
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 	__A(				a__				,      unittest.TestCase ):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__			    =			KandinskyVaaPriorPipeline
  SCREAMING_SNAKE_CASE__			    =			["""prompt"""]
  SCREAMING_SNAKE_CASE__			    =			["""prompt""", """negative_prompt"""]
  SCREAMING_SNAKE_CASE__			    =			[
      """num_images_per_prompt""",
      """generator""",
      """num_inference_steps""",
      """latents""",
      """negative_prompt""",
      """guidance_scale""",
      """output_type""",
      """return_dict""",
  ]
  SCREAMING_SNAKE_CASE__			    =			False







  @property
  def        UpperCAmelCase_					(self						):
       return 32







  @property
  def        UpperCAmelCase_					(self						):
       return 32







  @property
  def        UpperCAmelCase_					(self						):
       return self.time_input_dim







  @property
  def        UpperCAmelCase_					(self						):
       return self.time_input_dim * 4







  @property
  def        UpperCAmelCase_					(self						):
       return 1_00







  @property
  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)
       return tokenizer







  @property
  def        UpperCAmelCase_					(self						):
       torch.manual_seed(0						)
       UpperCamelCase__				=				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=10_00       ,			)
       return CLIPTextModelWithProjection(__lowerCAmelCase						)







  @property
  def        UpperCAmelCase_					(self						):
       torch.manual_seed(0						)

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

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







  @property
  def        UpperCAmelCase_					(self						):
       torch.manual_seed(0						)
       UpperCamelCase__				=				CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size       ,			image_size=2_24       ,			projection_dim=self.text_embedder_hidden_size       ,			intermediate_size=37       ,			num_attention_heads=4       ,			num_channels=3       ,			num_hidden_layers=5       ,			patch_size=14       ,			)

       UpperCamelCase__				=				CLIPVisionModelWithProjection(__lowerCAmelCase						)
       return model







  @property
  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				CLIPImageProcessor(
           crop_size=2_24       ,			do_center_crop=__lowerCAmelCase       ,			do_normalize=__lowerCAmelCase       ,			do_resize=__lowerCAmelCase       ,			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=2_24       ,			)

       return image_processor







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.dummy_prior
       UpperCamelCase__				=				self.dummy_image_encoder
       UpperCamelCase__				=				self.dummy_text_encoder
       UpperCamelCase__				=				self.dummy_tokenizer
       UpperCamelCase__				=				self.dummy_image_processor

       UpperCamelCase__				=				UnCLIPScheduler(
           variance_type="""fixed_small_log"""       ,			prediction_type="""sample"""       ,			num_train_timesteps=10_00       ,			clip_sample=__lowerCAmelCase       ,			clip_sample_range=10.0       ,			)

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

       return components







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_=0						):
       if str(__lowerCAmelCase						).startswith("""mps"""						):
            UpperCamelCase__				=				torch.manual_seed(__lowerCAmelCase						)
       else:
            UpperCamelCase__				=				torch.Generator(device=__lowerCAmelCase						).manual_seed(__lowerCAmelCase						)
       UpperCamelCase__				=				{
           '''prompt''': '''horse''',
           '''generator''': generator,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				'''cpu'''

       UpperCamelCase__				=				self.get_dummy_components()

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

       pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

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

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

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

       assert image.shape == (1, 32)

       UpperCamelCase__				=				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						):
       UpperCamelCase__				=				torch_device == '''cpu'''
       UpperCamelCase__				=				True
       UpperCamelCase__				=				False

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







  @skip_mps
  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				torch_device == '''cpu'''
       UpperCamelCase__				=				False

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






'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import tensorflow as tf

  from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       @slow
       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[str]  =					TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' )
        lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''google/mt5-small''' )

        lowercase__				:    Any  =					tokenizer('''Hello there'''	,   return_tensors='''tf''' ).input_ids
        lowercase__				:    List[Any]  =					tokenizer('''Hi I am'''	,   return_tensors='''tf''' ).input_ids

        lowercase__				:    str  =					model(__lowerCAmelCase	,   labels=__lowerCAmelCase ).loss
        lowercase__				:    List[str]  =					-tf.math.reduce_mean(__lowerCAmelCase ).numpy()

        lowercase__				:    str  =					-2_1.2_2_8_1_6_8
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2E-4 )

