


"""simple docstring"""






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class      __lowerCamelCase	(       a__	,       unittest.TestCase					):

     '''simple docstring'''
     A_							:						Dict     =		KandinskyPriorPipeline
     A_							:						List[Any]     =		['prompt']
     A_							:						List[Any]     =		['prompt', 'negative_prompt']
     A_							:						str     =		[
         'num_images_per_prompt',
         'generator',
         'num_inference_steps',
         'latents',
         'negative_prompt',
         'guidance_scale',
         'output_type',
         'return_dict',
     ]
     A_							:						List[str]     =		False


     @property
     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          return 32


     @property
     def 				_UpperCAmelCase   (   self				)							->				str:
          return 32


     @property
     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          return self.time_input_dim


     @property
     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          return self.time_input_dim * 4


     @property
     def 				_UpperCAmelCase   (   self				)							->				str:
          return 100


     @property
     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          _a     						=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)
          return tokenizer


     @property
     def 				_UpperCAmelCase   (   self				)							->				Dict:
          torch.manual_seed(0				)
          _a     						=					CLIPTextConfig(
              bos_token_id=0     ,					eos_token_id=2     ,					hidden_size=self.text_embedder_hidden_size     ,					projection_dim=self.text_embedder_hidden_size     ,					intermediate_size=37     ,					layer_norm_eps=1e-0_5     ,					num_attention_heads=4     ,					num_hidden_layers=5     ,					pad_token_id=1     ,					vocab_size=1000     ,					)
          return CLIPTextModelWithProjection(__UpperCAmelCase				)


     @property
     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          torch.manual_seed(0				)

          _a     						=					{
              '''num_attention_heads''': 2,
              '''attention_head_dim''': 12,
              '''embedding_dim''': self.text_embedder_hidden_size,
              '''num_layers''': 1,
          }

          _a     						=					PriorTransformer(**__UpperCAmelCase				)
          # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
          _a     						=					nn.Parameter(torch.ones(model.clip_std.shape				)				)
          return model


     @property
     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          torch.manual_seed(0				)
          _a     						=					CLIPVisionConfig(
              hidden_size=self.text_embedder_hidden_size     ,					image_size=224     ,					projection_dim=self.text_embedder_hidden_size     ,					intermediate_size=37     ,					num_attention_heads=4     ,					num_channels=3     ,					num_hidden_layers=5     ,					patch_size=14     ,					)

          _a     						=					CLIPVisionModelWithProjection(__UpperCAmelCase				)
          return model


     @property
     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          _a     						=					CLIPImageProcessor(
              crop_size=224     ,					do_center_crop=__UpperCAmelCase     ,					do_normalize=__UpperCAmelCase     ,					do_resize=__UpperCAmelCase     ,					image_mean=[0.48145466, 0.4578275, 0.40821073]     ,					image_std=[0.26862954, 0.26130258, 0.27577711]     ,					resample=3     ,					size=224     ,					)

          return image_processor


     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          _a     						=					self.dummy_prior
          _a     						=					self.dummy_image_encoder
          _a     						=					self.dummy_text_encoder
          _a     						=					self.dummy_tokenizer
          _a     						=					self.dummy_image_processor

          _a     						=					UnCLIPScheduler(
              variance_type='''fixed_small_log'''     ,					prediction_type='''sample'''     ,					num_train_timesteps=1000     ,					clip_sample=__UpperCAmelCase     ,					clip_sample_range=10.0     ,					)

          _a     						=					{
              '''prior''': prior,
              '''image_encoder''': image_encoder,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''scheduler''': scheduler,
              '''image_processor''': image_processor,
          }

          return components


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=0				)							->				str:
          if str(__UpperCAmelCase				).startswith('''mps'''				):
               _a     						=					torch.manual_seed(__UpperCAmelCase				)
          else:
               _a     						=					torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)
          _a     						=					{
              '''prompt''': '''horse''',
              '''generator''': generator,
              '''guidance_scale''': 4.0,
              '''num_inference_steps''': 2,
              '''output_type''': '''np''',
          }
          return inputs


     def 				_UpperCAmelCase   (   self				)							->				str:
          _a     						=					'''cpu'''

          _a     						=					self.get_dummy_components()

          _a     						=					self.pipeline_class(**__UpperCAmelCase				)
          _a     						=					pipe.to(__UpperCAmelCase				)

          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

          _a     						=					pipe(**self.get_dummy_inputs(__UpperCAmelCase				)				)
          _a     						=					output.image_embeds

          _a     						=					pipe(
              **self.get_dummy_inputs(__UpperCAmelCase				)     ,					return_dict=__UpperCAmelCase     ,					)[0]

          _a     						=					image[0, -10:]
          _a     						=					image_from_tuple[0, -10:]

          assert image.shape == (1, 32)

          _a     						=					np.array(
              [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]				)

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2


     @skip_mps
     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          _a     						=					torch_device == '''cpu'''
          _a     						=					True
          _a     						=					False

          self._test_inference_batch_single_identical(
              test_max_difference=__UpperCAmelCase     ,					relax_max_difference=__UpperCAmelCase     ,					test_mean_pixel_difference=__UpperCAmelCase     ,					)





     @skip_mps
     def 				_UpperCAmelCase   (   self				)							->				Any:
          _a     						=					torch_device == '''cpu'''
          _a     						=					False

          self._test_attention_slicing_forward_pass(
              test_max_difference=__UpperCAmelCase     ,					test_mean_pixel_difference=__UpperCAmelCase     ,					)



"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__snake_case												=							logging.get_logger(__name__)

__snake_case												=							{
    '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/config.json''',
    '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/config.json''',
    '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json''',
    '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json''',
    '''microsoft/deberta-xlarge-mnli''': '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json''',
}


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						List[Any]     =		'deberta'


     def __init__(   self     ,					__UpperCAmelCase=50265     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=0.02     ,					__UpperCAmelCase=1e-7     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=-1     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase="gelu"     ,					**__UpperCAmelCase     ,					)							->				Any:
          super().__init__(**__UpperCAmelCase				)

          _a     						=					hidden_size
          _a     						=					num_hidden_layers
          _a     						=					num_attention_heads
          _a     						=					intermediate_size
          _a     						=					hidden_act
          _a     						=					hidden_dropout_prob
          _a     						=					attention_probs_dropout_prob
          _a     						=					max_position_embeddings
          _a     						=					type_vocab_size
          _a     						=					initializer_range
          _a     						=					relative_attention
          _a     						=					max_relative_positions
          _a     						=					pad_token_id
          _a     						=					position_biased_input

          # Backwards compatibility
          if type(__UpperCAmelCase				) == str:
               _a     						=					[x.strip() for x in pos_att_type.lower().split('''|'''				)]

          _a     						=					pos_att_type
          _a     						=					vocab_size
          _a     						=					layer_norm_eps

          _a     						=					kwargs.get('''pooler_hidden_size'''     ,					__UpperCAmelCase				)
          _a     						=					pooler_dropout
          _a     						=					pooler_hidden_act







class      __lowerCamelCase	(       a__					):

     '''simple docstring'''


     @property
     def 				_UpperCAmelCase   (   self				)							->				Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _a     						=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
               _a     						=					{0: '''batch''', 1: '''sequence'''}
          if self._config.type_vocab_size > 0:
               return OrderedDict(
                   [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]				)
          else:
               return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]				)


     @property
     def 				_UpperCAmelCase   (   self				)							->				int:
          return 12





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = 3     ,					__UpperCAmelCase = 40     ,					__UpperCAmelCase = 40     ,					__UpperCAmelCase = None     ,					)							->				Mapping[str, Any]:
          _a     						=					super().generate_dummy_inputs(preprocessor=__UpperCAmelCase     ,					framework=__UpperCAmelCase				)
          if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
               del dummy_inputs["token_type_ids"]
          return dummy_inputs