

"""simple docstring"""






import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		MgpstrTokenizer
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		{}
  _UpperCAmelCase          =		False

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().setUp()

      # fmt: off
      _lowerCAmelCase       :   List[Any]  =    ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
      # fmt: on
      _lowerCAmelCase       :   List[str]  =    dict(zip(_A    ,range(len(_A      )      )      )      )

      _lowerCAmelCase       :   Optional[Any]  =    os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']      )
      with open(self.vocab_file    ,'w'    ,encoding='utf-8'      ) as fp:
          fp.write(json.dumps(_A      ) + '\n'      )

  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      return MgpstrTokenizer.from_pretrained(self.tmpdirname    ,**_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    'tester'
      _lowerCAmelCase       :   Tuple  =    'tester'
      return input_text, output_text

  @unittest.skip('MGP-STR always lower cases letters.'      )
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      pass

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.get_tokenizers(do_lower_case=_A      )
      for tokenizer in tokenizers:
          with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
              _lowerCAmelCase       :   List[str]  =    '[SPECIAL_TOKEN]'

              tokenizer.add_special_tokens({'cls_token': special_token}      )
              _lowerCAmelCase       :   Dict  =    tokenizer.encode([special_token]    ,add_special_tokens=_A      )
              self.assertEqual(len(_A      )    ,1      )

              _lowerCAmelCase       :   Dict  =    tokenizer.decode(_A    ,skip_special_tokens=_A      )
              self.assertTrue(special_token not in decoded      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    self.get_tokenizers()
      for tokenizer in tokenizers:
          with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
              _lowerCAmelCase       :   Tuple  =    self.get_input_output_texts(_A      )

              _lowerCAmelCase       :   Dict  =    tokenizer.tokenize(_A      )
              _lowerCAmelCase       :   List[Any]  =    tokenizer.convert_tokens_to_ids(_A      )
              _lowerCAmelCase       :   Union[str, Any]  =    tokenizer.encode(_A    ,add_special_tokens=_A      )
              self.assertListEqual(_A    ,_A      )

              _lowerCAmelCase       :   int  =    tokenizer.convert_ids_to_tokens(_A      )
              self.assertNotEqual(len(_A      )    ,0      )
              _lowerCAmelCase       :   int  =    tokenizer.decode(_A      )
              self.assertIsInstance(_A    ,_A      )

              self.assertEqual(text_a.replace(' '    ,''      )    ,_A      )

  @unittest.skip('MGP-STR tokenizer only handles one sequence.'      )
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      pass







  @unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'      )
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      pass








"""simple docstring"""






import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		LDMTextToImagePipeline
  _UpperCAmelCase          =		TEXT_TO_IMAGE_PARAMS - {
      "negative_prompt",
      "negative_prompt_embeds",
      "cross_attention_kwargs",
      "prompt_embeds",
  }
  _UpperCAmelCase          =		PipelineTesterMixin.required_optional_params - {
      "num_images_per_prompt",
      "callback",
      "callback_steps",
  }
  _UpperCAmelCase          =		TEXT_TO_IMAGE_BATCH_PARAMS
  _UpperCAmelCase          =		False

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      torch.manual_seed(0      )
      _lowerCAmelCase       :   Optional[int]  =    UNetaDConditionModel(
          block_out_channels=(32, 64)    ,layers_per_block=2    ,sample_size=32    ,in_channels=4    ,out_channels=4    ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,cross_attention_dim=32    ,)
      _lowerCAmelCase       :   Union[str, Any]  =    DDIMScheduler(
          beta_start=0.0_0_0_8_5    ,beta_end=0.0_1_2    ,beta_schedule='scaled_linear'    ,clip_sample=_A    ,set_alpha_to_one=_A    ,)
      torch.manual_seed(0      )
      _lowerCAmelCase       :   Union[str, Any]  =    AutoencoderKL(
          block_out_channels=(32, 64)    ,in_channels=3    ,out_channels=3    ,down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')    ,up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')    ,latent_channels=4    ,)
      torch.manual_seed(0      )
      _lowerCAmelCase       :   Dict  =    CLIPTextConfig(
          bos_token_id=0    ,eos_token_id=2    ,hidden_size=32    ,intermediate_size=37    ,layer_norm_eps=1E-05    ,num_attention_heads=4    ,num_hidden_layers=5    ,pad_token_id=1    ,vocab_size=1000    ,)
      _lowerCAmelCase       :   Tuple  =    CLIPTextModel(_A      )
      _lowerCAmelCase       :   Any  =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

      _lowerCAmelCase       :   List[str]  =    {
          'unet': unet,
          'scheduler': scheduler,
          'vqvae': vae,
          'bert': text_encoder,
          'tokenizer': tokenizer,
      }
      return components

  def __lowerCamelCase		(		self    ,_A    ,_A=0      ):
      '''simple docstring'''

      if str(_A      ).startswith('mps'      ):
          _lowerCAmelCase       :   int  =    torch.manual_seed(_A      )
      else:
          _lowerCAmelCase       :   Optional[Any]  =    torch.Generator(device=_A      ).manual_seed(_A      )
      _lowerCAmelCase       :   List[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      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    'cpu'  # ensure determinism for the device-dependent torch.Generator

      _lowerCAmelCase       :   int  =    self.get_dummy_components()
      _lowerCAmelCase       :   str  =    LDMTextToImagePipeline(**_A      )
      pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   List[Any]  =    self.get_dummy_inputs(_A      )
      _lowerCAmelCase       :   Any  =    pipe(**_A      ).images
      _lowerCAmelCase       :   Optional[int]  =    image[0, -3:, -3:, -1]

      assert image.shape == (1, 16, 16, 3)
      _lowerCAmelCase       :   Tuple  =    np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3







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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self    ,_A    ,_A=torch.floataa    ,_A=0      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    torch.manual_seed(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    np.random.RandomState(_A      ).standard_normal((1, 4, 32, 32)      )
      _lowerCAmelCase       :   Optional[Any]  =    torch.from_numpy(_A      ).to(device=_A    ,dtype=_A      )
      _lowerCAmelCase       :   List[str]  =    {
          'prompt': 'A painting of a squirrel eating a burger',
          'latents': latents,
          'generator': generator,
          'num_inference_steps': 3,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }
      return inputs







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'      ).to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Optional[Any]  =    self.get_inputs(_A      )
      _lowerCAmelCase       :   List[Any]  =    pipe(**_A      ).images
      _lowerCAmelCase       :   Dict  =    image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 256, 256, 3)
      _lowerCAmelCase       :   str  =    np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8]      )
      _lowerCAmelCase       :   Dict  =    np.abs(expected_slice - image_slice      ).max()
      assert max_diff < 1E-3







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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self    ,_A    ,_A=torch.floataa    ,_A=0      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    torch.manual_seed(_A      )
      _lowerCAmelCase       :   Optional[int]  =    np.random.RandomState(_A      ).standard_normal((1, 4, 32, 32)      )
      _lowerCAmelCase       :   List[Any]  =    torch.from_numpy(_A      ).to(device=_A    ,dtype=_A      )
      _lowerCAmelCase       :   int  =    {
          'prompt': 'A painting of a squirrel eating a burger',
          'latents': latents,
          'generator': generator,
          'num_inference_steps': 50,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }
      return inputs







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'      ).to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   str  =    self.get_inputs(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    pipe(**_A      ).images[0]

      _lowerCAmelCase       :   int  =    load_numpy(
          'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'      )
      _lowerCAmelCase       :   List[str]  =    np.abs(expected_image - image      ).max()
      assert max_diff < 1E-3





