
import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import 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        lowercase      (	lowercase__						,     unittest.TestCase     ):
		__lowercase : Tuple			  =       ShapEPipeline
		__lowercase : Optional[Any]			  =       ["prompt"]
		__lowercase : Union[str, Any]			  =       ["prompt"]
		__lowercase : Union[str, Any]			  =       [
		    "num_images_per_prompt",
		    "num_inference_steps",
		    "generator",
		    "latents",
		    "guidance_scale",
		    "frame_size",
		    "output_type",
		    "return_dict",
		]
		__lowercase : List[str]			  =       False



		@property
		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				return self.time_input_dim * 4



		@property
		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				return 8



		@property
		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
				return tokenizer



		@property
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				torch.manual_seed(0 )
				UpperCamelCase						=  CLIPTextConfig(
				    bos_token_id=0       ,   eos_token_id=2       ,   hidden_size=self.text_embedder_hidden_size       ,   projection_dim=self.text_embedder_hidden_size       ,   intermediate_size=37       ,   layer_norm_eps=1e-05       ,   num_attention_heads=4       ,   num_hidden_layers=5       ,   pad_token_id=1       ,   vocab_size=1_000       ,   )
				return CLIPTextModelWithProjection(A_ )



		@property
		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				torch.manual_seed(0 )

				UpperCamelCase						=  {
				    "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",
				    "encoder_hid_proj_type": None,
				    "added_emb_type": None,
				}

				UpperCamelCase						=  PriorTransformer(**A_ )
				return model



		@property
		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				torch.manual_seed(0 )

				UpperCamelCase						=  {
				    "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						=  ShapERenderer(**A_ )
				return model



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  self.dummy_prior
				UpperCamelCase						=  self.dummy_text_encoder
				UpperCamelCase						=  self.dummy_tokenizer
				UpperCamelCase						=  self.dummy_renderer

				UpperCamelCase						=  HeunDiscreteScheduler(
				    beta_schedule='exp'       ,   num_train_timesteps=1_024       ,   prediction_type='sample'       ,   use_karras_sigmas=A_       ,   clip_sample=A_       ,   clip_sample_range=1.0       ,   )
				UpperCamelCase						=  {
				    "prior": prior,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "renderer": renderer,
				    "scheduler": scheduler,
				}

				return components



		def __UpperCamelCase				(				self       ,   A_       ,   A_=0 )						->      Optional[Any]:
				"""simple docstring"""
				if str(A_ ).startswith('mps' ):
						UpperCamelCase						=  torch.manual_seed(A_ )
				else:
						UpperCamelCase						=  torch.Generator(device=A_ ).manual_seed(A_ )
				UpperCamelCase						=  {
				    "prompt": "horse",
				    "generator": generator,
				    "num_inference_steps": 1,
				    "frame_size": 32,
				    "output_type": "np",
				}
				return inputs



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  "cpu"

				UpperCamelCase						=  self.get_dummy_components()

				UpperCamelCase						=  self.pipeline_class(**A_ )
				UpperCamelCase						=  pipe.to(A_ )

				pipe.set_progress_bar_config(disable=A_ )

				UpperCamelCase						=  pipe(**self.get_dummy_inputs(A_ ) )
				UpperCamelCase						=  output.images[0]
				UpperCamelCase						=  image[0, -3:, -3:, -1]

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

				UpperCamelCase						=  np.array(
				    [
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				    ] )

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



		def __UpperCamelCase				(				self )						->      List[str]:
				"""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 )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  torch_device == "cpu"
				UpperCamelCase						=  True

				self._test_inference_batch_single_identical(
				    batch_size=2       ,   test_max_difference=A_       ,   relax_max_difference=A_       ,   )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.get_dummy_components()
				UpperCamelCase						=  self.pipeline_class(**A_ )
				UpperCamelCase						=  pipe.to(A_ )
				pipe.set_progress_bar_config(disable=A_ )

				UpperCamelCase						=  1
				UpperCamelCase						=  2

				UpperCamelCase						=  self.get_dummy_inputs(A_ )

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

				UpperCamelCase						=  pipe(**A_       ,   num_images_per_prompt=A_ )[0]

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






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



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/shap_e/test_shap_e_np_out.npy' )
				UpperCamelCase						=  ShapEPipeline.from_pretrained('openai/shap-e' )
				UpperCamelCase						=  pipe.to(A_ )
				pipe.set_progress_bar_config(disable=A_ )

				UpperCamelCase						=  torch.Generator(device=A_ ).manual_seed(0 )

				UpperCamelCase						=  pipe(
				    'a shark'       ,   generator=A_       ,   guidance_scale=15.0       ,   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_camembert import CamembertTokenizer
else:
					_UpperCAmelCase      :				List[str]          = None


_UpperCAmelCase      :				Any          = logging.get_logger(__name__)

_UpperCAmelCase      :				Tuple          = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_UpperCAmelCase      :				List[str]          = {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

_UpperCAmelCase      :				Optional[int]          = {
    "camembert-base": 512,
}

_UpperCAmelCase      :				Union[str, Any]          = "▁"


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : str			  =       VOCAB_FILES_NAMES
		__lowercase : Optional[Any]			  =       PRETRAINED_VOCAB_FILES_MAP
		__lowercase : List[str]			  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase : List[str]			  =       ["input_ids", "attention_mask"]
		__lowercase : Tuple			  =       CamembertTokenizer



		def __init__(				self       ,   A_=None       ,   A_=None       ,   A_="<s>"       ,   A_="</s>"       ,   A_="</s>"       ,   A_="<s>"       ,   A_="<unk>"       ,   A_="<pad>"       ,   A_="<mask>"       ,   A_=["<s>NOTUSED", "</s>NOTUSED"]       ,   **A_       ,   )						->      List[Any]:
				"""simple docstring"""
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCamelCase						=  AddedToken(A_       ,   lstrip=A_       ,   rstrip=A_ ) if isinstance(A_       ,   A_ ) else mask_token

				super().__init__(
				    A_       ,   tokenizer_file=A_       ,   bos_token=A_       ,   eos_token=A_       ,   sep_token=A_       ,   cls_token=A_       ,   unk_token=A_       ,   pad_token=A_       ,   mask_token=A_       ,   additional_special_tokens=A_       ,   **A_       ,   )

				UpperCamelCase						=  vocab_file
				UpperCamelCase						=  False if not self.vocab_file else True



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      List[int]:
				"""simple docstring"""

				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCamelCase						=  [self.cls_token_id]
				UpperCamelCase						=  [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      List[int]:
				"""simple docstring"""
				UpperCamelCase						=  [self.sep_token_id]
				UpperCamelCase						=  [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 + sep + token_ids_a + sep ) * [0]



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      Tuple[str]:
				"""simple docstring"""
				if not self.can_save_slow_tokenizer:
						raise ValueError(
						    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
						    'tokenizer.' )

				if not os.path.isdir(A_ ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
						return
				UpperCamelCase						=  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,)
