




'''simple docstring'''


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

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


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {
    "vocab_file": "vocab.txt",
    "merges_file": "bpe.codes",
}

UpperCAmelCase_ 		=      {
    "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",
    },
}

UpperCAmelCase_ 		=      {
    "vinai/phobert-base": 2_56,
    "vinai/phobert-large": 2_56,
}


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[Any]			):
  __a			=					set()
  __a			=					word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)			)
    __a			=					char

  __a			=					set(a_			)
  return pairs





class    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase="<s>"      ,							UpperCamelCase="</s>"      ,							UpperCamelCase="</s>"      ,							UpperCamelCase="<s>"      ,							UpperCamelCase="<unk>"      ,							UpperCamelCase="<pad>"      ,							UpperCamelCase="<mask>"      ,							**UpperCamelCase      ,							)				->    Optional[int]:
     super().__init__(
         bos_token=UpperCamelCase      ,							eos_token=UpperCamelCase      ,							unk_token=UpperCamelCase      ,							sep_token=UpperCamelCase      ,							cls_token=UpperCamelCase      ,							pad_token=UpperCamelCase      ,							mask_token=UpperCamelCase      ,							**UpperCamelCase      ,							)

     __a			=					vocab_file
     __a			=					merges_file

     __a			=					{}
     __a			=					0
     __a			=					1
     __a			=					2
     __a			=					3

     self.add_from_file(UpperCamelCase      )

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

     with open(UpperCamelCase      ,							encoding='utf-8'      ) as merges_handle:
       __a			=					merges_handle.read().split('\n'      )[:-1]
     __a			=					[tuple(merge.split()[:-1]      ) for merge in merges]
     __a			=					dict(zip(UpperCamelCase      ,							range(len(UpperCamelCase      )      )      )      )
     __a			=					{}







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:

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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = False      )				->    List[int]:

     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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __a			=					[self.sep_token_id]
     __a			=					[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 		UpperCamelCase__			(	self      )				->    str:
     return len(self.encoder      )







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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[str]:
     if token in self.cache:
       return self.cache[token]
     __a			=					tuple(UpperCamelCase      )
     __a			=					tuple(list(word[:-1]      ) + [word[-1] + '</w>']      )
     __a			=					get_pairs(UpperCamelCase      )

     if not pairs:
       return token

     while True:
       __a			=					min(UpperCamelCase      ,							key=lambda UpperCamelCase      : self.bpe_ranks.get(UpperCamelCase      ,							float('inf'      )      )      )
       if bigram not in self.bpe_ranks:
         break
       __a	,       __a			=					bigram
       __a			=					[]
       __a			=					0
       while i < len(UpperCamelCase      ):
         try:
           __a			=					word.index(UpperCamelCase      ,							UpperCamelCase      )
         except ValueError:
           new_word.extend(word[i:]      )
           break
         else:
           new_word.extend(word[i:j]      )
           __a			=					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			=					tuple(UpperCamelCase      )
       __a			=					new_word
       if len(UpperCamelCase      ) == 1:
         break
       else:
         __a			=					get_pairs(UpperCamelCase      )
     __a			=					'@@ '.join(UpperCamelCase      )
     __a			=					word[:-4]
     __a			=					word
     return word







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    str:
     __a			=					[]

     __a			=					re.findall(R'\S+\n?'      ,							UpperCamelCase      )

     for token in words:
       split_tokens.extend(list(self.bpe(UpperCamelCase      ).split(' '      )      )      )
     return split_tokens







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Any:
     return self.encoder.get(UpperCamelCase      ,							self.encoder.get(self.unk_token      )      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Any:
     return self.decoder.get(UpperCamelCase      ,							self.unk_token      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[Any]:
     __a			=					' '.join(UpperCamelCase      ).replace('@@ '      ,							''      ).strip()
     return out_string







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

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

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

     return out_vocab_file, out_merge_file







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Union[str, Any]:
     if isinstance(UpperCamelCase      ,							UpperCamelCase      ):
       try:
         with open(UpperCamelCase      ,							'r'      ,							encoding='utf-8'      ) as fd:
           self.add_from_file(UpperCamelCase      )
       except FileNotFoundError as fnfe:
         raise fnfe
       except UnicodeError:
         raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset"      )
       return

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









'''simple docstring'''


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


UpperCAmelCase_ 		=      False





class    __lowercase						(  unittest.TestCase ):







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







   @property
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     return 12







   @property
   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     return 12







   @property
   def 		UpperCamelCase__			(	self      )				->    List[str]:
     return 32







   @property
   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     torch.manual_seed(0      )
     __a			=					VQModel(
         block_out_channels=[32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,							latent_channels=3      ,							num_vq_embeddings=self.num_embed      ,							vq_embed_dim=3      ,							)
     return model







   @property
   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
     return tokenizer







   @property
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     torch.manual_seed(0      )
     __a			=					CLIPTextConfig(
         bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=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=1000      ,							)
     return CLIPTextModel(UpperCamelCase      )







   @property
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     torch.manual_seed(0      )

     __a			=					12
     __a			=					12

     __a			=					{
         'attention_bias': True,
         'cross_attention_dim': 32,
         'attention_head_dim': height * width,
         'num_attention_heads': 1,
         'num_vector_embeds': self.num_embed,
         'num_embeds_ada_norm': self.num_embeds_ada_norm,
         'norm_num_groups': 32,
         'sample_size': width,
         'activation_fn': 'geglu-approximate',
     }

     __a			=					TransformeraDModel(**UpperCamelCase      )
     return model







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					'cpu'

     __a			=					self.dummy_vqvae
     __a			=					self.dummy_text_encoder
     __a			=					self.dummy_tokenizer
     __a			=					self.dummy_transformer
     __a			=					VQDiffusionScheduler(self.num_embed      )
     __a			=					LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCamelCase      )

     __a			=					VQDiffusionPipeline(
         vqvae=UpperCamelCase      ,							text_encoder=UpperCamelCase      ,							tokenizer=UpperCamelCase      ,							transformer=UpperCamelCase      ,							scheduler=UpperCamelCase      ,							learned_classifier_free_sampling_embeddings=UpperCamelCase      ,							)
     __a			=					pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					'teddy bear playing in the pool'

     __a			=					torch.Generator(device=UpperCamelCase      ).manual_seed(0      )
     __a			=					pipe([prompt]      ,							generator=UpperCamelCase      ,							num_inference_steps=2      ,							output_type='np'      )
     __a			=					output.images

     __a			=					torch.Generator(device=UpperCamelCase      ).manual_seed(0      )
     __a			=					pipe(
         [prompt]      ,							generator=UpperCamelCase      ,							output_type='np'      ,							return_dict=UpperCamelCase      ,							num_inference_steps=2      )[0]

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

     assert image.shape == (1, 24, 24, 3)

     __a			=					np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]      )

     assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					'cpu'

     __a			=					self.dummy_vqvae
     __a			=					self.dummy_text_encoder
     __a			=					self.dummy_tokenizer
     __a			=					self.dummy_transformer
     __a			=					VQDiffusionScheduler(self.num_embed      )
     __a			=					LearnedClassifierFreeSamplingEmbeddings(
         learnable=UpperCamelCase      ,							hidden_size=self.text_embedder_hidden_size      ,							length=tokenizer.model_max_length      )

     __a			=					VQDiffusionPipeline(
         vqvae=UpperCamelCase      ,							text_encoder=UpperCamelCase      ,							tokenizer=UpperCamelCase      ,							transformer=UpperCamelCase      ,							scheduler=UpperCamelCase      ,							learned_classifier_free_sampling_embeddings=UpperCamelCase      ,							)
     __a			=					pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					'teddy bear playing in the pool'

     __a			=					torch.Generator(device=UpperCamelCase      ).manual_seed(0      )
     __a			=					pipe([prompt]      ,							generator=UpperCamelCase      ,							num_inference_steps=2      ,							output_type='np'      )
     __a			=					output.images

     __a			=					torch.Generator(device=UpperCamelCase      ).manual_seed(0      )
     __a			=					pipe(
         [prompt]      ,							generator=UpperCamelCase      ,							output_type='np'      ,							return_dict=UpperCamelCase      ,							num_inference_steps=2      )[0]

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

     assert image.shape == (1, 24, 24, 3)

     __a			=					np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]      )

     assert np.abs(image_slice.flatten() - expected_slice      ).max() < 2.0
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2





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







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







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'      )

     __a			=					VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'      )
     __a			=					pipeline.to(UpperCamelCase      )
     pipeline.set_progress_bar_config(disable=UpperCamelCase      )

     # requires GPU generator for gumbel softmax
     # don't use GPU generator in tests though
     __a			=					torch.Generator(device=UpperCamelCase      ).manual_seed(0      )
     __a			=					pipeline(
         'teddy bear playing in the pool'      ,							num_images_per_prompt=1      ,							generator=UpperCamelCase      ,							output_type='np'      ,							)

     __a			=					output.images[0]

     assert image.shape == (256, 256, 3)
     assert np.abs(expected_image - image      ).max() < 2.0



