


"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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 		_A    (    _a       ,unittest.TestCase    ):







    """simple docstring"""




    UpperCAmelCase			:				Optional[int]      =			KandinskyPipeline
    UpperCAmelCase			:				str      =			[
        """prompt""",
        """image_embeds""",
        """negative_image_embeds""",
    ]
    UpperCAmelCase			:				Tuple      =			["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
    UpperCAmelCase			:				str      =			[
        """generator""",
        """height""",
        """width""",
        """latents""",
        """guidance_scale""",
        """negative_prompt""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    UpperCAmelCase			:				List[Any]      =			False






    @property
    def       __snake_case				(   self							:       Dict):
      return 32






    @property
    def       __snake_case				(   self							:       List[str]):
      return 32






    @property
    def       __snake_case				(   self							:       List[Any]):
      return self.time_input_dim






    @property
    def       __snake_case				(   self							:       List[str]):
      return self.time_input_dim * 4






    @property
    def       __snake_case				(   self							:       Optional[Any]):
      return 100






    @property
    def       __snake_case				(   self							:       str):
      a		:						List[str] 				=     XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base")
      return tokenizer






    @property
    def       __snake_case				(   self							:       Tuple):
      torch.manual_seed(0)
      a		:						Dict 				=     MCLIPConfig(
          numDims=self.cross_attention_dim							,					transformerDimensions=self.text_embedder_hidden_size							,					hidden_size=self.text_embedder_hidden_size							,					intermediate_size=37							,					num_attention_heads=4							,					num_hidden_layers=5							,					vocab_size=1005							,					)

      a		:						Optional[Any] 				=     MultilingualCLIP(__UpperCAmelCase)
      a		:						str 				=     text_encoder.eval()

      return text_encoder






    @property
    def       __snake_case				(   self							:       List[Any]):
      torch.manual_seed(0)

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

      a		:						Any 				=     UNetaDConditionModel(**__UpperCAmelCase)
      return model






    @property
    def       __snake_case				(   self							:       str):
      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       __snake_case				(   self							:       Any):
      torch.manual_seed(0)
      a		:						Union[str, Any] 				=     VQModel(**self.dummy_movq_kwargs)
      return model






    def       __snake_case				(   self							:       Optional[Any]):
      a		:						Tuple 				=     self.dummy_text_encoder
      a		:						List[Any] 				=     self.dummy_tokenizer
      a		:						str 				=     self.dummy_unet
      a		:						Optional[Any] 				=     self.dummy_movq

      a		:						List[str] 				=     DDIMScheduler(
          num_train_timesteps=1000							,					beta_schedule="linear"							,					beta_start=0.00_085							,					beta_end=0.012							,					clip_sample=__UpperCAmelCase							,					set_alpha_to_one=__UpperCAmelCase							,					steps_offset=1							,					prediction_type="epsilon"							,					thresholding=__UpperCAmelCase							,					)

      a		:						int 				=     {
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }
      return components






    def       __snake_case				(   self							:       List[Any]							,					__UpperCAmelCase							:       int							,					__UpperCAmelCase							:       Optional[int]=0):
      a		:						Optional[int] 				=     floats_tensor((1, self.cross_attention_dim)							,					rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)
      a		:						Any 				=     floats_tensor((1, self.cross_attention_dim)							,					rng=random.Random(seed + 1)).to(__UpperCAmelCase)
      if str(__UpperCAmelCase).startswith("mps"):
        a		:						Union[str, Any] 				=     torch.manual_seed(__UpperCAmelCase)
      else:
        a		:						Optional[int] 				=     torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
      a		:						Optional[Any] 				=     {
          "prompt": "horse",
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "generator": generator,
          "height": 64,
          "width": 64,
          "guidance_scale": 4.0,
          "num_inference_steps": 2,
          "output_type": "np",
      }
      return inputs






    def       __snake_case				(   self							:       Tuple):
      a		:						List[str] 				=     "cpu"

      a		:						Optional[int] 				=     self.get_dummy_components()

      a		:						str 				=     self.pipeline_class(**__UpperCAmelCase)
      a		:						Union[str, Any] 				=     pipe.to(__UpperCAmelCase)

      pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a		:						Optional[Any] 				=     pipe(**self.get_dummy_inputs(__UpperCAmelCase))
      a		:						Dict 				=     output.images

      a		:						Any 				=     pipe(
          **self.get_dummy_inputs(__UpperCAmelCase)							,					return_dict=__UpperCAmelCase							,					)[0]

      a		:						Optional[int] 				=     image[0, -3:, -3:, -1]
      a		:						Optional[Any] 				=     image_from_tuple[0, -3:, -3:, -1]

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

      a		:						List[str] 				=     np.array(
          [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953])

      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 		_A    (    unittest.TestCase    ):







    """simple docstring"""




    def       __snake_case				(   self							:       List[str]):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def       __snake_case				(   self							:       str):
      a		:						Union[str, Any] 				=     load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinsky/kandinsky_text2img_cat_fp16.npy")

      a		:						List[Any] 				=     KandinskyPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-1-prior"							,					torch_dtype=torch.floataa)
      pipe_prior.to(__UpperCAmelCase)

      a		:						List[str] 				=     KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"							,					torch_dtype=torch.floataa)
      a		:						Optional[int] 				=     pipeline.to(__UpperCAmelCase)
      pipeline.set_progress_bar_config(disable=__UpperCAmelCase)

      a		:						Optional[Any] 				=     "red cat, 4k photo"

      a		:						Dict 				=     torch.Generator(device="cuda").manual_seed(0)
      a				,						a		:						List[str] 				=     pipe_prior(
          __UpperCAmelCase							,					generator=__UpperCAmelCase							,					num_inference_steps=5							,					negative_prompt=""							,					).to_tuple()

      a		:						Any 				=     torch.Generator(device="cuda").manual_seed(0)
      a		:						Dict 				=     pipeline(
          __UpperCAmelCase							,					image_embeds=__UpperCAmelCase							,					negative_image_embeds=__UpperCAmelCase							,					generator=__UpperCAmelCase							,					num_inference_steps=100							,					output_type="np"							,					)

      a		:						List[str] 				=     output.images[0]

      assert image.shape == (512, 512, 3)

      assert_mean_pixel_difference(__UpperCAmelCase							,					__UpperCAmelCase)










"""simple docstring"""







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


__lowercase          = logging.getLogger(__name__)






@dataclass
class 		_A    :







    """simple docstring"""




    UpperCAmelCase			:				str      =			field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    )
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
    UpperCAmelCase			:				bool      =			field(default=_a       ,metadata={"""help""": """Set this flag to use fast tokenization."""}    )
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,)






def 	lowercase     (							)-> Dict:
  '''simple docstring'''







  a		:						Tuple 				=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)

  if len(sys.argv			) == 2 and sys.argv[1].endswith(".json"			):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    a				,						a				,						a		:						Any 				=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
  else:
    a				,						a				,						a		:						List[str] 				=     parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir			)
      and os.listdir(training_args.output_dir			)
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
        " --overwrite_output_dir to overcome."			)

  # Setup logging
  logging.basicConfig(
      format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,  datefmt="%m/%d/%Y %H:%M:%S"    ,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,  )
  logger.warning(
      "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"    ,  training_args.local_rank    ,  training_args.device    ,  training_args.n_gpu    ,  bool(training_args.local_rank != -1			)    ,  training_args.fpaa    ,  )
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank			):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info("Training/evaluation parameters %s"    ,  A_			)

  # Prepare Question-Answering task
  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  a		:						str 				=     AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,  cache_dir=model_args.cache_dir    ,  )
  a		:						str 				=     AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,  cache_dir=model_args.cache_dir    ,  use_fast=A_    ,  )
  a		:						List[Any] 				=     AutoModelForQuestionAnswering.from_pretrained(
      model_args.model_name_or_path    ,  from_tf=bool(".ckpt" in model_args.model_name_or_path			)    ,  config=A_    ,  cache_dir=model_args.cache_dir    ,  )

  # Get datasets
  a		:						int 				=     hasattr(model.config    ,  "lang2id"			)
  a		:						Any 				=     (
      SquadDataset(
          A_    ,  tokenizer=A_    ,  is_language_sensitive=A_    ,  cache_dir=model_args.cache_dir			)
      if training_args.do_train
      else None
  )
  a		:						Tuple 				=     (
      SquadDataset(
          A_    ,  tokenizer=A_    ,  mode="dev"    ,  is_language_sensitive=A_    ,  cache_dir=model_args.cache_dir    ,  )
      if training_args.do_eval
      else None
  )

  # Data collator
  a		:						str 				=     DataCollatorWithPadding(A_    ,  pad_to_multiple_of=8			) if training_args.fpaa else None

  # Initialize our Trainer
  a		:						Tuple 				=     Trainer(
      model=A_    ,  args=A_    ,  train_dataset=A_    ,  eval_dataset=A_    ,  data_collator=A_    ,  )

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_master():
      tokenizer.save_pretrained(training_args.output_dir			)


def 	lowercase     (							A_			)-> int:
  '''simple docstring'''







  main()


if __name__ == "__main__":
 main()






