






import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 	a_(		lowercase__			,							unittest.TestCase	):



     """simple docstring"""



     __snake_case		:				List[Any]  					=KandinskyVaaPriorEmbaEmbPipeline
     __snake_case		:				Any  					=['''prompt''', '''image''']
     __snake_case		:				List[str]  					=['''prompt''', '''image''']
     __snake_case		:				Dict  					=[
         '''num_images_per_prompt''',
         '''strength''',
         '''generator''',
         '''num_inference_steps''',
         '''latents''',
         '''negative_prompt''',
         '''guidance_scale''',
         '''output_type''',
         '''return_dict''',
     ]
     __snake_case		:				List[Any]  					=False





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

      """simple docstring"""
      return 3_2





     @property
     def     __UpperCamelCase						(		self					:	int)							->	Optional[int]:

      """simple docstring"""
      return 3_2





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

      """simple docstring"""
      return self.time_input_dim





     @property
     def     __UpperCamelCase						(		self					:	Optional[Any])							->	str:

      """simple docstring"""
      return self.time_input_dim * 4





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

      """simple docstring"""
      return 1_0_0





     @property
     def     __UpperCamelCase						(		self					:	str)							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
      return tokenizer





     @property
     def     __UpperCamelCase						(		self					:	int)							->	Union[str, Any]:

      """simple docstring"""
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE				    =		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=3_7  ,  layer_norm_eps=1e-05  ,  num_attention_heads=4  ,  num_hidden_layers=5  ,  pad_token_id=1  ,  vocab_size=1_0_0_0  ,  )
      return CLIPTextModelWithProjection(lowerCAmelCase__)





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

      """simple docstring"""
      torch.manual_seed(0)

      SCREAMING_SNAKE_CASE				    =		{
          'num_attention_heads': 2,
          'attention_head_dim': 1_2,
          'embedding_dim': self.text_embedder_hidden_size,
          'num_layers': 1,
      }

      SCREAMING_SNAKE_CASE				    =		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
      SCREAMING_SNAKE_CASE				    =		nn.Parameter(torch.ones(model.clip_std.shape))
      return model





     @property
     def     __UpperCamelCase						(		self					:	int)							->	Tuple:

      """simple docstring"""
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE				    =		CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size  ,  image_size=2_2_4  ,  projection_dim=self.text_embedder_hidden_size  ,  intermediate_size=3_7  ,  num_attention_heads=4  ,  num_channels=3  ,  num_hidden_layers=5  ,  patch_size=1_4  ,  )

      SCREAMING_SNAKE_CASE				    =		CLIPVisionModelWithProjection(lowerCAmelCase__)
      return model





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		CLIPImageProcessor(
          crop_size=2_2_4  ,  do_center_crop=lowerCAmelCase__  ,  do_normalize=lowerCAmelCase__  ,  do_resize=lowerCAmelCase__  ,  image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]  ,  image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]  ,  resample=3  ,  size=2_2_4  ,  )

      return image_processor





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.dummy_prior
      SCREAMING_SNAKE_CASE				    =		self.dummy_image_encoder
      SCREAMING_SNAKE_CASE				    =		self.dummy_text_encoder
      SCREAMING_SNAKE_CASE				    =		self.dummy_tokenizer
      SCREAMING_SNAKE_CASE				    =		self.dummy_image_processor

      SCREAMING_SNAKE_CASE				    =		UnCLIPScheduler(
          variance_type='fixed_small_log'  ,  prediction_type='sample'  ,  num_train_timesteps=1_0_0_0  ,  clip_sample=lowerCAmelCase__  ,  clip_sample_range=10.0  ,  )

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

      return components





     def     __UpperCamelCase						(		self					:	List[Any]  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	Any=0)							->	int:

      """simple docstring"""
      if str(lowerCAmelCase__).startswith('mps'):
       SCREAMING_SNAKE_CASE				    =		torch.manual_seed(lowerCAmelCase__)
      else:
       SCREAMING_SNAKE_CASE				    =		torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		floats_tensor((1, 3, 6_4, 6_4)  ,  rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		image.cpu().permute(0  ,  2  ,  3  ,  1)[0]
      SCREAMING_SNAKE_CASE				    =		Image.fromarray(np.uinta(lowerCAmelCase__)).convert('RGB').resize((2_5_6, 2_5_6))

      SCREAMING_SNAKE_CASE				    =		{
          'prompt': 'horse',
          'image': init_image,
          'strength': 0.5,
          'generator': generator,
          'guidance_scale': 4.0,
          'num_inference_steps': 2,
          'output_type': 'np',
      }
      return inputs





     def     __UpperCamelCase						(		self					:	Any)							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		'cpu'

      SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()

      SCREAMING_SNAKE_CASE				    =		self.pipeline_class(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		pipe.to(lowerCAmelCase__)

      pipe.set_progress_bar_config(disable=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		pipe(**self.get_dummy_inputs(lowerCAmelCase__))
      SCREAMING_SNAKE_CASE				    =		output.image_embeds

      SCREAMING_SNAKE_CASE				    =		pipe(
          **self.get_dummy_inputs(lowerCAmelCase__)  ,  return_dict=lowerCAmelCase__  ,  )[0]

      SCREAMING_SNAKE_CASE				    =		image[0, -1_0:]
      SCREAMING_SNAKE_CASE				    =		image_from_tuple[0, -1_0:]

      assert image.shape == (1, 3_2)

      SCREAMING_SNAKE_CASE				    =		np.array(
          [
              0.1_07_12_84,
              1.3_33_02_71,
              0.61_26_02_23,
              -0.6_69_10_65,
              -0.3_84_68_52,
              -1.0_30_36_61,
              0.22_71_61_11,
              0.03_34_89_01,
              0.30_04_06_75,
              -0.24_80_50_29,
          ])

      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					:	Union[str, Any])							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		torch_device == 'cpu'
      SCREAMING_SNAKE_CASE				    =		True
      SCREAMING_SNAKE_CASE				    =		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					:	Union[str, Any])							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		torch_device == 'cpu'
      SCREAMING_SNAKE_CASE				    =		False

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











'''simple docstring'''






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case_     			=						logging.get_logger(__name__)

snake_case_     			=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

snake_case_     			=						{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
}

snake_case_     			=						{
    """roberta-base""": 5_12,
    """roberta-large""": 5_12,
    """roberta-large-mnli""": 5_12,
    """distilroberta-base""": 5_12,
    """roberta-base-openai-detector""": 5_12,
    """roberta-large-openai-detector""": 5_12,
}
@lru_cache()
def 				_lowerCamelCase(	)		->	List[Any]:
    A							:							Tuple				=    (
        list(range(ord('''!'''		)     ,							ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)     ,							ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)     ,							ord('''ÿ'''		) + 1		)		)
    )
    A							:							List[Any]				=    bs[:]
    A							:							Tuple				=    0
    for b in range(2**8		):
        if b not in bs:
            bs.append(UpperCamelCase__		)
            cs.append(2**8 + n		)
            n += 1
    A							:							Optional[int]				=    [chr(UpperCamelCase__		) for n in cs]
    return dict(zip(UpperCamelCase__     ,							UpperCamelCase__		)		)
def 				_lowerCamelCase(	UpperCamelCase__     :			int		)		->	str:
    A							:							int				=    set()
    A							:							Any				=    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        A							:							Tuple				=    char
    return pairs





class 	_lowercase    (				a					):
   _UpperCamelCase									= VOCAB_FILES_NAMES
   _UpperCamelCase									= PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase									= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase									= ["""input_ids""", """attention_mask"""]


   def __init__(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase="replace"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="<unk>"     , _UpperCAmelCase="<pad>"     , _UpperCAmelCase="<mask>"     , _UpperCAmelCase=False     , **_UpperCAmelCase     , ):
       A							:							Any				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else bos_token
       A							:							Optional[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else eos_token
       A							:							Any				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else sep_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else cls_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else unk_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       A							:							Optional[int]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else mask_token

       super().__init__(
           errors=_UpperCAmelCase     , bos_token=_UpperCAmelCase     , eos_token=_UpperCAmelCase     , unk_token=_UpperCAmelCase     , sep_token=_UpperCAmelCase     , cls_token=_UpperCAmelCase     , pad_token=_UpperCAmelCase     , mask_token=_UpperCAmelCase     , add_prefix_space=_UpperCAmelCase     , **_UpperCAmelCase     , )

       with open(_UpperCAmelCase     , encoding='''utf-8'''       ) as vocab_handle:
           A							:							int				=    json.load(_UpperCAmelCase       )
       A							:							Union[str, Any]				=    {v: k for k, v in self.encoder.items()}
       A							:							Tuple				=    errors  # how to handle errors in decoding
       A							:							List[Any]				=    bytes_to_unicode()
       A							:							List[Any]				=    {v: k for k, v in self.byte_encoder.items()}
       with open(_UpperCAmelCase     , encoding='''utf-8'''       ) as merges_handle:
           A							:							Optional[int]				=    merges_handle.read().split('''\n'''       )[1:-1]
       A							:							int				=    [tuple(merge.split()       ) for merge in bpe_merges]
       A							:							Union[str, Any]				=    dict(zip(_UpperCAmelCase     , range(len(_UpperCAmelCase       )       )       )       )
       A							:							List[str]				=    {}
       A							:							List[Any]				=    add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       A							:							Any				=    re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''       )


   @property
   def        snake_case					(			self       ):
       return len(self.encoder       )


   def        snake_case					(			self       ):
       return dict(self.encoder     , **self.added_tokens_encoder       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       if token in self.cache:
           return self.cache[token]
       A							:							Union[str, Any]				=    tuple(_UpperCAmelCase       )
       A							:							Tuple				=    get_pairs(_UpperCAmelCase       )

       if not pairs:
           return token

       while True:
           A							:							Any				=    min(_UpperCAmelCase     , key=lambda _UpperCAmelCase       : self.bpe_ranks.get(_UpperCAmelCase     , float('''inf'''       )       )       )
           if bigram not in self.bpe_ranks:
               break
           A,						A							:							Optional[int]				=    bigram
           A							:							str				=    []
           A							:							List[Any]				=    0
           while i < len(_UpperCAmelCase       ):
               try:
                   A							:							Any				=    word.index(_UpperCAmelCase     , _UpperCAmelCase       )
               except ValueError:
                   new_word.extend(word[i:]       )
                   break
               else:
                   new_word.extend(word[i:j]       )
                   A							:							Tuple				=    j

               if word[i] == first and i < len(_UpperCAmelCase       ) - 1 and word[i + 1] == second:
                   new_word.append(first + second       )
                   i += 2
               else:
                   new_word.append(word[i]       )
                   i += 1
           A							:							Optional[Any]				=    tuple(_UpperCAmelCase       )
           A							:							Dict				=    new_word
           if len(_UpperCAmelCase       ) == 1:
               break
           else:
               A							:							int				=    get_pairs(_UpperCAmelCase       )
       A							:							Union[str, Any]				=    ''' '''.join(_UpperCAmelCase       )
       A							:							Tuple				=    word
       return word


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							Tuple				=    []
       for token in re.findall(self.pat     , _UpperCAmelCase       ):
           A							:							Tuple				=    ''''''.join(
               self.byte_encoder[b] for b in token.encode('''utf-8'''       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_UpperCAmelCase       ).split(''' '''       )       )
       return bpe_tokens


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.encoder.get(_UpperCAmelCase     , self.encoder.get(self.unk_token       )       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.decoder.get(_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							Any				=    ''''''.join(_UpperCAmelCase       )
       A							:							List[Any]				=    bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8'''     , errors=self.errors       )
       return text


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if not os.path.isdir(_UpperCAmelCase       ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
           return
       A							:							Any				=    os.path.join(
           _UpperCAmelCase     , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
       A							:							Any				=    os.path.join(
           _UpperCAmelCase     , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

       with open(_UpperCAmelCase     , '''w'''     , encoding='''utf-8'''       ) as f:
           f.write(json.dumps(self.encoder     , indent=2     , sort_keys=_UpperCAmelCase     , ensure_ascii=_UpperCAmelCase       ) + '''\n'''       )

       A							:							Tuple				=    0
       with open(_UpperCAmelCase     , '''w'''     , encoding='''utf-8'''       ) as writer:
           writer.write('''#version: 0.2\n'''       )
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     , key=lambda _UpperCAmelCase       : kv[1]       ):
               if index != token_index:
                   logger.warning(
                       f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                       ''' Please check that the tokenizer is not corrupted!'''       )
                   A							:							List[Any]				=    token_index
               writer.write(''' '''.join(_UpperCAmelCase       ) + '''\n'''       )
               index += 1

       return vocab_file, merge_file


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       A							:							Union[str, Any]				=    [self.cls_token_id]
       A							:							Dict				=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None     , _UpperCAmelCase = False       ):
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_UpperCAmelCase     , token_ids_a=_UpperCAmelCase     , already_has_special_tokens=_UpperCAmelCase       )

       if token_ids_a is None:
           return [1] + ([0] * len(_UpperCAmelCase       )) + [1]
       return [1] + ([0] * len(_UpperCAmelCase       )) + [1, 1] + ([0] * len(_UpperCAmelCase       )) + [1]


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       A							:							Dict				=    [self.sep_token_id]
       A							:							Union[str, Any]				=    [self.cls_token_id]

       if token_ids_a is None:
           return len(cls + token_ids_a + sep       ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=False     , **_UpperCAmelCase       ):
       A							:							str				=    kwargs.pop('''add_prefix_space'''     , self.add_prefix_space       )
       if (is_split_into_words or add_prefix_space) and (len(_UpperCAmelCase       ) > 0 and not text[0].isspace()):
           A							:							List[Any]				=    ''' ''' + text
       return (text, kwargs)
