
import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


__magic_name__            =  logging.get_logger(__name__)

enable_full_determinism()




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					__UpperCamelCase       ,					unittest.TestCase ):
      _A						:		Optional[Any]    	=     UNetaDModel
      _A						:		Optional[int]    	=     'sample'
      @property
      def A_			(			self ):
          snake_case__						=				4
          snake_case__						=				3
          snake_case__						=				(32, 32)

          snake_case__						=				floats_tensor((batch_size, num_channels) + sizes ).to(lowerCamelCase )
          snake_case__						=				torch.tensor([10] ).to(lowerCamelCase )

          return {"sample": noise, "timestep": time_step}
      @property
      def A_			(			self ):
          return (3, 32, 32)
      @property
      def A_			(			self ):
          return (3, 32, 32)



      def A_			(			self ):
          snake_case__						=				{
              "block_out_channels": (32, 64),
              "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
              "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
              "attention_head_dim": 3,
              "out_channels": 3,
              "in_channels": 3,
              "layers_per_block": 2,
              "sample_size": 32,
          }
          snake_case__						=				self.dummy_input
          return init_dict, inputs_dict





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					__UpperCamelCase       ,					unittest.TestCase ):
      _A						:		str    	=     UNetaDModel
      _A						:		List[Any]    	=     'sample'
      @property
      def A_			(			self ):
          snake_case__						=				4
          snake_case__						=				4
          snake_case__						=				(32, 32)

          snake_case__						=				floats_tensor((batch_size, num_channels) + sizes ).to(lowerCamelCase )
          snake_case__						=				torch.tensor([10] ).to(lowerCamelCase )

          return {"sample": noise, "timestep": time_step}
      @property
      def A_			(			self ):
          return (4, 32, 32)
      @property
      def A_			(			self ):
          return (4, 32, 32)
      def A_			(			self ):
          snake_case__						=				{
              "sample_size": 32,
              "in_channels": 4,
              "out_channels": 4,
              "layers_per_block": 2,
              "block_out_channels": (32, 64),
              "attention_head_dim": 32,
              "down_block_types": ("DownBlock2D", "DownBlock2D"),
              "up_block_types": ("UpBlock2D", "UpBlock2D"),
          }
          snake_case__						=				self.dummy_input
          return init_dict, inputs_dict
      def A_			(			self ):
          snake_case__						,					snake_case__						=				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"		,	output_loading_info=lowerCamelCase )

          self.assertIsNotNone(lowerCamelCase )
          self.assertEqual(len(loading_info["missing_keys"] )		,	0 )

          model.to(lowerCamelCase )
          snake_case__						=				model(**self.dummy_input ).sample

          assert image is not None, "Make sure output is not None"
      @unittest.skipIf(torch_device != "cuda"		,	"This test is supposed to run on GPU" )
      def A_			(			self ):
          snake_case__						,					snake_case__						=				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"		,	output_loading_info=lowerCamelCase )
          model.to(lowerCamelCase )
          snake_case__						=				model(**self.dummy_input ).sample

          assert image is not None, "Make sure output is not None"
      @unittest.skipIf(torch_device != "cuda"		,	"This test is supposed to run on GPU" )
      def A_			(			self ):
          # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
          snake_case__						,					snake_case__						=				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"		,	output_loading_info=lowerCamelCase )
          model_accelerate.to(lowerCamelCase )
          model_accelerate.eval()

          snake_case__						=				torch.randn(
              1		,	model_accelerate.config.in_channels		,	model_accelerate.config.sample_size		,	model_accelerate.config.sample_size		,	generator=torch.manual_seed(0 )		,	)
          snake_case__						=				noise.to(lowerCamelCase )
          snake_case__						=				torch.tensor([10] * noise.shape[0] ).to(lowerCamelCase )

          snake_case__						=				model_accelerate(lowerCamelCase		,	lowerCamelCase )["sample"]

          # two models don't need to stay in the device at the same time
          del model_accelerate
          torch.cuda.empty_cache()
          gc.collect()

          snake_case__						,					snake_case__						=				UNetaDModel.from_pretrained(
              "fusing/unet-ldm-dummy-update"		,	output_loading_info=lowerCamelCase		,	low_cpu_mem_usage=lowerCamelCase )
          model_normal_load.to(lowerCamelCase )
          model_normal_load.eval()
          snake_case__						=				model_normal_load(lowerCamelCase		,	lowerCamelCase )["sample"]

          assert torch_all_close(lowerCamelCase		,	lowerCamelCase		,	rtol=1e-3 )



      def A_			(			self ):
          snake_case__						=				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" )
          model.eval()
          model.to(lowerCamelCase )

          snake_case__						=				torch.randn(
              1		,	model.config.in_channels		,	model.config.sample_size		,	model.config.sample_size		,	generator=torch.manual_seed(0 )		,	)
          snake_case__						=				noise.to(lowerCamelCase )
          snake_case__						=				torch.tensor([10] * noise.shape[0] ).to(lowerCamelCase )

          with torch.no_grad():
              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase ).sample

          snake_case__						=				output[0, -1, -3:, -3:].flatten().cpu()
          # fmt: off
          snake_case__						=				torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0] )
          # fmt: on

          self.assertTrue(torch_all_close(lowerCamelCase		,	lowerCamelCase		,	rtol=1e-3 ) )





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					__UpperCamelCase       ,					unittest.TestCase ):
      _A						:		List[Any]    	=     UNetaDModel
      _A						:		Optional[int]    	=     'sample'
      @property
      def A_			(			self		,	lowerCamelCase=(32, 32) ):
          snake_case__						=				4
          snake_case__						=				3

          snake_case__						=				floats_tensor((batch_size, num_channels) + sizes ).to(lowerCamelCase )
          snake_case__						=				torch.tensor(batch_size * [10] ).to(dtype=torch.intaa		,	device=lowerCamelCase )

          return {"sample": noise, "timestep": time_step}
      @property
      def A_			(			self ):
          return (3, 32, 32)
      @property
      def A_			(			self ):
          return (3, 32, 32)
      def A_			(			self ):
          snake_case__						=				{
              "block_out_channels": [32, 64, 64, 64],
              "in_channels": 3,
              "layers_per_block": 1,
              "out_channels": 3,
              "time_embedding_type": "fourier",
              "norm_eps": 1e-6,
              "mid_block_scale_factor": math.sqrt(2.0 ),
              "norm_num_groups": None,
              "down_block_types": [
                  "SkipDownBlock2D",
                  "AttnSkipDownBlock2D",
                  "SkipDownBlock2D",
                  "SkipDownBlock2D",
              ],
              "up_block_types": [
                  "SkipUpBlock2D",
                  "SkipUpBlock2D",
                  "AttnSkipUpBlock2D",
                  "SkipUpBlock2D",
              ],
          }
          snake_case__						=				self.dummy_input
          return init_dict, inputs_dict
      @slow
      def A_			(			self ):
          snake_case__						,					snake_case__						=				UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"		,	output_loading_info=lowerCamelCase )
          self.assertIsNotNone(lowerCamelCase )
          self.assertEqual(len(loading_info["missing_keys"] )		,	0 )

          model.to(lowerCamelCase )
          snake_case__						=				self.dummy_input
          snake_case__						=				floats_tensor((4, 3) + (2_56, 2_56) ).to(lowerCamelCase )
          snake_case__						=				noise
          snake_case__						=				model(**lowerCamelCase )

          assert image is not None, "Make sure output is not None"
      @slow
      def A_			(			self ):
          snake_case__						=				UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" )
          model.to(lowerCamelCase )

          snake_case__						=				4
          snake_case__						=				3
          snake_case__						=				(2_56, 2_56)

          snake_case__						=				torch.ones((batch_size, num_channels) + sizes ).to(lowerCamelCase )
          snake_case__						=				torch.tensor(batch_size * [1e-4] ).to(lowerCamelCase )

          with torch.no_grad():
              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase ).sample

          snake_case__						=				output[0, -3:, -3:, -1].flatten().cpu()
          # fmt: off
          snake_case__						=				torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8] )
          # fmt: on

          self.assertTrue(torch_all_close(lowerCamelCase		,	lowerCamelCase		,	rtol=1e-2 ) )
      def A_			(			self ):
          snake_case__						=				UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update" )
          model.to(lowerCamelCase )

          snake_case__						=				4
          snake_case__						=				3
          snake_case__						=				(32, 32)

          snake_case__						=				torch.ones((batch_size, num_channels) + sizes ).to(lowerCamelCase )
          snake_case__						=				torch.tensor(batch_size * [1e-4] ).to(lowerCamelCase )

          with torch.no_grad():
              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase ).sample

          snake_case__						=				output[0, -3:, -3:, -1].flatten().cpu()
          # fmt: off
          snake_case__						=				torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6] )
          # fmt: on

          self.assertTrue(torch_all_close(lowerCamelCase		,	lowerCamelCase		,	rtol=1e-2 ) )



      def A_			(			self ):
          # not required for this model
          pass





import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

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


__magic_name__            =  logging.get_logger(__name__)

__magic_name__            =  {'''vocab_file''': '''sentencepiece.model'''}

__magic_name__            =  {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

__magic_name__            =  {
    '''google/rembert''': 256,
}




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Optional[Any]    	=     VOCAB_FILES_NAMES
      _A						:		Optional[int]    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		Any    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=False		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[UNK]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[PAD]"		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[MASK]"		,	**lowerCamelCase		,	):
          super().__init__(
              do_lower_case=lowerCamelCase		,	remove_space=lowerCamelCase		,	keep_accents=lowerCamelCase		,	bos_token=lowerCamelCase		,	eos_token=lowerCamelCase		,	unk_token=lowerCamelCase		,	sep_token=lowerCamelCase		,	pad_token=lowerCamelCase		,	cls_token=lowerCamelCase		,	mask_token=lowerCamelCase		,	**lowerCamelCase		,	)

          snake_case__						=				do_lower_case
          snake_case__						=				remove_space
          snake_case__						=				keep_accents
          snake_case__						=				vocab_file

          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(lowerCamelCase )
      @property
      def A_			(			self ):
          return len(self.sp_model )
      def A_			(			self ):
          snake_case__						=				{self.convert_ids_to_tokens(lowerCamelCase ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab
      def __getstate__(			self ):
          snake_case__						=				self.__dict__.copy()
          snake_case__						=				None
          return state
      def __setstate__(			self		,	lowerCamelCase ):
          snake_case__						=				d
          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(self.vocab_file )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=False ):
          snake_case__						=				self.sp_model.EncodeAsPieces(lowerCamelCase )
          return pieces
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.PieceToId(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.IdToPiece(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				self.sp_model.decode_pieces(lowerCamelCase )
          return out_string
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          snake_case__						=				[self.sep_token_id]
          snake_case__						=				[self.cls_token_id]
          if token_ids_a is None:
              return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = False ):

          if already_has_special_tokens:
              if token_ids_a is not None:
                  raise ValueError(
                      "You should not supply a second sequence if the provided sequence of "
                      "ids is already formatted with special tokens for the model." )
              return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is not None:
              return [1] + ([0] * len(lowerCamelCase )) + [1] + ([0] * len(lowerCamelCase )) + [1]
          return [1] + ([0] * len(lowerCamelCase )) + [1]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          snake_case__						=				[self.sep_token_id]
          snake_case__						=				[self.cls_token_id]

          if token_ids_a is None:
              return len(cls + token_ids_a + sep ) * [0]
          return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]



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

          if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase ):
              copyfile(self.vocab_file		,	lowerCamelCase )

          return (out_vocab_file,)



