

"""simple docstring"""






import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

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


_lowerCAmelCase						=		logging.get_logger(__name__)


_lowerCAmelCase						=		{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_lowerCAmelCase						=		{
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

_lowerCAmelCase						=		{"""facebook/blenderbot_small-90M""": 5_1_2}

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    set()
    _lowerCAmelCase       :   Optional[int]  =    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)   )
        _lowerCAmelCase       :   Tuple  =    char

    _lowerCAmelCase       :   Dict  =    set(_lowerCamelCase   )
    return pairs







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		["input_ids", "attention_mask"]

  def __init__(		self    ,_A    ,_A    ,_A="__start__"    ,_A="__end__"    ,_A="__unk__"    ,_A="__null__"    ,**_A    ,):
      '''simple docstring'''

      super().__init__(unk_token=_A    ,bos_token=_A    ,eos_token=_A    ,pad_token=_A    ,**_A      )

      with open(_A    ,encoding='utf-8'      ) as vocab_handle:
          _lowerCAmelCase       :   Union[str, Any]  =    json.load(_A      )
      _lowerCAmelCase       :   int  =    {v: k for k, v in self.encoder.items()}
      with open(_A    ,encoding='utf-8'      ) as merges_handle:
          _lowerCAmelCase       :   Optional[int]  =    merges_handle.read().split('\n'      )[1:-1]
      _lowerCAmelCase       :   Optional[int]  =    [tuple(merge.split()      ) for merge in merges]
      _lowerCAmelCase       :   Union[str, Any]  =    dict(zip(_A    ,range(len(_A      )      )      )      )
      _lowerCAmelCase       :   Dict  =    {}

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return len(self.encoder      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if token in self.cache:
          return self.cache[token]
      _lowerCAmelCase       :   Union[str, Any]  =    re.sub('([.,!?()])'    ,r' \1'    ,_A      )
      _lowerCAmelCase       :   Any  =    re.sub('(\')'    ,r' \1 '    ,_A      )
      _lowerCAmelCase       :   Tuple  =    re.sub(r'\s{2,}'    ,' '    ,_A      )
      if "\n" in token:
          _lowerCAmelCase       :   Optional[Any]  =    token.replace('\n'    ,' __newln__'      )

      _lowerCAmelCase       :   Optional[Any]  =    token.split(' '      )
      _lowerCAmelCase       :   List[str]  =    []
      for token in tokens:
          if not len(_A      ):
              continue

          _lowerCAmelCase       :   str  =    token.lower()
          _lowerCAmelCase       :   Optional[Any]  =    tuple(_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    tuple(list(word[:-1]      ) + [word[-1] + '</w>']      )
          _lowerCAmelCase       :   str  =    get_pairs(_A      )

          if not pairs:
              words.append(_A      )
              continue

          while True:
              _lowerCAmelCase       :   str  =    min(_A    ,key=lambda _A      : self.bpe_ranks.get(_A    ,float('inf'      )      )      )
              if bigram not in self.bpe_ranks:
                  break
              _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    bigram
              _lowerCAmelCase       :   List[Any]  =    []
              _lowerCAmelCase       :   Any  =    0

              while i < len(_A      ):
                  try:
                      _lowerCAmelCase       :   str  =    word.index(_A    ,_A      )
                      new_word.extend(word[i:j]      )
                      _lowerCAmelCase       :   Union[str, Any]  =    j
                  except ValueError:
                      new_word.extend(word[i:]      )
                      break

                  if word[i] == first and i < len(_A      ) - 1 and word[i + 1] == second:
                      new_word.append(first + second      )
                      i += 2
                  else:
                      new_word.append(word[i]      )
                      i += 1
              _lowerCAmelCase       :   Optional[int]  =    tuple(_A      )
              _lowerCAmelCase       :   str  =    new_word
              if len(_A      ) == 1:
                  break
              else:
                  _lowerCAmelCase       :   List[Any]  =    get_pairs(_A      )
          _lowerCAmelCase       :   Any  =    '@@ '.join(_A      )
          _lowerCAmelCase       :   List[str]  =    word[:-4]

          _lowerCAmelCase       :   Any  =    word
          words.append(_A      )
      return " ".join(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    []

      _lowerCAmelCase       :   int  =    re.findall(r'\S+\n?'    ,_A      )

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

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    token.lower()
      return self.encoder.get(_A    ,self.encoder.get(self.unk_token      )      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    ' '.join(_A      ).replace('@@ '    ,''      ).strip()
      return out_string







  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      if not os.path.isdir(_A      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
      _lowerCAmelCase       :   Dict  =    os.path.join(
          _A    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
      _lowerCAmelCase       :   Tuple  =    os.path.join(
          _A    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

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

      _lowerCAmelCase       :   Tuple  =    0
      with open(_A    ,'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 _A      : 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       :   Dict  =    token_index
              writer.write(' '.join(_A      ) + '\n'      )
              index += 1

      return vocab_file, merge_file








"""simple docstring"""






import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		LDMTextToImagePipeline
  _UpperCAmelCase          =		TEXT_TO_IMAGE_PARAMS - {
      "negative_prompt",
      "negative_prompt_embeds",
      "cross_attention_kwargs",
      "prompt_embeds",
  }
  _UpperCAmelCase          =		PipelineTesterMixin.required_optional_params - {
      "num_images_per_prompt",
      "callback",
      "callback_steps",
  }
  _UpperCAmelCase          =		TEXT_TO_IMAGE_BATCH_PARAMS
  _UpperCAmelCase          =		False

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      torch.manual_seed(0      )
      _lowerCAmelCase       :   Optional[int]  =    UNetaDConditionModel(
          block_out_channels=(32, 64)    ,layers_per_block=2    ,sample_size=32    ,in_channels=4    ,out_channels=4    ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,cross_attention_dim=32    ,)
      _lowerCAmelCase       :   Union[str, Any]  =    DDIMScheduler(
          beta_start=0.0_0_0_8_5    ,beta_end=0.0_1_2    ,beta_schedule='scaled_linear'    ,clip_sample=_A    ,set_alpha_to_one=_A    ,)
      torch.manual_seed(0      )
      _lowerCAmelCase       :   Union[str, Any]  =    AutoencoderKL(
          block_out_channels=(32, 64)    ,in_channels=3    ,out_channels=3    ,down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')    ,up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')    ,latent_channels=4    ,)
      torch.manual_seed(0      )
      _lowerCAmelCase       :   Dict  =    CLIPTextConfig(
          bos_token_id=0    ,eos_token_id=2    ,hidden_size=32    ,intermediate_size=37    ,layer_norm_eps=1E-05    ,num_attention_heads=4    ,num_hidden_layers=5    ,pad_token_id=1    ,vocab_size=1000    ,)
      _lowerCAmelCase       :   Tuple  =    CLIPTextModel(_A      )
      _lowerCAmelCase       :   Any  =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

      _lowerCAmelCase       :   List[str]  =    {
          'unet': unet,
          'scheduler': scheduler,
          'vqvae': vae,
          'bert': text_encoder,
          'tokenizer': tokenizer,
      }
      return components

  def __lowerCamelCase		(		self    ,_A    ,_A=0      ):
      '''simple docstring'''

      if str(_A      ).startswith('mps'      ):
          _lowerCAmelCase       :   int  =    torch.manual_seed(_A      )
      else:
          _lowerCAmelCase       :   Optional[Any]  =    torch.Generator(device=_A      ).manual_seed(_A      )
      _lowerCAmelCase       :   List[Any]  =    {
          'prompt': 'A painting of a squirrel eating a burger',
          'generator': generator,
          'num_inference_steps': 2,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }
      return inputs







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    'cpu'  # ensure determinism for the device-dependent torch.Generator

      _lowerCAmelCase       :   int  =    self.get_dummy_components()
      _lowerCAmelCase       :   str  =    LDMTextToImagePipeline(**_A      )
      pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   List[Any]  =    self.get_dummy_inputs(_A      )
      _lowerCAmelCase       :   Any  =    pipe(**_A      ).images
      _lowerCAmelCase       :   Optional[int]  =    image[0, -3:, -3:, -1]

      assert image.shape == (1, 16, 16, 3)
      _lowerCAmelCase       :   Tuple  =    np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3







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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self    ,_A    ,_A=torch.floataa    ,_A=0      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    torch.manual_seed(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    np.random.RandomState(_A      ).standard_normal((1, 4, 32, 32)      )
      _lowerCAmelCase       :   Optional[Any]  =    torch.from_numpy(_A      ).to(device=_A    ,dtype=_A      )
      _lowerCAmelCase       :   List[str]  =    {
          'prompt': 'A painting of a squirrel eating a burger',
          'latents': latents,
          'generator': generator,
          'num_inference_steps': 3,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }
      return inputs







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'      ).to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Optional[Any]  =    self.get_inputs(_A      )
      _lowerCAmelCase       :   List[Any]  =    pipe(**_A      ).images
      _lowerCAmelCase       :   Dict  =    image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 256, 256, 3)
      _lowerCAmelCase       :   str  =    np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8]      )
      _lowerCAmelCase       :   Dict  =    np.abs(expected_slice - image_slice      ).max()
      assert max_diff < 1E-3







@nightly
@require_torch_gpu
class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self    ,_A    ,_A=torch.floataa    ,_A=0      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    torch.manual_seed(_A      )
      _lowerCAmelCase       :   Optional[int]  =    np.random.RandomState(_A      ).standard_normal((1, 4, 32, 32)      )
      _lowerCAmelCase       :   List[Any]  =    torch.from_numpy(_A      ).to(device=_A    ,dtype=_A      )
      _lowerCAmelCase       :   int  =    {
          'prompt': 'A painting of a squirrel eating a burger',
          'latents': latents,
          'generator': generator,
          'num_inference_steps': 50,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }
      return inputs







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'      ).to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   str  =    self.get_inputs(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    pipe(**_A      ).images[0]

      _lowerCAmelCase       :   int  =    load_numpy(
          'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'      )
      _lowerCAmelCase       :   List[str]  =    np.abs(expected_image - image      ).max()
      assert max_diff < 1E-3





