






"""simple docstring"""


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, 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


enable_full_determinism()






class 						snake_case  (  unittest.TestCase	):
    __magic_name__   					=					StableDiffusionLDMaDPipeline
    __magic_name__   					=					TEXT_TO_IMAGE_PARAMS
    __magic_name__   					=					TEXT_TO_IMAGE_BATCH_PARAMS
    __magic_name__   					=					TEXT_TO_IMAGE_IMAGE_PARAMS





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      torch.manual_seed(0      )
      a					:      str    =       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')    ,		cross_attention_dim=3_2    ,		)
      a					:      Tuple    =       DDIMScheduler(
          beta_start=0.0_00_85    ,		beta_end=0.0_12    ,		beta_schedule='scaled_linear'    ,		clip_sample=A    ,		set_alpha_to_one=A    ,		)
      torch.manual_seed(0      )
      a					:      Dict    =       AutoencoderKL(
          block_out_channels=[3_2, 6_4]    ,		in_channels=6    ,		out_channels=6    ,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,		latent_channels=4    ,		)
      torch.manual_seed(0      )
      a					:      Tuple    =       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    ,		)
      a					:      Optional[int]    =       CLIPTextModel(A      )
      a					:      List[str]    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

      a					:      Optional[Any]    =       {
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'safety_checker': None,
          'feature_extractor': None,
      }
      return components





    def     lowerCamelCase__				(							self     : Tuple    ,		A     : int    ,		A     : int=0      ):


      '''simple docstring'''




      if str(A      ).startswith('mps'      ):
        a					:      Optional[int]    =       torch.manual_seed(A      )
      else:
        a					:      Optional[int]    =       torch.Generator(device=A      ).manual_seed(A      )
      a					:      Optional[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     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      a					:      Tuple    =       'cpu'  # ensure determinism for the device-dependent torch.Generator

      a					:      Optional[Any]    =       self.get_dummy_components()
      a					:      List[Any]    =       StableDiffusionLDMaDPipeline(**A      )
      a					:      List[Any]    =       ldmad_pipe.to(A      )
      ldmad_pipe.set_progress_bar_config(disable=A      )

      a					:      Optional[int]    =       self.get_dummy_inputs(A      )
      a					:      Union[str, Any]    =       ldmad_pipe(**A      )
      a					:      int    =       output.rgb, output.depth

      a					:      Any    =       rgb[0, -3:, -3:, -1]
      a					:      List[Any]    =       depth[0, -3:, -1]

      assert rgb.shape == (1, 6_4, 6_4, 3)
      assert depth.shape == (1, 6_4, 6_4)

      a					:      Union[str, Any]    =       np.array(
          [0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62]      )
      a					:      Union[str, Any]    =       np.array([1_0_3.4_6_7_2_7, 85.81_20_04, 87.84_92_36]      )

      assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb      ).max() < 1E-2
      assert np.abs(image_slice_depth.flatten() - expected_slice_depth      ).max() < 1E-2





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       self.get_dummy_components()
      a					:      Dict    =       StableDiffusionLDMaDPipeline(**A      )
      a					:      Dict    =       ldmad_pipe.to(A      )
      ldmad_pipe.set_progress_bar_config(disable=A      )

      a					:      Dict    =       self.get_dummy_inputs(A      )
      a					:      Tuple    =       3 * [inputs['prompt']]

      # forward
      a					:      Dict    =       ldmad_pipe(**A      )
      a					:      Optional[int]    =       output.rgb, output.depth
      a					:      Tuple    =       rgb_slice_a[0, -3:, -3:, -1]
      a					:      Dict    =       depth_slice_a[0, -3:, -1]

      a					:      str    =       self.get_dummy_inputs(A      )
      a					:      str    =       3 * [inputs.pop('prompt'      )]

      a					:      Any    =       ldmad_pipe.tokenizer(
          A    ,		padding='max_length'    ,		max_length=ldmad_pipe.tokenizer.model_max_length    ,		truncation=A    ,		return_tensors='pt'    ,		)
      a					:      str    =       text_inputs['input_ids'].to(A      )

      a					:      Any    =       ldmad_pipe.text_encoder(A      )[0]

      a					:      List[str]    =       prompt_embeds

      # forward
      a					:      Optional[Any]    =       ldmad_pipe(**A      )
      a					:      int    =       output.rgb, output.depth
      a					:      Any    =       rgb_slice_a[0, -3:, -3:, -1]
      a					:      Dict    =       depth_slice_a[0, -3:, -1]

      assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()      ).max() < 1E-4
      assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()      ).max() < 1E-4





    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
      a					:      Union[str, Any]    =       self.get_dummy_components()
      a					:      Optional[Any]    =       PNDMScheduler(skip_prk_steps=A      )
      a					:      Tuple    =       StableDiffusionLDMaDPipeline(**A      )
      a					:      Optional[int]    =       ldmad_pipe.to(A      )
      ldmad_pipe.set_progress_bar_config(disable=A      )

      a					:      Union[str, Any]    =       self.get_dummy_inputs(A      )
      a					:      int    =       'french fries'
      a					:      Union[str, Any]    =       ldmad_pipe(**A    ,		negative_prompt=A      )

      a					:      int    =       output.rgb, output.depth
      a					:      Optional[Any]    =       rgb[0, -3:, -3:, -1]
      a					:      str    =       depth[0, -3:, -1]

      assert rgb.shape == (1, 6_4, 6_4, 3)
      assert depth.shape == (1, 6_4, 6_4)

      a					:      Tuple    =       np.array(
          [0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17]      )
      a					:      int    =       np.array([1_0_7.8_4_7_3_8, 84.6_28_02, 89.96_21_35]      )
      assert np.abs(rgb_slice.flatten() - expected_slice_rgb      ).max() < 1E-2
      assert np.abs(depth_slice.flatten() - expected_slice_depth      ).max() < 1E-2






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





    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Tuple    ,		A     : List[str]    ,		A     : List[str]="cpu"    ,		A     : Optional[Any]=torch.floataa    ,		A     : Tuple=0      ):


      '''simple docstring'''




      a					:      Tuple    =       torch.Generator(device=A      ).manual_seed(A      )
      a					:      Any    =       np.random.RandomState(A      ).standard_normal((1, 4, 6_4, 6_4)      )
      a					:      List[str]    =       torch.from_numpy(A      ).to(device=A    ,		dtype=A      )
      a					:      str    =       {
          '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     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      a					:      List[str]    =       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'      )
      a					:      int    =       ldmad_pipe.to(A      )
      ldmad_pipe.set_progress_bar_config(disable=A      )

      a					:      Tuple    =       self.get_inputs(A      )
      a					:      Tuple    =       ldmad_pipe(**A      )
      a					:      Optional[Any]    =       output.rgb, output.depth
      a					:      int    =       rgb[0, -3:, -3:, -1].flatten()
      a					:      str    =       rgb[0, -3:, -1].flatten()

      assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
      assert depth.shape == (1, 5_1_2, 5_1_2)

      a					:      int    =       np.array(
          [0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06]      )
      a					:      Tuple    =       np.array(
          [0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06]      )
      assert np.abs(rgb_slice - expected_slice_rgb      ).max() < 3E-3
      assert np.abs(depth_slice - expected_slice_depth      ).max() < 3E-3






@nightly
@require_torch_gpu
class 						snake_case  (  unittest.TestCase	):





    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : List[str]    ,		A     : List[str]    ,		A     : Tuple="cpu"    ,		A     : Optional[Any]=torch.floataa    ,		A     : int=0      ):


      '''simple docstring'''




      a					:      str    =       torch.Generator(device=A      ).manual_seed(A      )
      a					:      Optional[int]    =       np.random.RandomState(A      ).standard_normal((1, 4, 6_4, 6_4)      )
      a					:      Optional[Any]    =       torch.from_numpy(A      ).to(device=A    ,		dtype=A      )
      a					:      List[Any]    =       {
          'prompt': 'a photograph of an astronaut riding a horse',
          'latents': latents,
          'generator': generator,
          'num_inference_steps': 5_0,
          'guidance_scale': 7.5,
          'output_type': 'numpy',
      }
      return inputs





    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      a					:      List[str]    =       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'      ).to(A      )
      ldmad_pipe.set_progress_bar_config(disable=A      )

      a					:      Union[str, Any]    =       self.get_inputs(A      )
      a					:      Optional[int]    =       ldmad_pipe(**A      )
      a					:      Union[str, Any]    =       output.rgb, output.depth

      a					:      str    =       0.49_55_86
      a					:      Optional[Any]    =       0.33_79_55_15
      a					:      List[str]    =       1_1_2.4_8_5_1_8
      a					:      str    =       98.48_97_46
      assert np.abs(expected_rgb_mean - rgb.mean()      ) < 1E-3
      assert np.abs(expected_rgb_std - rgb.std()      ) < 1E-3
      assert np.abs(expected_depth_mean - depth.mean()      ) < 1E-3
      assert np.abs(expected_depth_std - depth.std()      ) < 1E-3





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Optional[int]    =       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c'      ).to(A      )
      ldmad_pipe.set_progress_bar_config(disable=A      )

      a					:      List[Any]    =       self.get_inputs(A      )
      a					:      Optional[Any]    =       ldmad_pipe(**A      )
      a					:      Dict    =       output.rgb, output.depth

      a					:      str    =       0.4_19_41_27
      a					:      Optional[int]    =       0.35_37_55_86
      a					:      Tuple    =       0.5_63_85_02
      a					:      Any    =       0.34_68_61_03

      assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
      assert depth.shape == (1, 5_1_2, 5_1_2, 1)
      assert np.abs(expected_rgb_mean - rgb.mean()      ) < 1E-3
      assert np.abs(expected_rgb_std - rgb.std()      ) < 1E-3
      assert np.abs(expected_depth_mean - depth.mean()      ) < 1E-3
      assert np.abs(expected_depth_std - depth.std()      ) < 1E-3












"""simple docstring"""


import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase    :   int   			=						get_tests_dir('fixtures/test_sentencepiece_bpe.model')






class 						snake_case  (  UpperCAmelCase       ,			unittest.TestCase	):
    __magic_name__   					=					BartphoTokenizer
    __magic_name__   					=					False
    __magic_name__   					=					True





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      super().setUp()

      a					:      Any    =       ['▁This', '▁is', '▁a', '▁t', 'est']
      a					:      List[Any]    =       dict(zip(A    ,		range(len(A      )      )      )      )
      a					:      int    =       {'unk_token': '<unk>'}

      a					:      Tuple    =       os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['monolingual_vocab_file']      )
      with open(self.monolingual_vocab_file    ,		'w'    ,		encoding='utf-8'      ) as fp:
        for token in vocab_tokens:
          fp.write(F'''{token} {vocab_tokens[token]}\n'''      )

      a					:      Optional[int]    =       BartphoTokenizer(A    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map      )
      tokenizer.save_pretrained(self.tmpdirname      )





    def     lowerCamelCase__				(							self     : Dict    ,		**A     : str      ):


      '''simple docstring'''




      kwargs.update(self.special_tokens_map      )
      return BartphoTokenizer.from_pretrained(self.tmpdirname    ,		**A      )





    def     lowerCamelCase__				(							self     : Optional[int]    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      Tuple    =       'This is a là test'
      a					:      List[Any]    =       'This is a<unk><unk> test'
      return input_text, output_text





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Tuple    =       BartphoTokenizer(A    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map      )
      a					:      int    =       'This is a là test'
      a					:      int    =       '▁This ▁is ▁a ▁l à ▁t est'.split()
      a					:      str    =       tokenizer.tokenize(A      )
      self.assertListEqual(A    ,		A      )

      a					:      Union[str, Any]    =       tokens + [tokenizer.unk_token]
      a					:      Dict    =       [4, 5, 6, 3, 3, 7, 8, 3]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A      )    ,		A      )




