


'''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 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       Dict            =       KandinskyPriorPipeline
 _lowerCamelCase			:       Dict            =       ['prompt']
 _lowerCamelCase			:       List[str]            =       ['prompt', 'negative_prompt']
 _lowerCamelCase			:       Tuple            =       [
     'num_images_per_prompt',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 _lowerCamelCase			:       Tuple            =       False

 @property
 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    return 32

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





    """simple docstring"""



    return 32

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    return self.time_input_dim

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





    """simple docstring"""



    return self.time_input_dim * 4

 @property
 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    return 100

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



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

 @property
 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    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-0_5,       num_attention_heads=4,       num_hidden_layers=5,       pad_token_id=1,       vocab_size=1000,       )
    return CLIPTextModelWithProjection(a_       )

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





    """simple docstring"""



    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(**a_       )
    # 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 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    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,       )

    UpperCamelCase__			    =    CLIPVisionModelWithProjection(a_       )
    return model

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





    """simple docstring"""



    UpperCamelCase__			    =    CLIPImageProcessor(
        crop_size=224,       do_center_crop=a_,       do_normalize=a_,       do_resize=a_,       image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073],       image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711],       resample=3,       size=224,       )

    return image_processor

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    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=1000,       clip_sample=a_,       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 		lowercase_    (			self			:						Optional[Any],       a_			:						List[Any],       a_			:						List[str]=0       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cpu"

    UpperCamelCase__			    =    self.get_dummy_components()

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

    pipe.set_progress_bar_config(disable=a_       )

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

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

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

    assert image.shape == (1, 32)

    UpperCamelCase__			    =    np.array(
        [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]       )

    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 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch_device == "cpu"
    UpperCamelCase__			    =    True
    UpperCamelCase__			    =    False

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

 @skip_mps
 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch_device == "cpu"
    UpperCamelCase__			    =    False

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



import unittest

from cer import CER


__lowerCAmelCase									=				CER()





class     __a     (       unittest.TestCase ):
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  ['White House']
        lowercase__:  Optional[int]         =  ['white house']
        # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
        lowercase__:  str         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_8_1_8_1_8_1_8_1_8					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  ['were wolf']
        lowercase__:  Union[str, Any]         =  ['werewolf']
        # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
        lowercase__:  Tuple         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_1_1_1_1_1_1					) < 1E-6					)

        lowercase__:  Dict         =  ['werewolf']
        lowercase__:  Optional[Any]         =  ['weae     wolf']
        # S = 1, D = 1, I = 0, N = 8, CER = 0.25
        lowercase__:  Optional[int]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.2_5					) < 1E-6					)

        # consecutive whitespaces case 1
        lowercase__:  Union[str, Any]         =  ['were wolf']
        lowercase__:  int         =  ['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        lowercase__:  int         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.0					) < 1E-6					)

        # consecutive whitespaces case 2
        lowercase__:  List[str]         =  ['were   wolf']
        lowercase__:  Tuple         =  ['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        lowercase__:  Dict         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.0					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  Optional[int]         =  ['werewolf']
        lowercase__:  Dict         =  ['weaewolf']
        # S = 1, D = 0, I = 0, N = 8, CER = 0.125
        lowercase__:  int         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_2_5					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  int         =  ['werewolf']
        lowercase__:  Optional[int]         =  ['wereawolf']
        # S = 0, D = 1, I = 0, N = 8, CER = 0.125
        lowercase__:  Dict         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_2_5					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  ['werewolf']
        lowercase__:  List[str]         =  ['wereolf']
        # S = 0, D = 0, I = 1, N = 8, CER = 0.125
        lowercase__:  Optional[Any]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_2_5					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  List[str]         =  ['werewolf']
        lowercase__:  List[Any]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertEqual(lowerCAmelCase__							,					0.0					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  int         =  ['werewolf', 'I am your father']
        lowercase__:  Union[str, Any]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertEqual(lowerCAmelCase__							,					0.0					)

        lowercase__:  List[str]         =  ['werewolf', 'I am your father', 'doge']
        lowercase__:  Any         =  ['werxwolf', 'I       am your father', 'doge']
        # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
        lowercase__:  int         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.0_3_5_7_1_4_2_8					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  List[Any]         =  ['My hovercraft', 'is full of eels']
        lowercase__:  Union[str, Any]         =  ['My hovercraft is full', ' of eels']
        # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
        # whitespace at the front of " of eels" will be strip during preporcessing
        # so need to insert 2 whitespaces
        lowercase__:  Optional[int]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__							,					concatenate_texts=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.0_7_1_4_2_8					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        lowercase__:  Dict         =  ['我能吞下玻璃而不伤身体']
        lowercase__:  Tuple         =  [' 能吞虾玻璃而 不霜身体啦']
        # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
        lowercase__:  str         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5					) < 1E-6					)

        lowercase__:  Any         =  ['我能吞下玻璃', '而不伤身体']
        lowercase__:  Union[str, Any]         =  ['我    能 吞 下 玻 璃', '而不伤身体']
        # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
        lowercase__:  Optional[Any]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5_4_5					) < 1E-6					)

        lowercase__:  int         =  ['我能吞下玻璃而不伤身体']
        lowercase__:  Any         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertFalse(lowerCAmelCase__							,					0.0					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  Dict         =  ['']
        lowercase__:  int         =  ['Hypothesis']
        with self.assertRaises(lowerCAmelCase__					):
               cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)


if __name__ == "__main__":
 unittest.main()


