import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class      A							(				UpperCAmelCase_		,				UpperCAmelCase_		,				unittest.TestCase     ):
   __UpperCAmelCase : int      =			StableDiffusionXLImgaImgPipeline
   __UpperCAmelCase : List[str]      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
   __UpperCAmelCase : str      =			PipelineTesterMixin.required_optional_params - {'latents'}
   __UpperCAmelCase : Optional[int]      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   __UpperCAmelCase : Optional[int]      =			IMAGE_TO_IMAGE_IMAGE_PARAMS
   __UpperCAmelCase : Dict      =			IMAGE_TO_IMAGE_IMAGE_PARAMS


   def 			lowercase_      (self     :						Tuple		)						->				List[Any]:
         """simple docstring"""



         torch.manual_seed(0		)
         UpperCAmelCase__								=  UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)       ,  layers_per_block=2       ,  sample_size=3_2       ,  in_channels=4       ,  out_channels=4       ,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,  attention_head_dim=(2, 4)       ,  use_linear_projection=__UpperCAmelCase       ,  addition_embed_type="text_time"       ,  addition_time_embed_dim=8       ,  transformer_layers_per_block=(1, 2)       ,  projection_class_embeddings_input_dim=8_0       ,  cross_attention_dim=6_4       ,  )
         UpperCAmelCase__								=  EulerDiscreteScheduler(
             beta_start=0.00085       ,  beta_end=0.012       ,  steps_offset=1       ,  beta_schedule="scaled_linear"       ,  timestep_spacing="leading"       ,  )
         torch.manual_seed(0		)
         UpperCAmelCase__								=  AutoencoderKL(
             block_out_channels=[3_2, 6_4]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]       ,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]       ,  latent_channels=4       ,  sample_size=1_2_8       ,  )
         torch.manual_seed(0		)
         UpperCAmelCase__								=  CLIPTextConfig(
             bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=3_2       ,  intermediate_size=3_7       ,  layer_norm_eps=1E-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1_0_0_0       ,  hidden_act="gelu"       ,  projection_dim=3_2       ,  )
         UpperCAmelCase__								=  CLIPTextModel(__UpperCAmelCase		)
         UpperCAmelCase__								=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       ,  local_files_only=__UpperCAmelCase		)

         UpperCAmelCase__								=  CLIPTextModelWithProjection(__UpperCAmelCase		)
         UpperCAmelCase__								=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       ,  local_files_only=__UpperCAmelCase		)

         UpperCAmelCase__								=  {
             "unet": unet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "text_encoder_2": text_encoder_a,
             "tokenizer_2": tokenizer_a,
             # "safety_checker": None,
             # "feature_extractor": None,
         }
         return components


   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						Tuple       ,  __UpperCAmelCase     :						Optional[int]=0		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  floats_tensor((1, 3, 3_2, 3_2)       ,  rng=random.Random(__UpperCAmelCase		)		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  image / 2 + 0.5
         if str(__UpperCAmelCase		).startswith("mps"		):
               UpperCAmelCase__								=  torch.manual_seed(__UpperCAmelCase		)
         else:
               UpperCAmelCase__								=  torch.Generator(device=__UpperCAmelCase		).manual_seed(__UpperCAmelCase		)
         UpperCAmelCase__								=  {
             "prompt": "A painting of a squirrel eating a burger",
             "image": image,
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 5.0,
             "output_type": "numpy",
             "strength": 0.75,
         }
         return inputs


   def 			lowercase_      (self     :						Any		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__								=  self.get_dummy_components()
         UpperCAmelCase__								=  StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase		)
         UpperCAmelCase__								=  sd_pipe.to(__UpperCAmelCase		)
         sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         UpperCAmelCase__								=  self.get_dummy_inputs(__UpperCAmelCase		)
         UpperCAmelCase__								=  sd_pipe(**__UpperCAmelCase		).images
         UpperCAmelCase__								=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)

         UpperCAmelCase__								=  np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]		)

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


   def 			lowercase_      (self     :						Tuple		)						->				Optional[Any]:
         """simple docstring"""



         super().test_attention_slicing_forward_pass(expected_max_diff=3E-3		)


   def 			lowercase_      (self     :						int		)						->				List[Any]:
         """simple docstring"""



         super().test_inference_batch_single_identical(expected_max_diff=3E-3		)


   def 			lowercase_      (self     :						Tuple		)						->				Union[str, Any]:
         """simple docstring"""



         pass





   def 			lowercase_      (self     :						int		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_dummy_components()
         UpperCAmelCase__								=  StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase		)
         UpperCAmelCase__								=  sd_pipe.to(__UpperCAmelCase		)
         UpperCAmelCase__								=  sd_pipe.to(__UpperCAmelCase		)
         sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         # forward without prompt embeds
         UpperCAmelCase__								=  self.get_dummy_inputs(__UpperCAmelCase		)
         UpperCAmelCase__								=  3 * ["this is a negative prompt"]
         UpperCAmelCase__								=  negative_prompt
         UpperCAmelCase__								=  3 * [inputs["prompt"]]

         UpperCAmelCase__								=  sd_pipe(**__UpperCAmelCase		)
         UpperCAmelCase__								=  output.images[0, -3:, -3:, -1]

         # forward with prompt embeds
         UpperCAmelCase__								=  self.get_dummy_inputs(__UpperCAmelCase		)
         UpperCAmelCase__								=  3 * ["this is a negative prompt"]
         UpperCAmelCase__								=  3 * [inputs.pop("prompt"		)]

         (
             (
             UpperCAmelCase__
         )		,      (
             UpperCAmelCase__
         )		,      (
             UpperCAmelCase__
         )		,      (
             UpperCAmelCase__
         )		,      
         )								=  sd_pipe.encode_prompt(__UpperCAmelCase       ,  negative_prompt=__UpperCAmelCase		)

         UpperCAmelCase__								=  sd_pipe(
             **__UpperCAmelCase       ,  prompt_embeds=__UpperCAmelCase       ,  negative_prompt_embeds=__UpperCAmelCase       ,  pooled_prompt_embeds=__UpperCAmelCase       ,  negative_pooled_prompt_embeds=__UpperCAmelCase       ,  )
         UpperCAmelCase__								=  output.images[0, -3:, -3:, -1]

         # make sure that it's equal
         assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()		).max() < 1E-4





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


   def 			lowercase_      (self     :						Any		)						->				Optional[Any]:
         """simple docstring"""



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


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[Any]="cpu"       ,  __UpperCAmelCase     :						Any=torch.floataa       ,  __UpperCAmelCase     :						List[Any]=0		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  torch.Generator(device=__UpperCAmelCase		).manual_seed(__UpperCAmelCase		)
         UpperCAmelCase__								=  np.random.RandomState(__UpperCAmelCase		).standard_normal((1, 4, 6_4, 6_4)		)
         UpperCAmelCase__								=  torch.from_numpy(__UpperCAmelCase		).to(device=__UpperCAmelCase       ,  dtype=__UpperCAmelCase		)
         UpperCAmelCase__								=  {
             "prompt": "a photograph of an astronaut riding a horse",
             "latents": latents,
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
             "output_type": "numpy",
         }
         return inputs





   def 			lowercase_      (self     :						Optional[int]		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"		)
         pipe.to(__UpperCAmelCase		)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         UpperCAmelCase__								=  self.get_inputs(__UpperCAmelCase		)
         UpperCAmelCase__								=  pipe(**__UpperCAmelCase		).images
         UpperCAmelCase__								=  image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         UpperCAmelCase__								=  np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]		)
         assert np.abs(image_slice - expected_slice		).max() < 7E-3




import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
UpperCamelCase__											=					logging.getLogger(__name__)




def    lowerCAmelCase_ (     )     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  argparse.ArgumentParser(
          description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."							)
      parser.add_argument("--file_path",  type=__A,  default="data/dump.txt",  help="The path to the data."							)
      parser.add_argument("--tokenizer_type",  type=__A,  default="bert",  choices=["bert", "roberta", "gpt2"]							)
      parser.add_argument("--tokenizer_name",  type=__A,  default="bert-base-uncased",  help="The tokenizer to use."							)
      parser.add_argument("--dump_file",  type=__A,  default="data/dump",  help="The dump file prefix."							)
      UpperCAmelCase__								=  parser.parse_args()

      logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""							)
      if args.tokenizer_type == "bert":
            UpperCAmelCase__								=  BertTokenizer.from_pretrained(args.tokenizer_name							)
            UpperCAmelCase__								=  tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
            UpperCAmelCase__								=  tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            UpperCAmelCase__								=  RobertaTokenizer.from_pretrained(args.tokenizer_name							)
            UpperCAmelCase__								=  tokenizer.special_tokens_map["cls_token"]  # `<s>`
            UpperCAmelCase__								=  tokenizer.special_tokens_map["sep_token"]  # `</s>`
      elif args.tokenizer_type == "gpt2":
            UpperCAmelCase__								=  GPTaTokenizer.from_pretrained(args.tokenizer_name							)
            UpperCAmelCase__								=  tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
            UpperCAmelCase__								=  tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

      logger.info(f"""Loading text from {args.file_path}"""							)
      with open(args.file_path,  "r",  encoding="utf8"							) as fp:
            UpperCAmelCase__								=  fp.readlines()

      logger.info("Start encoding"							)
      logger.info(f"""{len(__A							)} examples to process."""							)

      UpperCAmelCase__								=  []
      UpperCAmelCase__								=  0
      UpperCAmelCase__								=  10_000
      UpperCAmelCase__								=  time.time()
      for text in data:
            UpperCAmelCase__								=  f"""{bos} {text.strip()} {sep}"""
            UpperCAmelCase__								=  tokenizer.encode(__A,  add_special_tokens=__A							)
            rslt.append(__A							)

            iter += 1
            if iter % interval == 0:
                  UpperCAmelCase__								=  time.time()
                  logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""							)
                  UpperCAmelCase__								=  time.time()
      logger.info("Finished binarization"							)
      logger.info(f"""{len(__A							)} examples processed."""							)

      UpperCAmelCase__								=  f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
      UpperCAmelCase__								=  tokenizer.vocab_size
      if vocab_size < (1 << 16):
            UpperCAmelCase__								=  [np.uintaa(__A							) for d in rslt]
      else:
            UpperCAmelCase__								=  [np.intaa(__A							) for d in rslt]
      random.shuffle(rslt_							)
      logger.info(f"""Dump to {dp_file}"""							)
      with open(__A,  "wb"							) as handle:
            pickle.dump(rslt_,  __A,  protocol=pickle.HIGHEST_PROTOCOL							)


if __name__ == "__main__":
      main()



