    __UpperCAmelCase         =					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 			snake_case	(							self:    Union[str, Any]			,a:    Union[str, Any]			,a:    List[Any]			,a:    int			,a:    List[Any]			,a:    Optional[Any]			,a:    Union[str, Any]			,a:    Dict	):
    __UpperCAmelCase         =					YosoForQuestionAnswering(config=a	)
    model.to(a	)
    model.eval()
    __UpperCAmelCase         =					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 			snake_case	(							self:    Optional[int]			,a:    Optional[int]			,a:    Optional[Any]			,a:    Tuple			,a:    str			,a:    str			,a:    Optional[Any]			,a:    Tuple	):
    __UpperCAmelCase         =					self.num_labels
    __UpperCAmelCase         =					YosoForSequenceClassification(a	)
    model.to(a	)
    model.eval()
    __UpperCAmelCase         =					model(a			,attention_mask=a			,token_type_ids=a			,labels=a	)
    self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)	)





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





   def 			snake_case	(							self:    Union[str, Any]	):
    __UpperCAmelCase         =					self.prepare_config_and_inputs()
    (
        (
        __UpperCAmelCase 
    )  ,			(
        __UpperCAmelCase 
    )  ,			(
        __UpperCAmelCase 
    )  ,			(
        __UpperCAmelCase 
    )  ,			(
        __UpperCAmelCase 
    )  ,			(
        __UpperCAmelCase 
    )  ,			(
        __UpperCAmelCase 
    )  ,			 
    )         =					config_and_inputs
    __UpperCAmelCase         =					{'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    =  (
       (
           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 			snake_case	(							self:    Any	):
    __UpperCAmelCase         =					YosoModelTester(self	)
    __UpperCAmelCase         =					ConfigTester(self			,config_class=a			,hidden_size=37	)





   def 			snake_case	(							self:    Tuple	):
    self.config_tester.run_common_tests()





   def 			snake_case	(							self:    Tuple	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a	)





   def 			snake_case	(							self:    Optional[Any]	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
     __UpperCAmelCase         =					type
     self.model_tester.create_and_check_model(*a	)





   def 			snake_case	(							self:    Union[str, Any]	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*a	)





   def 			snake_case	(							self:    List[str]	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_multiple_choice(*a	)





   def 			snake_case	(							self:    Dict	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*a	)





   def 			snake_case	(							self:    List[str]	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*a	)





   def 			snake_case	(							self:    Any	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*a	)





   @slow
   def 			snake_case	(							self:    List[Any]	):
    for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __UpperCAmelCase         =					YosoModel.from_pretrained(a	)
     self.assertIsNotNone(a	)





   def 			snake_case	(							self:    Optional[int]	):
    return




@require_torch
class   _lowercase(     unittest.TestCase				):





   @slow
   def 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase         =					YosoModel.from_pretrained('uw-madison/yoso-4096'	)
    __UpperCAmelCase         =					torch.tensor([[0, 1, 2, 3, 4, 5]]	)

    with torch.no_grad():
     __UpperCAmelCase         =					model(a	)[0]

    __UpperCAmelCase         =					torch.Size((1, 6, 768)	)
    self.assertEqual(output.shape			,a	)

    __UpperCAmelCase         =					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 			snake_case	(							self:    Optional[Any]	):
    __UpperCAmelCase         =					YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096'	)
    __UpperCAmelCase         =					torch.tensor([[0, 1, 2, 3, 4, 5]]	)

    with torch.no_grad():
     __UpperCAmelCase         =					model(a	)[0]

    __UpperCAmelCase         =					50265

    __UpperCAmelCase         =					torch.Size((1, 6, vocab_size)	)
    self.assertEqual(output.shape			,a	)

    __UpperCAmelCase         =					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 			snake_case	(							self:    Dict	):
    __UpperCAmelCase         =					YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096'	)
    __UpperCAmelCase         =					torch.arange(4096	).unsqueeze(0	)

    with torch.no_grad():
     __UpperCAmelCase         =					model(a	)[0]

    __UpperCAmelCase         =					50265

    __UpperCAmelCase         =					torch.Size((1, 4096, vocab_size)	)
    self.assertEqual(output.shape			,a	)

    __UpperCAmelCase         =					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	)	)




'''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   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   def __init__(							self:    List[str]			,a:    AutoencoderKL			,a:    CLIPTextModel			,a:    CLIPTokenizer			,a:    UNetaDConditionModel			,a:    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]			,):
    super().__init__()

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





   def 			snake_case	(							self:    Union[str, Any]			,a:    Any	):
    with torch.no_grad():
     __UpperCAmelCase         =					self.vae.encode(tfms.ToTensor()(a	).unsqueeze(0	).to(self.device	) * 2 - 1	)
     __UpperCAmelCase         =					0.18215 * latent.latent_dist.sample()
    return latent





   def 			snake_case	(							self:    Any			,a:    List[str]	):
    __UpperCAmelCase         =					(1 / 0.18215) * latent
    with torch.no_grad():
     __UpperCAmelCase         =					self.vae.decode(a	).sample
    __UpperCAmelCase         =					(img / 2 + 0.5).clamp(0			,1	)
    __UpperCAmelCase         =					img.detach().cpu().permute(0			,2			,3			,1	).numpy()
    __UpperCAmelCase         =					(img * 255).round().astype('uint8'	)
    return Image.fromarray(img[0]	)





   def 			snake_case	(							self:    Any			,a:    str	):
    __UpperCAmelCase         =					self.tokenizer(
        a			,padding='max_length'			,max_length=self.tokenizer.model_max_length			,truncation=a			,return_tensors='pt'			,)

    __UpperCAmelCase         =					self.text_encoder(text_input.input_ids.to(self.device	)	)[0]

    __UpperCAmelCase         =					self.tokenizer(
        ''			,padding='max_length'			,max_length=self.tokenizer.model_max_length			,truncation=a			,return_tensors='pt'			,)

    __UpperCAmelCase         =					self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

    return torch.cat([uncond_embedding, text_embedding]	)





   def __call__(							self:    Optional[Any]			,a:    Image.Image			,a:    str			,a:    float = 0.3			,a:    float = 0.6			,a:    float = 0.5			,a:    int = 42			,a:    int = 50			,a:    float = 7.5			,):
    __UpperCAmelCase         =					steps - int(kmin * steps	)
    __UpperCAmelCase         =					steps - int(kmax * steps	)

    __UpperCAmelCase         =					self.prep_text(a	)

    self.scheduler.set_timesteps(a	)

    __UpperCAmelCase  ,			__UpperCAmelCase         =					img.size
    __UpperCAmelCase         =					self.encode(a	)

    torch.manual_seed(a	)
    __UpperCAmelCase         =					torch.randn(
        (1, self.unet.config.in_channels, height // 8, width // 8)			,).to(self.device	)

    __UpperCAmelCase         =					self.scheduler.add_noise(
        a			,a			,timesteps=self.scheduler.timesteps[tmax]			,)

    __UpperCAmelCase         =					torch.cat([latents] * 2	)

    __UpperCAmelCase         =					self.scheduler.scale_model_input(a			,self.scheduler.timesteps[tmax]	)

    with torch.no_grad():
     __UpperCAmelCase         =					self.unet(
         a			,self.scheduler.timesteps[tmax]			,encoder_hidden_states=a			,).sample

    __UpperCAmelCase  ,			__UpperCAmelCase         =					pred.chunk(2	)
    __UpperCAmelCase         =					pred_uncond + guidance_scale * (pred_text - pred_uncond)

    __UpperCAmelCase         =					self.scheduler.step(a			,self.scheduler.timesteps[tmax]			,a	).prev_sample

    for i, t in enumerate(tqdm(self.scheduler.timesteps	)	):
     if i > tmax:
      if i < tmin:  # layout generation phase
       __UpperCAmelCase         =					self.scheduler.add_noise(
           a			,a			,timesteps=a			,)

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

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

      __UpperCAmelCase         =					self.scheduler.scale_model_input(a			,a	)

      with torch.no_grad():
       __UpperCAmelCase         =					self.unet(
           a			,a			,encoder_hidden_states=a			,).sample

      __UpperCAmelCase  ,			__UpperCAmelCase         =					pred.chunk(2	)
      __UpperCAmelCase         =					pred_uncond + guidance_scale * (pred_text - pred_uncond)

      __UpperCAmelCase         =					self.scheduler.step(a			,a			,a	).prev_sample

    return self.decode(a	)



