




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 		lowercase							(     unittest.TestCase	):

     @property
     def       a__			(						self       )		->							int:
           torch.manual_seed(0       )
           _A							:  int    =		UNetaDModel(
               block_out_channels=(32, 64)	,  layers_per_block=2	,  sample_size=32	,  in_channels=3	,  out_channels=3	,  down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")	,  up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")	,  )
           return model



     def       a__			(						self       )		->							Dict:
           _A							:  List[str]    =		self.dummy_uncond_unet
           _A							:  Tuple    =		KarrasVeScheduler()

           _A							:  Any    =		KarrasVePipeline(unet=_a	,  scheduler=_a       )
           pipe.to(_a       )
           pipe.set_progress_bar_config(disable=_a       )

           _A							:  Dict    =		torch.manual_seed(0       )
           _A							:  Optional[Any]    =		pipe(num_inference_steps=2	,  generator=_a	,  output_type="""numpy"""       ).images

           _A							:  Optional[Any]    =		torch.manual_seed(0       )
           _A							:  Tuple    =		pipe(num_inference_steps=2	,  generator=_a	,  output_type="""numpy"""	,  return_dict=_a       )[0]

           _A							:  Tuple    =		image[0, -3:, -3:, -1]
           _A							:  Union[str, Any]    =		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           _A							:  Dict    =		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

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







@slow
@require_torch
class 		lowercase							(     unittest.TestCase	):

     def       a__			(						self       )		->							Tuple:
           _A							:  int    =		"""google/ncsnpp-celebahq-256"""
           _A							:  str    =		UNetaDModel.from_pretrained(_a       )
           _A							:  int    =		KarrasVeScheduler()

           _A							:  str    =		KarrasVePipeline(unet=_a	,  scheduler=_a       )
           pipe.to(_a       )
           pipe.set_progress_bar_config(disable=_a       )

           _A							:  int    =		torch.manual_seed(0       )
           _A							:  Tuple    =		pipe(num_inference_steps=20	,  generator=_a	,  output_type="""numpy"""       ).images

           _A							:  List[Any]    =		image[0, -3:, -3:, -1]
           assert image.shape == (1, 256, 256, 3)
           _A							:  Optional[Any]    =		np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]       )

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







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class 		lowercase							(     tf.keras.layers.Layer	):

     def __init__(						self	,  _a	,  _a	,  _a = None	,  _a = None       )		->							Any:
           super().__init__()
           _A							:  Dict    =		pad_token_id
           _A							:  List[Any]    =		max_length
           _A							:  Optional[int]    =		vocab
           _A							:  Optional[int]    =		merges
           _A							:  Optional[int]    =		BytePairTokenizer(_a	,  _a	,  sequence_length=_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							str:
           _A							:  Any    =		[""" """.join(_a       ) for m in tokenizer.bpe_ranks.keys()]
           _A							:  str    =		tokenizer.get_vocab()
           return cls(_a	,  _a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							List[Any]:
           _A							:  Union[str, Any]    =		GPTaTokenizer.from_pretrained(_a	,  *_a	,  **_a       )
           return cls.from_tokenizer(_a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a       )		->							Union[str, Any]:
           return cls(**_a       )

     def       a__			(						self       )		->							Union[str, Any]:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }



     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  Optional[int]    =		self.tf_tokenizer(_a       )
           _A							:  Tuple    =		tf.ones_like(_a       )

           if self.pad_token_id is not None:
                 # pad the tokens up to max length
                 _A							:  Dict    =		max_length if max_length is not None else self.max_length

                 if max_length is not None:
                       _A    ,     _A							:  Dict    =		pad_model_inputs(
                           _a	,  max_seq_length=_a	,  pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}

