


import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A   :					str	=      logging.get_logger(__name__)

A   :					Union[str, Any]	=      {
    "vocab_file": "vocab.txt",
    "merges_file": "bpe.codes",
}

A   :					Dict	=      {
    "vocab_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
    },
    "merges_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
    },
}

A   :					int	=      {
    "vinai/phobert-base": 256,
    "vinai/phobert-large": 256,
}

def    lowercase_	(	_A    :					str		):
      lowerCamelCase__	:       List[str]					=						set()
      lowerCamelCase__	:       Optional[Any]					=						word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)		)
            lowerCamelCase__	:       List[Any]					=						char

      lowerCamelCase__	:       str					=						set(_lowerCAmelCase		)
      return pairs







class     _lowercase				(							__UpperCAmelCase):


       """simple docstring"""




       A__      		=    VOCAB_FILES_NAMES
       A__      		=    PRETRAINED_VOCAB_FILES_MAP
       A__      		=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





       def __init__(     self							:    Tuple      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Optional[Any]="<s>"      ,   __lowerCamelCase							:    Any="</s>"      ,   __lowerCamelCase							:    str="</s>"      ,   __lowerCamelCase							:    int="<s>"      ,   __lowerCamelCase							:    int="<unk>"      ,   __lowerCamelCase							:    Optional[Any]="<pad>"      ,   __lowerCamelCase							:    List[str]="<mask>"      ,   **__lowerCamelCase							:    int      ,   ):


             '''simple docstring'''



             super().__init__(
                 bos_token=_lowerCamelCase      ,   eos_token=_lowerCamelCase      ,   unk_token=_lowerCamelCase      ,   sep_token=_lowerCamelCase      ,   cls_token=_lowerCamelCase      ,   pad_token=_lowerCamelCase      ,   mask_token=_lowerCamelCase      ,   **_lowerCamelCase      ,   )

             lowerCamelCase__	:       List[Any]					=						vocab_file
             lowerCamelCase__	:       List[str]					=						merges_file

             lowerCamelCase__	:       int					=						{}
             lowerCamelCase__	:       Optional[int]					=						0
             lowerCamelCase__	:       List[str]					=						1
             lowerCamelCase__	:       Any					=						2
             lowerCamelCase__	:       str					=						3

             self.add_from_file(_lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						{v: k for k, v in self.encoder.items()}

             with open(_lowerCamelCase      ,   encoding="utf-8" ) as merges_handle:
                   lowerCamelCase__	:       Union[str, Any]					=						merges_handle.read().split("\n" )[:-1]
             lowerCamelCase__	:       Dict					=						[tuple(merge.split()[:-1] ) for merge in merges]
             lowerCamelCase__	:       List[str]					=						dict(zip(_lowerCamelCase      ,   range(len(_lowerCamelCase ) ) ) )
             lowerCamelCase__	:       List[str]					=						{}





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    List[int]      ,   __lowerCamelCase							:    Optional[List[int]] = 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__	:       List[Any]					=						[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    List[int]      ,   __lowerCamelCase							:    Optional[List[int]] = None      ,   __lowerCamelCase							:    bool = 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 		lowerCAmelCase  (     self							:    List[Any]      ,   __lowerCamelCase							:    List[int]      ,   __lowerCamelCase							:    Optional[List[int]] = None ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						[self.sep_token_id]
             lowerCamelCase__	:       str					=						[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]





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


             '''simple docstring'''



             return len(self.encoder )





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    Any ):


             '''simple docstring'''



             if token in self.cache:
                   return self.cache[token]
             lowerCamelCase__	:       int					=						tuple(_lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
             lowerCamelCase__	:       List[Any]					=						get_pairs(_lowerCamelCase )

             if not pairs:
                   return token

             while True:
                   lowerCamelCase__	:       Union[str, Any]					=						min(_lowerCamelCase      ,   key=lambda __lowerCamelCase : self.bpe_ranks.get(_lowerCamelCase      ,   float("inf" ) ) )
                   if bigram not in self.bpe_ranks:
                         break
                   lowerCamelCase__					,     lowerCamelCase__	:       Union[str, Any]					=						bigram
                   lowerCamelCase__	:       int					=						[]
                   lowerCamelCase__	:       List[str]					=						0
                   while i < len(_lowerCamelCase ):
                         try:
                               lowerCamelCase__	:       Tuple					=						word.index(_lowerCamelCase      ,   _lowerCamelCase )
                         except ValueError:
                               new_word.extend(word[i:] )
                               break
                         else:
                               new_word.extend(word[i:j] )
                               lowerCamelCase__	:       str					=						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__	:       Tuple					=						tuple(_lowerCamelCase )
                   lowerCamelCase__	:       Optional[int]					=						new_word
                   if len(_lowerCamelCase ) == 1:
                         break
                   else:
                         lowerCamelCase__	:       List[Any]					=						get_pairs(_lowerCamelCase )
             lowerCamelCase__	:       Dict					=						"@@ ".join(_lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						word[:-4]
             lowerCamelCase__	:       Dict					=						word
             return word





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						[]

             lowerCamelCase__	:       Optional[Any]					=						re.findall(R"\S+\n?"      ,   _lowerCamelCase )

             for token in words:
                   split_tokens.extend(list(self.bpe(_lowerCamelCase ).split(" " ) ) )
             return split_tokens





       def 		lowerCAmelCase  (     self							:    List[Any]      ,   __lowerCamelCase							:    Tuple ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   __lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             return self.decoder.get(_lowerCamelCase      ,   self.unk_token )





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						" ".join(_lowerCamelCase ).replace("@@ "      ,   "" ).strip()
             return out_string





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Optional[str] = None ):


             '''simple docstring'''



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

             if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ):
                   copyfile(self.vocab_file      ,   _lowerCamelCase )

             if os.path.abspath(self.merges_file ) != os.path.abspath(_lowerCamelCase ):
                   copyfile(self.merges_file      ,   _lowerCamelCase )

             return out_vocab_file, out_merge_file





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             if isinstance(_lowerCamelCase      ,   _lowerCamelCase ):
                   try:
                         with open(_lowerCamelCase      ,   "r"      ,   encoding="utf-8" ) as fd:
                               self.add_from_file(_lowerCamelCase )
                   except FileNotFoundError as fnfe:
                         raise fnfe
                   except UnicodeError:
                         raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset" )
                   return

             lowerCamelCase__	:       str					=						f.readlines()
             for lineTmp in lines:
                   lowerCamelCase__	:       Any					=						lineTmp.strip()
                   lowerCamelCase__	:       List[Any]					=						line.rfind(" " )
                   if idx == -1:
                         raise ValueError("Incorrect dictionary format, expected \'<token> <cnt>\'" )
                   lowerCamelCase__	:       Optional[Any]					=						line[:idx]
                   lowerCamelCase__	:       Any					=						len(self.encoder )






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, 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     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    KandinskyVaaImgaImgPipeline
       A__      		=    ["image_embeds", "negative_image_embeds", "image"]
       A__      		=    [
           "image_embeds",
           "negative_image_embeds",
           "image",
       ]
       A__      		=    [
           "generator",
           "height",
           "width",
           "strength",
           "guidance_scale",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       A__      		=    False





       @property
       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             return 32





       @property
       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return 32





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


             '''simple docstring'''



             return self.time_input_dim





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


             '''simple docstring'''



             return self.time_input_dim * 4





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


             '''simple docstring'''



             return 100





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


             '''simple docstring'''



             torch.manual_seed(0 )

             lowerCamelCase__	:       Optional[Any]					=						{
                 "in_channels": 4,
                 # Out channels is double in channels because predicts mean and variance
                 "out_channels": 8,
                 "addition_embed_type": "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": "image_proj",
                 "cross_attention_dim": self.cross_attention_dim,
                 "attention_head_dim": 4,
                 "resnet_time_scale_shift": "scale_shift",
                 "class_embed_type": None,
             }

             lowerCamelCase__	:       Tuple					=						UNetaDConditionModel(**__lowerCamelCase )
             return model





       @property
       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             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 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             torch.manual_seed(0 )
             lowerCamelCase__	:       int					=						VQModel(**self.dummy_movq_kwargs )
             return model





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.dummy_unet
             lowerCamelCase__	:       Optional[Any]					=						self.dummy_movq

             lowerCamelCase__	:       Optional[int]					=						{
                 "num_train_timesteps": 1000,
                 "beta_schedule": "linear",
                 "beta_start": 0.0_0_0_8_5,
                 "beta_end": 0.0_1_2,
                 "clip_sample": False,
                 "set_alpha_to_one": False,
                 "steps_offset": 0,
                 "prediction_type": "epsilon",
                 "thresholding": False,
             }

             lowerCamelCase__	:       List[Any]					=						DDIMScheduler(**__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						{
                 "unet": unet,
                 "scheduler": scheduler,
                 "movq": movq,
             }

             return components





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    int=0 ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(seed + 1 ) ).to(
                 __lowerCamelCase )
             # create init_image
             lowerCamelCase__	:       Union[str, Any]					=						floats_tensor((1, 3, 64, 64)      ,   rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						image.cpu().permute(0      ,   2      ,   3      ,   1 )[0]
             lowerCamelCase__	:       Optional[int]					=						Image.fromarray(np.uinta(__lowerCamelCase ) ).convert("RGB" ).resize((256, 256) )

             if str(__lowerCamelCase ).startswith("mps" ):
                   lowerCamelCase__	:       Optional[int]					=						torch.manual_seed(__lowerCamelCase )
             else:
                   lowerCamelCase__	:       List[str]					=						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						{
                 "image": init_image,
                 "image_embeds": image_embeds,
                 "negative_image_embeds": negative_image_embeds,
                 "generator": generator,
                 "height": 64,
                 "width": 64,
                 "num_inference_steps": 10,
                 "guidance_scale": 7.0,
                 "strength": 0.2,
                 "output_type": "np",
             }
             return inputs





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						"cpu"

             lowerCamelCase__	:       str					=						self.get_dummy_components()

             lowerCamelCase__	:       Optional[int]					=						self.pipeline_class(**__lowerCamelCase )
             lowerCamelCase__	:       List[str]					=						pipe.to(__lowerCamelCase )

             pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Optional[Any]					=						pipe(**self.get_dummy_inputs(__lowerCamelCase ) )
             lowerCamelCase__	:       List[str]					=						output.images

             lowerCamelCase__	:       Optional[Any]					=						pipe(
                 **self.get_dummy_inputs(__lowerCamelCase )      ,   return_dict=__lowerCamelCase      ,   )[0]

             lowerCamelCase__	:       int					=						image[0, -3:, -3:, -1]
             lowerCamelCase__	:       int					=						image_from_tuple[0, -3:, -3:, -1]

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

             lowerCamelCase__	:       str					=						np.array(
                 [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_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     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/kandinskyv22/kandinskyv22_img2img_frog.npy" )

             lowerCamelCase__	:       Tuple					=						load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
             lowerCamelCase__	:       Any					=						"A red cartoon frog, 4k"

             lowerCamelCase__	:       str					=						KandinskyVaaPriorPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-2-prior"      ,   torch_dtype=torch.floataa )
             pipe_prior.to(__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						KandinskyVaaImgaImgPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-2-decoder"      ,   torch_dtype=torch.floataa )
             lowerCamelCase__	:       str					=						pipeline.to(__lowerCamelCase )

             pipeline.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						torch.Generator(device="cpu" ).manual_seed(0 )
             lowerCamelCase__					,     lowerCamelCase__	:       List[str]					=						pipe_prior(
                 __lowerCamelCase      ,   generator=__lowerCamelCase      ,   num_inference_steps=5      ,   negative_prompt=""      ,   ).to_tuple()

             lowerCamelCase__	:       Optional[Any]					=						pipeline(
                 image=__lowerCamelCase      ,   image_embeds=__lowerCamelCase      ,   negative_image_embeds=__lowerCamelCase      ,   generator=__lowerCamelCase      ,   num_inference_steps=100      ,   height=768      ,   width=768      ,   strength=0.2      ,   output_type="np"      ,   )

             lowerCamelCase__	:       Union[str, Any]					=						output.images[0]

             assert image.shape == (768, 768, 3)

             assert_mean_pixel_difference(__lowerCamelCase      ,   __lowerCamelCase )


