





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   __snake_case				(			_a       ,				unittest.TestCase   ):






    '''simple docstring'''
    _lowerCAmelCase     =	KandinskyPriorPipeline
    _lowerCAmelCase     =	["""prompt"""]
    _lowerCAmelCase     =	["""prompt""", """negative_prompt"""]
    _lowerCAmelCase     =	[
        """num_images_per_prompt""",
        """generator""",
        """num_inference_steps""",
        """latents""",
        """negative_prompt""",
        """guidance_scale""",
        """output_type""",
        """return_dict""",
    ]
    _lowerCAmelCase     =	False





    @property
    def 	A		(   self						:		str							):

      """simple docstring"""





      return 32





    @property
    def 	A		(   self						:		Tuple							):

      """simple docstring"""





      return 32





    @property
    def 	A		(   self						:		List[str]							):

      """simple docstring"""





      return self.time_input_dim





    @property
    def 	A		(   self						:		Any							):

      """simple docstring"""





      return self.time_input_dim * 4





    @property
    def 	A		(   self						:		Tuple							):

      """simple docstring"""





      return 100





    @property
    def 	A		(   self						:		Union[str, Any]							):

      """simple docstring"""





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





    @property
    def 	A		(   self						:		List[Any]							):

      """simple docstring"""





      torch.manual_seed(0							)
      __lowerCAmelCase			=				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-05			,						num_attention_heads=4			,						num_hidden_layers=5			,						pad_token_id=1			,						vocab_size=1_000			,						)
      return CLIPTextModelWithProjection(snake_case							)





    @property
    def 	A		(   self						:		Dict							):

      """simple docstring"""





      torch.manual_seed(0							)

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

      __lowerCAmelCase			=				PriorTransformer(**snake_case							)
      # 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
      __lowerCAmelCase			=				nn.Parameter(torch.ones(model.clip_std.shape							)							)
      return model





    @property
    def 	A		(   self						:		List[Any]							):

      """simple docstring"""





      torch.manual_seed(0							)
      __lowerCAmelCase			=				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			,						)

      __lowerCAmelCase			=				CLIPVisionModelWithProjection(snake_case							)
      return model





    @property
    def 	A		(   self						:		Union[str, Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				CLIPImageProcessor(
          crop_size=224			,						do_center_crop=snake_case			,						do_normalize=snake_case			,						do_resize=snake_case			,						image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]			,						image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]			,						resample=3			,						size=224			,						)

      return image_processor





    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				self.dummy_prior
      __lowerCAmelCase			=				self.dummy_image_encoder
      __lowerCAmelCase			=				self.dummy_text_encoder
      __lowerCAmelCase			=				self.dummy_tokenizer
      __lowerCAmelCase			=				self.dummy_image_processor

      __lowerCAmelCase			=				UnCLIPScheduler(
          variance_type='''fixed_small_log'''			,						prediction_type='''sample'''			,						num_train_timesteps=1_000			,						clip_sample=snake_case			,						clip_sample_range=10.0			,						)

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

      return components





    def 	A		(   self						:		Optional[int]			,						snake_case						:		Dict			,						snake_case						:		List[str]=0							):

      """simple docstring"""





      if str(snake_case							).startswith('''mps'''							):
        __lowerCAmelCase			=				torch.manual_seed(snake_case							)
      else:
        __lowerCAmelCase			=				torch.Generator(device=snake_case							).manual_seed(snake_case							)
      __lowerCAmelCase			=				{
          '''prompt''': '''horse''',
          '''generator''': generator,
          '''guidance_scale''': 4.0,
          '''num_inference_steps''': 2,
          '''output_type''': '''np''',
      }
      return inputs





    def 	A		(   self						:		List[str]							):

      """simple docstring"""





      __lowerCAmelCase			=				'''cpu'''

      __lowerCAmelCase			=				self.get_dummy_components()

      __lowerCAmelCase			=				self.pipeline_class(**snake_case							)
      __lowerCAmelCase			=				pipe.to(snake_case							)

      pipe.set_progress_bar_config(disable=snake_case							)

      __lowerCAmelCase			=				pipe(**self.get_dummy_inputs(snake_case							)							)
      __lowerCAmelCase			=				output.image_embeds

      __lowerCAmelCase			=				pipe(
          **self.get_dummy_inputs(snake_case							)			,						return_dict=snake_case			,						)[0]

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

      assert image.shape == (1, 32)

      __lowerCAmelCase			=				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 	A		(   self						:		Tuple							):

      """simple docstring"""





      __lowerCAmelCase			=				torch_device == '''cpu'''
      __lowerCAmelCase			=				True
      __lowerCAmelCase			=				False

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





    @skip_mps
    def 	A		(   self						:		List[str]							):

      """simple docstring"""





      __lowerCAmelCase			=				torch_device == '''cpu'''
      __lowerCAmelCase			=				False

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__lowercase													=			{
    """configuration_speech_to_text_2""": ["""SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2Text2Config"""],
    """processing_speech_to_text_2""": ["""Speech2Text2Processor"""],
    """tokenization_speech_to_text_2""": ["""Speech2Text2Tokenizer"""],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase													=			[
          """SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """Speech2Text2ForCausalLM""",
          """Speech2Text2PreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
      from .processing_speech_to_text_a import SpeechaTextaProcessor
      from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speech_to_text_a import (
                SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechaTextaForCausalLM,
                SpeechaTextaPreTrainedModel,
            )

else:
      import sys

      __lowercase													=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
