
"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


class A_	(UpperCamelCase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		ShapEImgaImgPipeline
  SCREAMING_SNAKE_CASE__			:						List[str]       =		["""image"""]
  SCREAMING_SNAKE_CASE__			:						int       =		["""image"""]
  SCREAMING_SNAKE_CASE__			:						Dict       =		[
      """num_images_per_prompt""",
      """num_inference_steps""",
      """generator""",
      """latents""",
      """guidance_scale""",
      """frame_size""",
      """output_type""",
      """return_dict""",
  ]
  SCREAMING_SNAKE_CASE__			:						Dict       =		False







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return 32







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return 32







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return self.time_input_dim * 4







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return 8







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     torch.manual_seed(0  )
     UpperCAmelCase_					:					Union[str, Any]			     =    CLIPVisionConfig(
         hidden_size=self.text_embedder_hidden_size				,   image_size=64				,   projection_dim=self.text_embedder_hidden_size				,   intermediate_size=37				,   num_attention_heads=4				,   num_channels=3				,   num_hidden_layers=5				,   patch_size=1				,   )

     UpperCAmelCase_					:					List[Any]			     =    CLIPVisionModel(_a  )
     return model







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    CLIPImageProcessor(
         crop_size=224				,   do_center_crop=_a				,   do_normalize=_a				,   do_resize=_a				,   image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]				,   image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]				,   resample=3				,   size=224				,   )

     return image_processor







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     torch.manual_seed(0  )

     UpperCAmelCase_					:					str			     =    {
         """num_attention_heads""": 2,
         """attention_head_dim""": 16,
         """embedding_dim""": self.time_input_dim,
         """num_embeddings""": 32,
         """embedding_proj_dim""": self.text_embedder_hidden_size,
         """time_embed_dim""": self.time_embed_dim,
         """num_layers""": 1,
         """clip_embed_dim""": self.time_input_dim * 2,
         """additional_embeddings""": 0,
         """time_embed_act_fn""": """gelu""",
         """norm_in_type""": """layer""",
         """embedding_proj_norm_type""": """layer""",
         """encoder_hid_proj_type""": None,
         """added_emb_type""": None,
     }

     UpperCAmelCase_					:					Optional[int]			     =    PriorTransformer(**_a  )
     return model







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     torch.manual_seed(0  )

     UpperCAmelCase_					:					List[str]			     =    {
         """param_shapes""": (
             (self.renderer_dim, 93),
             (self.renderer_dim, 8),
             (self.renderer_dim, 8),
             (self.renderer_dim, 8),
         ),
         """d_latent""": self.time_input_dim,
         """d_hidden""": self.renderer_dim,
         """n_output""": 12,
         """background""": (
             0.1,
             0.1,
             0.1,
         ),
     }
     UpperCAmelCase_					:					int			     =    ShapERenderer(**_a  )
     return model







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    self.dummy_prior
     UpperCAmelCase_					:					Tuple			     =    self.dummy_image_encoder
     UpperCAmelCase_					:					str			     =    self.dummy_image_processor
     UpperCAmelCase_					:					Any			     =    self.dummy_renderer

     UpperCAmelCase_					:					str			     =    HeunDiscreteScheduler(
         beta_schedule="exp"				,   num_train_timesteps=1024				,   prediction_type="sample"				,   use_karras_sigmas=_a				,   clip_sample=_a				,   clip_sample_range=1.0				,   )
     UpperCAmelCase_					:					Union[str, Any]			     =    {
         """prior""": prior,
         """image_encoder""": image_encoder,
         """image_processor""": image_processor,
         """renderer""": renderer,
         """scheduler""": scheduler,
     }

     return components







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_=0  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    floats_tensor((1, 3, 64, 64)				,   rng=random.Random(_a  )  ).to(_a  )

     if str(_a  ).startswith("mps"  ):
        UpperCAmelCase_					:					List[Any]			     =    torch.manual_seed(_a  )
     else:
        UpperCAmelCase_					:					Tuple			     =    torch.Generator(device=_a  ).manual_seed(_a  )
     UpperCAmelCase_					:					List[Any]			     =    {
         """image""": input_image,
         """generator""": generator,
         """num_inference_steps""": 1,
         """frame_size""": 32,
         """output_type""": """np""",
     }
     return inputs







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    """cpu"""

     UpperCAmelCase_					:					List[str]			     =    self.get_dummy_components()

     UpperCAmelCase_					:					str			     =    self.pipeline_class(**_a  )
     UpperCAmelCase_					:					Optional[int]			     =    pipe.to(_a  )

     pipe.set_progress_bar_config(disable=_a  )

     UpperCAmelCase_					:					Tuple			     =    pipe(**self.get_dummy_inputs(_a  )  )
     UpperCAmelCase_					:					Tuple			     =    output.images[0]
     UpperCAmelCase_					:					Dict			     =    image[0, -3:, -3:, -1]

     assert image.shape == (20, 32, 32, 3)

     UpperCAmelCase_					:					Dict			     =    np.array(
         [
             0.00_03_92_16,
             0.00_03_92_16,
             0.00_03_92_16,
             0.00_03_92_16,
             0.00_03_92_16,
             0.00_03_92_16,
             0.00_03_92_16,
             0.00_03_92_16,
             0.00_03_92_16,
         ]  )

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
     self._test_inference_batch_consistent(batch_sizes=[1, 2]  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    torch_device == """cpu"""
     UpperCAmelCase_					:					Union[str, Any]			     =    True
     self._test_inference_batch_single_identical(
         batch_size=2				,   test_max_difference=_a				,   relax_max_difference=_a				,   )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    self.get_dummy_components()
     UpperCAmelCase_					:					Optional[int]			     =    self.pipeline_class(**_a  )
     UpperCAmelCase_					:					int			     =    pipe.to(_a  )
     pipe.set_progress_bar_config(disable=_a  )

     UpperCAmelCase_					:					int			     =    1
     UpperCAmelCase_					:					List[str]			     =    2

     UpperCAmelCase_					:					str			     =    self.get_dummy_inputs(_a  )

     for key in inputs.keys():
        if key in self.batch_params:
           UpperCAmelCase_					:					str			     =    batch_size * [inputs[key]]

     UpperCAmelCase_					:					Union[str, Any]			     =    pipe(**_a				,   num_images_per_prompt=_a  )[0]

     assert images.shape[0] == batch_size * num_images_per_prompt




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




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png"  )
     UpperCAmelCase_					:					str			     =    load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/shap_e/test_shap_e_img2img_out.npy"  )
     UpperCAmelCase_					:					List[Any]			     =    ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img"  )
     UpperCAmelCase_					:					Union[str, Any]			     =    pipe.to(_a  )
     pipe.set_progress_bar_config(disable=_a  )

     UpperCAmelCase_					:					Tuple			     =    torch.Generator(device=_a  ).manual_seed(0  )

     UpperCAmelCase_					:					Optional[Any]			     =    pipe(
         _a				,   generator=_a				,   guidance_scale=3.0				,   num_inference_steps=64				,   frame_size=64				,   output_type="np"				,   ).images[0]

     assert images.shape == (20, 64, 64, 3)

     assert_mean_pixel_difference(_a				,   _a  )








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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_fnet import FNetTokenizer
else:
  _snake_case										=       None

_snake_case										=       logging.get_logger(__name__)
_snake_case										=       {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_snake_case										=       {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

_snake_case										=       {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

_snake_case										=       "▁"







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						VOCAB_FILES_NAMES
     _a							 =						PRETRAINED_VOCAB_FILES_MAP
     _a							 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _a							 =						["input_ids", "token_type_ids"]
     _a							 =						FNetTokenizer

     def __init__(  self		,     _a=None		,     _a=None		,     _a=False		,     _a=True		,     _a=True		,     _a="<unk>"		,     _a="[SEP]"		,     _a="<pad>"		,     _a="[CLS]"		,     _a="[MASK]"		,     **_a		,     )						->							Optional[int]:
           # Mask token behave like a normal word, i.e. include the space before it and
           # is included in the raw text, there should be a match in a non-normalized sentence.
           _A							:     int			=						(
               AddedToken(_a		,     lstrip=_a		,     rstrip=_a		,     normalized=_a				)
               if isinstance(_a		,     _a				)
               else mask_token
           )

           super().__init__(
               _a		,     tokenizer_file=_a		,     do_lower_case=_a		,     remove_space=_a		,     keep_accents=_a		,     unk_token=_a		,     sep_token=_a		,     pad_token=_a		,     cls_token=_a		,     mask_token=_a		,     **_a		,     )

           _A							:     Optional[int]			=						do_lower_case
           _A							:     List[Any]			=						remove_space
           _A							:     str			=						keep_accents
           _A							:     int			=						vocab_file
           _A							:     int			=						False if not self.vocab_file else True

     def 					a__	(  self		,     _a		,     _a = None				)						->							List[int]:
           _A							:     str			=						[self.sep_token_id]
           _A							:     Dict			=						[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		,     _a		,     _a = None				)						->							List[int]:
           _A							:     Any			=						[self.sep_token_id]
           _A							:     Any			=						[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		,     _a		,     _a = None				)						->							Tuple[str]:
           if not os.path.isdir(_a				):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
                 return
           _A							:     List[str]			=						os.path.join(
               _a		,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

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

           return (out_vocab_file,)

