





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


__lowerCAmelCase 	=      logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
__lowerCAmelCase 	=      {
    "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",
    },
}

__lowerCAmelCase 	=      {
    "RUCAIBox/mvp": 1_024,
}

@lru_cache()
def      a    (   )  ->Any:
		UpperCamelCase__								=						(
		    list(range(ord("""!"""							)			,    ord("""~"""							) + 1							)							) + list(range(ord("""¡"""							)			,    ord("""¬"""							) + 1							)							) + list(range(ord("""®"""							)			,    ord("""ÿ"""							) + 1							)							)
		)
		UpperCamelCase__								=						bs[:]
		UpperCamelCase__								=						0
		for b in range(2**8							):
				if b not in bs:
						bs.append(_lowercase							)
						cs.append(2**8 + n							)
						n += 1
		UpperCamelCase__								=						[chr(_lowercase							) for n in cs]
		return dict(zip(_lowercase			,    _lowercase							)							)

def      a    (   _lowercase :  int							)  ->List[Any]:
		UpperCamelCase__								=						set()
		UpperCamelCase__								=						word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)							)
				UpperCamelCase__								=						char
		return pairs




class 	_UpperCAmelCase						( _lowercase       ):
					__A        = VOCAB_FILES_NAMES
					__A        = PRETRAINED_VOCAB_FILES_MAP
					__A        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__A        = ["input_ids", "attention_mask"]




					def __init__(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase="replace"     ,   __lowerCAmelCase="<s>"     ,   __lowerCAmelCase="</s>"     ,   __lowerCAmelCase="</s>"     ,   __lowerCAmelCase="<s>"     ,   __lowerCAmelCase="<unk>"     ,   __lowerCAmelCase="<pad>"     ,   __lowerCAmelCase="<mask>"     ,   __lowerCAmelCase=False     ,   **__lowerCAmelCase     ,   )    ->	str:
							UpperCamelCase__								=						AddedToken(__lowerCAmelCase     ,   lstrip=__lowerCAmelCase     ,   rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							) else bos_token
							UpperCamelCase__								=						AddedToken(__lowerCAmelCase     ,   lstrip=__lowerCAmelCase     ,   rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							) else eos_token
							UpperCamelCase__								=						AddedToken(__lowerCAmelCase     ,   lstrip=__lowerCAmelCase     ,   rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							) else sep_token
							UpperCamelCase__								=						AddedToken(__lowerCAmelCase     ,   lstrip=__lowerCAmelCase     ,   rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							) else cls_token
							UpperCamelCase__								=						AddedToken(__lowerCAmelCase     ,   lstrip=__lowerCAmelCase     ,   rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							) else unk_token
							UpperCamelCase__								=						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
							UpperCamelCase__								=						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:
									UpperCamelCase__								=						json.load(__lowerCAmelCase							)
							UpperCamelCase__								=						{v: k for k, v in self.encoder.items()}
							UpperCamelCase__								=						errors  # how to handle errors in decoding
							UpperCamelCase__								=						bytes_to_unicode()
							UpperCamelCase__								=						{v: k for k, v in self.byte_encoder.items()}
							with open(__lowerCAmelCase     ,   encoding="""utf-8"""							) as merges_handle:
									UpperCamelCase__								=						merges_handle.read().split("""\n"""							)[1:-1]
							UpperCamelCase__								=						[tuple(merge.split()							) for merge in bpe_merges]
							UpperCamelCase__								=						dict(zip(__lowerCAmelCase     ,   range(len(__lowerCAmelCase							)							)							)							)
							UpperCamelCase__								=						{}
							UpperCamelCase__								=						add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							UpperCamelCase__								=						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							)    ->	Union[str, Any]:
							return len(self.encoder							)




					def 						snake_case_		(    self							)    ->	Any:
							return dict(self.encoder     ,   **self.added_tokens_encoder							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	List[str]:
							if token in self.cache:
									return self.cache[token]
							UpperCamelCase__								=						tuple(__lowerCAmelCase							)
							UpperCamelCase__								=						get_pairs(__lowerCAmelCase							)

							if not pairs:
									return token

							while True:
									UpperCamelCase__								=						min(__lowerCAmelCase     ,   key=lambda __lowerCAmelCase							: self.bpe_ranks.get(__lowerCAmelCase     ,   float("""inf"""							)							)							)
									if bigram not in self.bpe_ranks:
											break
									UpperCamelCase__	,      UpperCamelCase__								=						bigram
									UpperCamelCase__								=						[]
									UpperCamelCase__								=						0
									while i < len(__lowerCAmelCase							):
											try:
													UpperCamelCase__								=						word.index(__lowerCAmelCase     ,   __lowerCAmelCase							)
											except ValueError:
													new_word.extend(word[i:]							)
													break
											else:
													new_word.extend(word[i:j]							)
													UpperCamelCase__								=						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
									UpperCamelCase__								=						tuple(__lowerCAmelCase							)
									UpperCamelCase__								=						new_word
									if len(__lowerCAmelCase							) == 1:
											break
									else:
											UpperCamelCase__								=						get_pairs(__lowerCAmelCase							)
							UpperCamelCase__								=						""" """.join(__lowerCAmelCase							)
							UpperCamelCase__								=						word
							return word




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	int:
							UpperCamelCase__								=						[]
							for token in re.findall(self.pat     ,   __lowerCAmelCase							):
									UpperCamelCase__								=						"""""".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 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	Dict:
							return self.encoder.get(__lowerCAmelCase     ,   self.encoder.get(self.unk_token							)							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	Optional[Any]:
							return self.decoder.get(__lowerCAmelCase							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	Any:
							UpperCamelCase__								=						"""""".join(__lowerCAmelCase							)
							UpperCamelCase__								=						bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""     ,   errors=self.errors							)
							return text




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase = None							)    ->	Tuple[str]:
							if not os.path.isdir(__lowerCAmelCase							):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""							)
									return
							UpperCamelCase__								=						os.path.join(
							    __lowerCAmelCase     ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
							UpperCamelCase__								=						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"""							)

							UpperCamelCase__								=						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 __lowerCAmelCase							: 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!"""							)
													UpperCamelCase__								=						token_index
											writer.write(""" """.join(__lowerCAmelCase							) + """\n"""							)
											index += 1

							return vocab_file, merge_file




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase = None							)    ->	List[int]:
							if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCamelCase__								=						[self.cls_token_id]
							UpperCamelCase__								=						[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase = None     ,   __lowerCAmelCase = False							)    ->	List[int]:
							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 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase = None							)    ->	List[int]:
							UpperCamelCase__								=						[self.sep_token_id]
							UpperCamelCase__								=						[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     ,   __lowerCAmelCase     ,   __lowerCAmelCase=False     ,   **__lowerCAmelCase							)    ->	Optional[Any]:
							UpperCamelCase__								=						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()):
									UpperCamelCase__								=						""" """ + text
							return (text, kwargs)












import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 	_UpperCAmelCase						( _lowercase			,							unittest.TestCase       ):
					__A        = KandinskyPipeline
					__A        = [
					    "prompt",
					    "image_embeds",
					    "negative_image_embeds",
					]
					__A        = ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
					__A        = [
					    "generator",
					    "height",
					    "width",
					    "latents",
					    "guidance_scale",
					    "negative_prompt",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					__A        = False




					@property
					def 						snake_case_		(    self							)    ->	Any:
							return 32




					@property
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							return 32




					@property
					def 						snake_case_		(    self							)    ->	List[Any]:
							return self.time_input_dim




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							return self.time_input_dim * 4




					@property
					def 						snake_case_		(    self							)    ->	Dict:
							return 100




					@property
					def 						snake_case_		(    self							)    ->	List[str]:
							UpperCamelCase__								=						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""							)
							return tokenizer




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							torch.manual_seed(0							)
							UpperCamelCase__								=						MCLIPConfig(
							    numDims=self.cross_attention_dim     ,   transformerDimensions=self.text_embedder_hidden_size     ,   hidden_size=self.text_embedder_hidden_size     ,   intermediate_size=37     ,   num_attention_heads=4     ,   num_hidden_layers=5     ,   vocab_size=1_005     ,   )

							UpperCamelCase__								=						MultilingualCLIP(__lowerCAmelCase							)
							UpperCamelCase__								=						text_encoder.eval()

							return text_encoder




					@property
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							torch.manual_seed(0							)

							UpperCamelCase__								=						{
							    """in_channels""": 4,
							    # Out channels is double in channels because predicts mean and variance
							    """out_channels""": 8,
							    """addition_embed_type""": """text_image""",
							    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
							    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
							    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
							    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
							    """layers_per_block""": 1,
							    """encoder_hid_dim""": self.text_embedder_hidden_size,
							    """encoder_hid_dim_type""": """text_image_proj""",
							    """cross_attention_dim""": self.cross_attention_dim,
							    """attention_head_dim""": 4,
							    """resnet_time_scale_shift""": """scale_shift""",
							    """class_embed_type""": None,
							}

							UpperCamelCase__								=						UNetaDConditionModel(**__lowerCAmelCase							)
							return model




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							return {
							    "block_out_channels": [32, 64],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}




					@property
					def 						snake_case_		(    self							)    ->	Optional[int]:
							torch.manual_seed(0							)
							UpperCamelCase__								=						VQModel(**self.dummy_movq_kwargs							)
							return model




					def 						snake_case_		(    self							)    ->	Dict:
							UpperCamelCase__								=						self.dummy_text_encoder
							UpperCamelCase__								=						self.dummy_tokenizer
							UpperCamelCase__								=						self.dummy_unet
							UpperCamelCase__								=						self.dummy_movq

							UpperCamelCase__								=						DDIMScheduler(
							    num_train_timesteps=1_000     ,   beta_schedule="""linear"""     ,   beta_start=0.0_0_0_8_5     ,   beta_end=0.0_1_2     ,   clip_sample=__lowerCAmelCase     ,   set_alpha_to_one=__lowerCAmelCase     ,   steps_offset=1     ,   prediction_type="""epsilon"""     ,   thresholding=__lowerCAmelCase     ,   )

							UpperCamelCase__								=						{
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}
							return components




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=0							)    ->	str:
							UpperCamelCase__								=						floats_tensor((1, self.cross_attention_dim)     ,   rng=random.Random(__lowerCAmelCase							)							).to(__lowerCAmelCase							)
							UpperCamelCase__								=						floats_tensor((1, self.cross_attention_dim)     ,   rng=random.Random(seed + 1							)							).to(__lowerCAmelCase							)
							if str(__lowerCAmelCase							).startswith("""mps"""							):
									UpperCamelCase__								=						torch.manual_seed(__lowerCAmelCase							)
							else:
									UpperCamelCase__								=						torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)
							UpperCamelCase__								=						{
							    """prompt""": """horse""",
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """guidance_scale""": 4.0,
							    """num_inference_steps""": 2,
							    """output_type""": """np""",
							}
							return inputs




					def 						snake_case_		(    self							)    ->	Tuple:
							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.images

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

							UpperCamelCase__								=						image[0, -3:, -3:, -1]
							UpperCamelCase__								=						image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							UpperCamelCase__								=						np.array(
							    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]							)

							assert (
							    np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
							), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
							), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




@slow
@require_torch_gpu
class 	_UpperCAmelCase						( unittest.TestCase       ):




					def 						snake_case_		(    self							)    ->	str:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""							)

							UpperCamelCase__								=						KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""     ,   torch_dtype=torch.floataa							)
							pipe_prior.to(__lowerCAmelCase							)

							UpperCamelCase__								=						KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""     ,   torch_dtype=torch.floataa							)
							UpperCamelCase__								=						pipeline.to(__lowerCAmelCase							)
							pipeline.set_progress_bar_config(disable=__lowerCAmelCase							)

							UpperCamelCase__								=						"""red cat, 4k photo"""

							UpperCamelCase__								=						torch.Generator(device="""cuda"""							).manual_seed(0							)
							UpperCamelCase__	,      UpperCamelCase__								=						pipe_prior(
							    __lowerCAmelCase     ,   generator=__lowerCAmelCase     ,   num_inference_steps=5     ,   negative_prompt=""""""     ,   ).to_tuple()

							UpperCamelCase__								=						torch.Generator(device="""cuda"""							).manual_seed(0							)
							UpperCamelCase__								=						pipeline(
							    __lowerCAmelCase     ,   image_embeds=__lowerCAmelCase     ,   negative_image_embeds=__lowerCAmelCase     ,   generator=__lowerCAmelCase     ,   num_inference_steps=100     ,   output_type="""np"""     ,   )

							UpperCamelCase__								=						output.images[0]

							assert image.shape == (512, 512, 3)

							assert_mean_pixel_difference(__lowerCAmelCase     ,   __lowerCAmelCase							)





