




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


UpperCamelCase__		=						logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCamelCase__		=						{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
}

UpperCamelCase__		=						{
    """RUCAIBox/mvp""": 1_0_2_4,
}






@lru_cache()
def  snake_case (							)							->							List[str]:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							       =							(
	    list(range(ord('!'      )						,       ord('~'      ) + 1      )      ) + list(range(ord('¡'      )						,       ord('¬'      ) + 1      )      ) + list(range(ord('®'      )						,       ord('ÿ'      ) + 1      )      )
	)
	lowerCamelCase_							:		Optional[int]							       =							bs[:]
	lowerCamelCase_							:		List[Any]							       =							0
	for b in range(2**8      ):
		if b not in bs:
			bs.append(A      )
			cs.append(2**8 + n      )
			n += 1
	lowerCamelCase_							:		str							       =							[chr(A      ) for n in cs]
	return dict(zip(A						,       A      )      )






def  snake_case (							A      )							->							Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_							:		str							       =							set()
	lowerCamelCase_							:		Union[str, Any]							       =							word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)      )
		lowerCamelCase_							:		Dict							       =							char
	return pairs



class 					__SCREAMING_SNAKE_CASE							(		_UpperCAmelCase  ):
					_lowerCamelCase		  =			VOCAB_FILES_NAMES
					_lowerCamelCase		  =			PRETRAINED_VOCAB_FILES_MAP
					_lowerCamelCase		  =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCamelCase		  =			['''input_ids''', '''attention_mask''']

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase="replace"			,	__lowercase="<s>"			,	__lowercase="</s>"			,	__lowercase="</s>"			,	__lowercase="<s>"			,	__lowercase="<unk>"			,	__lowercase="<pad>"			,	__lowercase="<mask>"			,	__lowercase=False			,	**__lowercase			,	):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							AddedToken(lowerCAmelCase__			,	lstrip=lowerCAmelCase__			,	rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__			,	lowerCAmelCase__ ) else bos_token
						lowerCamelCase_							:		int							       =							AddedToken(lowerCAmelCase__			,	lstrip=lowerCAmelCase__			,	rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__			,	lowerCAmelCase__ ) else eos_token
						lowerCamelCase_							:		int							       =							AddedToken(lowerCAmelCase__			,	lstrip=lowerCAmelCase__			,	rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__			,	lowerCAmelCase__ ) else sep_token
						lowerCamelCase_							:		List[Any]							       =							AddedToken(lowerCAmelCase__			,	lstrip=lowerCAmelCase__			,	rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__			,	lowerCAmelCase__ ) else cls_token
						lowerCamelCase_							:		Dict							       =							AddedToken(lowerCAmelCase__			,	lstrip=lowerCAmelCase__			,	rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__			,	lowerCAmelCase__ ) else unk_token
						lowerCamelCase_							:		str							       =							AddedToken(lowerCAmelCase__			,	lstrip=lowerCAmelCase__			,	rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__			,	lowerCAmelCase__ ) else pad_token

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

						super().__init__(
						    errors=lowerCAmelCase__			,	bos_token=lowerCAmelCase__			,	eos_token=lowerCAmelCase__			,	unk_token=lowerCAmelCase__			,	sep_token=lowerCAmelCase__			,	cls_token=lowerCAmelCase__			,	pad_token=lowerCAmelCase__			,	mask_token=lowerCAmelCase__			,	add_prefix_space=lowerCAmelCase__			,	**lowerCAmelCase__			,	)
						with open(lowerCAmelCase__			,	encoding='utf-8' ) as vocab_handle:
							lowerCamelCase_							:		List[str]							       =							json.load(lowerCAmelCase__ )
						lowerCamelCase_							:		Optional[int]							       =							{v: k for k, v in self.encoder.items()}
						lowerCamelCase_							:		List[str]							       =							errors  # how to handle errors in decoding
						lowerCamelCase_							:		Tuple							       =							bytes_to_unicode()
						lowerCamelCase_							:		Dict							       =							{v: k for k, v in self.byte_encoder.items()}
						with open(lowerCAmelCase__			,	encoding='utf-8' ) as merges_handle:
							lowerCamelCase_							:		int							       =							merges_handle.read().split('\n' )[1:-1]
						lowerCamelCase_							:		Any							       =							[tuple(merge.split() ) for merge in bpe_merges]
						lowerCamelCase_							:		Union[str, Any]							       =							dict(zip(lowerCAmelCase__			,	range(len(lowerCAmelCase__ ) ) ) )
						lowerCamelCase_							:		Dict							       =							{}
						lowerCamelCase_							:		Dict							       =							add_prefix_space

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

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return len(self.encoder )

					def      lowercase_							(    self ):







						'''simple docstring'''




						return dict(self.encoder			,	**self.added_tokens_encoder )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						if token in self.cache:
							return self.cache[token]
						lowerCamelCase_							:		int							       =							tuple(lowerCAmelCase__ )
						lowerCamelCase_							:		List[str]							       =							get_pairs(lowerCAmelCase__ )

						if not pairs:
							return token

						while True:
							lowerCamelCase_							:		Dict							       =							min(lowerCAmelCase__			,	key=lambda __lowercase : self.bpe_ranks.get(lowerCAmelCase__			,	float('inf' ) ) )
							if bigram not in self.bpe_ranks:
								break
							lowerCamelCase_       ,      lowerCamelCase_							:		str							       =							bigram
							lowerCamelCase_							:		Any							       =							[]
							lowerCamelCase_							:		Any							       =							0
							while i < len(lowerCAmelCase__ ):
								try:
									lowerCamelCase_							:		Any							       =							word.index(lowerCAmelCase__			,	lowerCAmelCase__ )
								except ValueError:
									new_word.extend(word[i:] )
									break
								else:
									new_word.extend(word[i:j] )
									lowerCamelCase_							:		Any							       =							j

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

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							[]
						for token in re.findall(self.pat			,	lowerCAmelCase__ ):
							lowerCamelCase_							:		Union[str, Any]							       =							''.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(lowerCAmelCase__ ).split(' ' ) )
						return bpe_tokens

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						return self.encoder.get(lowerCAmelCase__			,	self.encoder.get(self.unk_token ) )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						return self.decoder.get(lowerCAmelCase__ )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							''.join(lowerCAmelCase__ )
						lowerCamelCase_							:		Optional[int]							       =							bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'			,	errors=self.errors )
						return text

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						if not os.path.isdir(lowerCAmelCase__ ):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
							return
						lowerCamelCase_							:		str							       =							os.path.join(
						    lowerCAmelCase__			,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
						lowerCamelCase_							:		str							       =							os.path.join(
						    lowerCAmelCase__			,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

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

						lowerCamelCase_							:		str							       =							0
						with open(lowerCAmelCase__			,	'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 __lowercase : 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!' )
									lowerCamelCase_							:		Any							       =							token_index
								writer.write(' '.join(lowerCAmelCase__ ) + '\n' )
								index += 1

						return vocab_file, merge_file

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						lowerCamelCase_							:		Any							       =							[self.cls_token_id]
						lowerCamelCase_							:		Optional[Any]							       =							[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None			,	__lowercase = False ):







						'''simple docstring'''




						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowerCAmelCase__			,	token_ids_a=lowerCAmelCase__			,	already_has_special_tokens=lowerCAmelCase__ )

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

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							[self.sep_token_id]
						lowerCamelCase_							:		List[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      lowercase_							(    self			,	__lowercase			,	__lowercase=False			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							kwargs.pop('add_prefix_space'			,	self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__ ) > 0 and not text[0].isspace()):
							lowerCamelCase_							:		List[Any]							       =							' ' + text
						return (text, kwargs)






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     __SCREAMING_SNAKE_CASE							(		lowerCamelCase       ,					unittest.TestCase  ):
					_lowerCamelCase		  =			KandinskyVaaPriorEmbaEmbPipeline
					_lowerCamelCase		  =			["""prompt""", """image"""]
					_lowerCamelCase		  =			["""prompt""", """image"""]
					_lowerCamelCase		  =			[
					    """num_images_per_prompt""",
					    """strength""",
					    """generator""",
					    """num_inference_steps""",
					    """latents""",
					    """negative_prompt""",
					    """guidance_scale""",
					    """output_type""",
					    """return_dict""",
					]
					_lowerCamelCase		  =			False

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return 32

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return 32

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return self.time_input_dim

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return self.time_input_dim * 4

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return 100

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




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

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						torch.manual_seed(0 )
						lowerCamelCase_							:		Dict							       =							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(__lowercase )

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						torch.manual_seed(0 )

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

						lowerCamelCase_							:		str							       =							PriorTransformer(**__lowercase )
						# 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_							:		Dict							       =							nn.Parameter(torch.ones(model.clip_std.shape ) )
						return model

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						torch.manual_seed(0 )
						lowerCamelCase_							:		str							       =							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_							:		List[str]							       =							CLIPVisionModelWithProjection(__lowercase )
						return model

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							CLIPImageProcessor(
						    crop_size=224			,	do_center_crop=__lowercase			,	do_normalize=__lowercase			,	do_resize=__lowercase			,	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=224			,	)

						return image_processor

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							self.dummy_prior
						lowerCamelCase_							:		str							       =							self.dummy_image_encoder
						lowerCamelCase_							:		Tuple							       =							self.dummy_text_encoder
						lowerCamelCase_							:		str							       =							self.dummy_tokenizer
						lowerCamelCase_							:		int							       =							self.dummy_image_processor

						lowerCamelCase_							:		Dict							       =							UnCLIPScheduler(
						    variance_type='fixed_small_log'			,	prediction_type='sample'			,	num_train_timesteps=1_000			,	clip_sample=__lowercase			,	clip_sample_range=10.0			,	)

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

						return components

					def      lowercase_							(    self			,	__lowercase			,	__lowercase=0 ):







						'''simple docstring'''




						if str(__lowercase ).startswith('mps' ):
							lowerCamelCase_							:		Union[str, Any]							       =							torch.manual_seed(__lowercase )
						else:
							lowerCamelCase_							:		Optional[Any]							       =							torch.Generator(device=__lowercase ).manual_seed(__lowercase )

						lowerCamelCase_							:		Optional[int]							       =							floats_tensor((1, 3, 64, 64)			,	rng=random.Random(__lowercase ) ).to(__lowercase )
						lowerCamelCase_							:		Any							       =							image.cpu().permute(0			,	2			,	3			,	1 )[0]
						lowerCamelCase_							:		List[Any]							       =							Image.fromarray(np.uinta(__lowercase ) ).convert('RGB' ).resize((256, 256) )

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

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							'cpu'

						lowerCamelCase_							:		Union[str, Any]							       =							self.get_dummy_components()

						lowerCamelCase_							:		str							       =							self.pipeline_class(**__lowercase )
						lowerCamelCase_							:		Optional[Any]							       =							pipe.to(__lowercase )

						pipe.set_progress_bar_config(disable=__lowercase )

						lowerCamelCase_							:		List[str]							       =							pipe(**self.get_dummy_inputs(__lowercase ) )
						lowerCamelCase_							:		Union[str, Any]							       =							output.image_embeds

						lowerCamelCase_							:		List[str]							       =							pipe(
						    **self.get_dummy_inputs(__lowercase )			,	return_dict=__lowercase			,	)[0]

						lowerCamelCase_							:		List[Any]							       =							image[0, -10:]
						lowerCamelCase_							:		List[Any]							       =							image_from_tuple[0, -10:]

						assert image.shape == (1, 32)

						lowerCamelCase_							:		Any							       =							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      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							torch_device == 'cpu'
						lowerCamelCase_							:		Union[str, Any]							       =							True
						lowerCamelCase_							:		str							       =							False

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





					@skip_mps
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							torch_device == 'cpu'
						lowerCamelCase_							:		Optional[Any]							       =							False

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