




'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     ={
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A     =['RoFormerTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A     =[
       'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'RoFormerForCausalLM',
       'RoFormerForMaskedLM',
       'RoFormerForMultipleChoice',
       'RoFormerForQuestionAnswering',
       'RoFormerForSequenceClassification',
       'RoFormerForTokenClassification',
       'RoFormerLayer',
       'RoFormerModel',
       'RoFormerPreTrainedModel',
       'load_tf_weights_in_roformer',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A     =[
       'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFRoFormerForCausalLM',
       'TFRoFormerForMaskedLM',
       'TFRoFormerForMultipleChoice',
       'TFRoFormerForQuestionAnswering',
       'TFRoFormerForSequenceClassification',
       'TFRoFormerForTokenClassification',
       'TFRoFormerLayer',
       'TFRoFormerModel',
       'TFRoFormerPreTrainedModel',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A     =[
       'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaxRoFormerForMaskedLM',
       'FlaxRoFormerForMultipleChoice',
       'FlaxRoFormerForQuestionAnswering',
       'FlaxRoFormerForSequenceClassification',
       'FlaxRoFormerForTokenClassification',
       'FlaxRoFormerModel',
       'FlaxRoFormerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
   from .tokenization_roformer import RoFormerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roformer_fast import RoFormerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roformer import (
          ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoFormerForCausalLM,
          RoFormerForMaskedLM,
          RoFormerForMultipleChoice,
          RoFormerForQuestionAnswering,
          RoFormerForSequenceClassification,
          RoFormerForTokenClassification,
          RoFormerLayer,
          RoFormerModel,
          RoFormerPreTrainedModel,
          load_tf_weights_in_roformer,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roformer import (
          TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForMultipleChoice,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerLayer,
          TFRoFormerModel,
          TFRoFormerPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roformer import (
          FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerModel,
          FlaxRoFormerPreTrainedModel,
      )


else:
   import sys

   A     =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












import unittest

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

from diffusers import KandinskyVaaPriorPipeline, 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 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  KandinskyVaaPriorPipeline
      __snake_case       =  ['''prompt''']
      __snake_case       =  ['''prompt''', '''negative_prompt''']
      __snake_case       =  [
          '''num_images_per_prompt''',
          '''generator''',
          '''num_inference_steps''',
          '''latents''',
          '''negative_prompt''',
          '''guidance_scale''',
          '''output_type''',
          '''return_dict''',
      ]
      __snake_case       =  False







      @property
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Union[str, Any]:




       """simple docstring"""


       return 32







      @property
      def       __lowerCAmelCase					(      self      :						Dict  )    ->Any:




       """simple docstring"""


       return 32







      @property
      def       __lowerCAmelCase					(      self      :						int  )    ->List[str]:




       """simple docstring"""


       return self.time_input_dim







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Any:




       """simple docstring"""


       return self.time_input_dim * 4







      @property
      def       __lowerCAmelCase					(      self      :						Any  )    ->List[Any]:




       """simple docstring"""


       return 100







      @property
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->str:




       """simple docstring"""


       a		  =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )
       return tokenizer







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->str:




       """simple docstring"""


       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=1_000 ,					)
       return CLIPTextModelWithProjection(__UpperCAmelCase  )







      @property
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Optional[Any]:




       """simple docstring"""


       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       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[Any]:




       """simple docstring"""


       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       __lowerCAmelCase					(      self      :						Tuple  )    ->int:




       """simple docstring"""


       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       __lowerCAmelCase					(      self      :						List[Any]  )    ->Optional[Any]:




       """simple docstring"""


       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=1_000 ,					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       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						str=0  )    ->int:




       """simple docstring"""


       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       __lowerCAmelCase					(      self      :						str  )    ->Tuple:




       """simple docstring"""


       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       __lowerCAmelCase					(      self      :						List[Any]  )    ->Optional[Any]:




       """simple docstring"""


       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       __lowerCAmelCase					(      self      :						List[str]  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      torch_device == '''cpu'''
       a		  =      False

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


