'''simple docstring'''





import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 					UpperCAmelCase			(							__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     unittest.TestCase			):




    '''simple docstring'''







    snake_case__				: List[Any]						    =    AltDiffusionPipeline
    snake_case__				: List[Any]						    =    TEXT_TO_IMAGE_PARAMS
    snake_case__				: Any						    =    TEXT_TO_IMAGE_BATCH_PARAMS
    snake_case__				: Any						    =    TEXT_TO_IMAGE_IMAGE_PARAMS
    snake_case__				: Tuple						    =    TEXT_TO_IMAGE_IMAGE_PARAMS






    def    _UpperCamelCase     (							self			)				->			Dict:
         torch.manual_seed(0			)
         SCREAMING_SNAKE_CASE       :      str     						=							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     ,				)
         SCREAMING_SNAKE_CASE       :      List[Any]     						=							DDIMScheduler(
             beta_start=0.0_0_0_8_5     ,				beta_end=0.0_1_2     ,				beta_schedule='scaled_linear'     ,				clip_sample=lowercase__     ,				set_alpha_to_one=lowercase__     ,				)
         torch.manual_seed(0			)
         SCREAMING_SNAKE_CASE       :      Optional[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     ,				)

         # TODO: address the non-deterministic text encoder (fails for save-load tests)
         # torch.manual_seed(0)
         # text_encoder_config = RobertaSeriesConfig(
         #     hidden_size=32,
         #     project_dim=32,
         #     intermediate_size=37,
         #     layer_norm_eps=1e-05,
         #     num_attention_heads=4,
         #     num_hidden_layers=5,
         #     vocab_size=5002,
         # )
         # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

         torch.manual_seed(0			)
         SCREAMING_SNAKE_CASE       :      Any     						=							CLIPTextConfig(
             bos_token_id=0     ,				eos_token_id=2     ,				hidden_size=32     ,				projection_dim=32     ,				intermediate_size=37     ,				layer_norm_eps=1E-05     ,				num_attention_heads=4     ,				num_hidden_layers=5     ,				pad_token_id=1     ,				vocab_size=5_002     ,				)
         SCREAMING_SNAKE_CASE       :      int     						=							CLIPTextModel(lowercase__			)

         SCREAMING_SNAKE_CASE       :      str     						=							XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'			)
         SCREAMING_SNAKE_CASE       :      Dict     						=							77

         SCREAMING_SNAKE_CASE       :      Dict     						=							{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components






    def    _UpperCamelCase     (							self     ,				lowercase__     ,				lowercase__=0			)				->			Any:
         if str(lowercase__			).startswith('mps'			):
              SCREAMING_SNAKE_CASE       :      str     						=							torch.manual_seed(lowercase__			)
         else:
              SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							torch.Generator(device=lowercase__			).manual_seed(lowercase__			)
         SCREAMING_SNAKE_CASE       :      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    _UpperCamelCase     (							self			)				->			List[Any]:
         super().test_attention_slicing_forward_pass(expected_max_diff=3E-3			)






    def    _UpperCamelCase     (							self			)				->			Tuple:
         super().test_inference_batch_single_identical(expected_max_diff=3E-3			)






    def    _UpperCamelCase     (							self			)				->			Tuple:
         SCREAMING_SNAKE_CASE       :      List[str]     						=							'cpu'  # ensure determinism for the device-dependent torch.Generator

         SCREAMING_SNAKE_CASE       :      Tuple     						=							self.get_dummy_components()
         torch.manual_seed(0			)
         SCREAMING_SNAKE_CASE       :      str     						=							RobertaSeriesConfig(
             hidden_size=32     ,				project_dim=32     ,				intermediate_size=37     ,				layer_norm_eps=1E-05     ,				num_attention_heads=4     ,				num_hidden_layers=5     ,				vocab_size=5_002     ,				)
         # TODO: remove after fixing the non-deterministic text encoder
         SCREAMING_SNAKE_CASE       :      str     						=							RobertaSeriesModelWithTransformation(lowercase__			)
         SCREAMING_SNAKE_CASE       :      List[str]     						=							text_encoder

         SCREAMING_SNAKE_CASE       :      Any     						=							AltDiffusionPipeline(**lowercase__			)
         SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							alt_pipe.to(lowercase__			)
         alt_pipe.set_progress_bar_config(disable=lowercase__			)

         SCREAMING_SNAKE_CASE       :      List[str]     						=							self.get_dummy_inputs(lowercase__			)
         SCREAMING_SNAKE_CASE       :      str     						=							'A photo of an astronaut'
         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							alt_pipe(**lowercase__			)
         SCREAMING_SNAKE_CASE       :      List[str]     						=							output.images
         SCREAMING_SNAKE_CASE       :      Dict     						=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							np.array(
             [0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3]			)

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






    def    _UpperCamelCase     (							self			)				->			str:
         SCREAMING_SNAKE_CASE       :      str     						=							'cpu'  # ensure determinism for the device-dependent torch.Generator

         SCREAMING_SNAKE_CASE       :      List[Any]     						=							self.get_dummy_components()
         SCREAMING_SNAKE_CASE       :      List[Any]     						=							PNDMScheduler(skip_prk_steps=lowercase__			)
         torch.manual_seed(0			)
         SCREAMING_SNAKE_CASE       :      Dict     						=							RobertaSeriesConfig(
             hidden_size=32     ,				project_dim=32     ,				intermediate_size=37     ,				layer_norm_eps=1E-05     ,				num_attention_heads=4     ,				num_hidden_layers=5     ,				vocab_size=5_002     ,				)
         # TODO: remove after fixing the non-deterministic text encoder
         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							RobertaSeriesModelWithTransformation(lowercase__			)
         SCREAMING_SNAKE_CASE       :      str     						=							text_encoder
         SCREAMING_SNAKE_CASE       :      Tuple     						=							AltDiffusionPipeline(**lowercase__			)
         SCREAMING_SNAKE_CASE       :      int     						=							alt_pipe.to(lowercase__			)
         alt_pipe.set_progress_bar_config(disable=lowercase__			)

         SCREAMING_SNAKE_CASE       :      Any     						=							self.get_dummy_inputs(lowercase__			)
         SCREAMING_SNAKE_CASE       :      List[str]     						=							alt_pipe(**lowercase__			)
         SCREAMING_SNAKE_CASE       :      List[Any]     						=							output.images
         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         SCREAMING_SNAKE_CASE       :      str     						=							np.array(
             [0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7]			)

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






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




    '''simple docstring'''







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






    def    _UpperCamelCase     (							self			)				->			int:
         # make sure here that pndm scheduler skips prk
         SCREAMING_SNAKE_CASE       :      Any     						=							AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'     ,				safety_checker=lowercase__			)
         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							alt_pipe.to(lowercase__			)
         alt_pipe.set_progress_bar_config(disable=lowercase__			)

         SCREAMING_SNAKE_CASE       :      Any     						=							'A painting of a squirrel eating a burger'
         SCREAMING_SNAKE_CASE       :      str     						=							torch.manual_seed(0			)
         SCREAMING_SNAKE_CASE       :      Any     						=							alt_pipe([prompt]     ,				generator=lowercase__     ,				guidance_scale=6.0     ,				num_inference_steps=20     ,				output_type='np'			)

         SCREAMING_SNAKE_CASE       :      Tuple     						=							output.images

         SCREAMING_SNAKE_CASE       :      List[str]     						=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							np.array([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6]			)

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






    def    _UpperCamelCase     (							self			)				->			int:
         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							DDIMScheduler.from_pretrained('BAAI/AltDiffusion'     ,				subfolder='scheduler'			)

         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'     ,				scheduler=lowercase__     ,				safety_checker=lowercase__			)
         SCREAMING_SNAKE_CASE       :      Any     						=							alt_pipe.to(lowercase__			)
         alt_pipe.set_progress_bar_config(disable=lowercase__			)

         SCREAMING_SNAKE_CASE       :      List[Any]     						=							'A painting of a squirrel eating a burger'
         SCREAMING_SNAKE_CASE       :      Any     						=							torch.manual_seed(0			)

         SCREAMING_SNAKE_CASE       :      Tuple     						=							alt_pipe([prompt]     ,				generator=lowercase__     ,				num_inference_steps=2     ,				output_type='numpy'			)
         SCREAMING_SNAKE_CASE       :      List[str]     						=							output.images

         SCREAMING_SNAKE_CASE       :      List[str]     						=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         SCREAMING_SNAKE_CASE       :      int     						=							np.array([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3]			)

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





import json
import os
from typing import Optional, Tuple

import regex as re

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


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

__magic_name__  							=	{
    '''vocab_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'''},
    '''merges_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'''},
}

__magic_name__  							=	{
    '''ctrl''': 256,
}

__magic_name__  							=	{
    '''Pregnancy''': 168_629,
    '''Christianity''': 7_675,
    '''Explain''': 106_423,
    '''Fitness''': 63_440,
    '''Saving''': 63_163,
    '''Ask''': 27_171,
    '''Ass''': 95_985,
    '''Joke''': 163_509,
    '''Questions''': 45_622,
    '''Thoughts''': 49_605,
    '''Retail''': 52_342,
    '''Feminism''': 164_338,
    '''Writing''': 11_992,
    '''Atheism''': 192_263,
    '''Netflix''': 48_616,
    '''Computing''': 39_639,
    '''Opinion''': 43_213,
    '''Alone''': 44_967,
    '''Funny''': 58_917,
    '''Gaming''': 40_358,
    '''Human''': 4_088,
    '''India''': 1_331,
    '''Joker''': 77_138,
    '''Diet''': 36_206,
    '''Legal''': 11_859,
    '''Norman''': 4_939,
    '''Tip''': 72_689,
    '''Weight''': 52_343,
    '''Movies''': 46_273,
    '''Running''': 23_425,
    '''Science''': 2_090,
    '''Horror''': 37_793,
    '''Confession''': 60_572,
    '''Finance''': 12_250,
    '''Politics''': 16_360,
    '''Scary''': 191_985,
    '''Support''': 12_654,
    '''Technologies''': 32_516,
    '''Teenage''': 66_160,
    '''Event''': 32_769,
    '''Learned''': 67_460,
    '''Notion''': 182_770,
    '''Wikipedia''': 37_583,
    '''Books''': 6_665,
    '''Extract''': 76_050,
    '''Confessions''': 102_701,
    '''Conspiracy''': 75_932,
    '''Links''': 63_674,
    '''Narcissus''': 150_425,
    '''Relationship''': 54_766,
    '''Relationships''': 134_796,
    '''Reviews''': 41_671,
    '''News''': 4_256,
    '''Translation''': 26_820,
    '''multilingual''': 128_406,
}





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					set()
    snake_case__    =					word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)				)
        snake_case__    =					char

    snake_case__    =					set(__lowerCAmelCase				)
    return pairs





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Tuple     						=  VOCAB_FILES_NAMES
      _A    :		str     						=  PRETRAINED_VOCAB_FILES_MAP
      _A    :		int     						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A    :		List[Any]     						=  CONTROL_CODES
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase="<unk>"		,						**lowerCamelCase			):
          super().__init__(unk_token=lowerCamelCase		,						**lowerCamelCase			)

          with open(lowerCamelCase		,						encoding="utf-8"			) as vocab_handle:
              snake_case__    =					json.load(lowerCamelCase			)
          snake_case__    =					{v: k for k, v in self.encoder.items()}
          with open(lowerCamelCase		,						encoding="utf-8"			) as merges_handle:
              snake_case__    =					merges_handle.read().split("\n"			)[1:-1]
          snake_case__    =					[tuple(merge.split()			) for merge in merges]
          snake_case__    =					dict(zip(lowerCamelCase		,						range(len(lowerCamelCase			)			)			)			)
          snake_case__    =					{}
      @property
      def A_      (      self			):
          return len(self.encoder			)
      def A_      (      self			):
          return dict(self.encoder		,						**self.added_tokens_encoder			)
      def A_      (      self		,						lowerCamelCase			):
          if token in self.cache:
              return self.cache[token]
          snake_case__    =					tuple(lowerCamelCase			)
          snake_case__    =					tuple(list(word[:-1]			) + [word[-1] + "</w>"]			)
          snake_case__    =					get_pairs(lowerCamelCase			)

          if not pairs:
              return token

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

          snake_case__    =					re.findall(r"\S+\n?"		,						lowerCamelCase			)

          for token in words:
              split_tokens.extend(list(self.bpe(lowerCamelCase			).split(" "			)			)			)
          return split_tokens
      def A_      (      self		,						lowerCamelCase			):
          return self.encoder.get(lowerCamelCase		,						self.encoder.get(self.unk_token			)			)
      def A_      (      self		,						lowerCamelCase			):
          return self.decoder.get(lowerCamelCase		,						self.unk_token			)
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					" ".join(lowerCamelCase			).replace("@@ "		,						""			).strip()
          return out_string



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

          snake_case__    =					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!"			)
                      snake_case__    =					token_index
                  writer.write(" ".join(lowerCamelCase			) + "\n"			)
                  index += 1

          return vocab_file, merge_file

      # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
      #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
      #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
      #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
      #     return ''.join(tokens_generated_so_far)



