



import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


a__    =	False







class 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			Any)				->				Dict:

    """simple docstring"""







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

   @property
   def      UpperCamelCase_  (					self							:			Tuple)				->				Tuple:

    """simple docstring"""







    return 12

   @property
   def      UpperCamelCase_  (					self							:			str)				->				Any:

    """simple docstring"""







    return 12

   @property
   def      UpperCamelCase_  (					self							:			Dict)				->				int:

    """simple docstring"""







    return 32

   @property
   def      UpperCamelCase_  (					self							:			List[str])				->				Union[str, Any]:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case    :  Any					=					VQModel(
        block_out_channels=[32, 64]				,		in_channels=3				,		out_channels=3				,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,		latent_channels=3				,		num_vq_embeddings=self.num_embed				,		vq_embed_dim=3				,		)
    return model

   @property
   def      UpperCamelCase_  (					self							:			Optional[Any])				->				Tuple:

    """simple docstring"""







    _snake_case    :  int					=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")
    return tokenizer

   @property
   def      UpperCamelCase_  (					self							:			Optional[int])				->				List[Any]:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case    :  str					=					CLIPTextConfig(
        bos_token_id=0				,		eos_token_id=2				,		hidden_size=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=1000				,		)
    return CLIPTextModel(lowerCAmelCase)

   @property
   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				List[Any]:

    """simple docstring"""







    torch.manual_seed(0)

    _snake_case    :  Union[str, Any]					=					12
    _snake_case    :  Dict					=					12

    _snake_case    :  Dict					=					{
        """attention_bias""": True,
        """cross_attention_dim""": 32,
        """attention_head_dim""": height * width,
        """num_attention_heads""": 1,
        """num_vector_embeds""": self.num_embed,
        """num_embeds_ada_norm""": self.num_embeds_ada_norm,
        """norm_num_groups""": 32,
        """sample_size""": width,
        """activation_fn""": """geglu-approximate""",
    }

    _snake_case    :  List[Any]					=					TransformeraDModel(**lowerCAmelCase)
    return model

   def      UpperCamelCase_  (					self							:			List[str])				->				Tuple:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					"""cpu"""

    _snake_case    :  List[Any]					=					self.dummy_vqvae
    _snake_case    :  str					=					self.dummy_text_encoder
    _snake_case    :  Dict					=					self.dummy_tokenizer
    _snake_case    :  Optional[int]					=					self.dummy_transformer
    _snake_case    :  List[Any]					=					VQDiffusionScheduler(self.num_embed)
    _snake_case    :  List[str]					=					LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCAmelCase)

    _snake_case    :  Any					=					VQDiffusionPipeline(
        vqvae=lowerCAmelCase				,		text_encoder=lowerCAmelCase				,		tokenizer=lowerCAmelCase				,		transformer=lowerCAmelCase				,		scheduler=lowerCAmelCase				,		learned_classifier_free_sampling_embeddings=lowerCAmelCase				,		)
    _snake_case    :  Tuple					=					pipe.to(lowerCAmelCase)
    pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  Tuple					=					"""teddy bear playing in the pool"""

    _snake_case    :  Tuple					=					torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case    :  Dict					=					pipe([prompt]				,		generator=lowerCAmelCase				,		num_inference_steps=2				,		output_type="""np""")
    _snake_case    :  Any					=					output.images

    _snake_case    :  List[str]					=					torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case    :  str					=					pipe(
        [prompt]				,		generator=lowerCAmelCase				,		output_type="""np"""				,		return_dict=lowerCAmelCase				,		num_inference_steps=2)[0]

    _snake_case    :  Any					=					image[0, -3:, -3:, -1]
    _snake_case    :  Optional[Any]					=					image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 24, 24, 3)

    _snake_case    :  Tuple					=					np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992])

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


   def      UpperCamelCase_  (					self							:			Tuple)				->				Tuple:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					"""cpu"""

    _snake_case    :  Union[str, Any]					=					self.dummy_vqvae
    _snake_case    :  Tuple					=					self.dummy_text_encoder
    _snake_case    :  Dict					=					self.dummy_tokenizer
    _snake_case    :  Any					=					self.dummy_transformer
    _snake_case    :  int					=					VQDiffusionScheduler(self.num_embed)
    _snake_case    :  Optional[int]					=					LearnedClassifierFreeSamplingEmbeddings(
        learnable=lowerCAmelCase				,		hidden_size=self.text_embedder_hidden_size				,		length=tokenizer.model_max_length)

    _snake_case    :  List[Any]					=					VQDiffusionPipeline(
        vqvae=lowerCAmelCase				,		text_encoder=lowerCAmelCase				,		tokenizer=lowerCAmelCase				,		transformer=lowerCAmelCase				,		scheduler=lowerCAmelCase				,		learned_classifier_free_sampling_embeddings=lowerCAmelCase				,		)
    _snake_case    :  int					=					pipe.to(lowerCAmelCase)
    pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  int					=					"""teddy bear playing in the pool"""

    _snake_case    :  Tuple					=					torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case    :  Dict					=					pipe([prompt]				,		generator=lowerCAmelCase				,		num_inference_steps=2				,		output_type="""np""")
    _snake_case    :  List[str]					=					output.images

    _snake_case    :  Dict					=					torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case    :  str					=					pipe(
        [prompt]				,		generator=lowerCAmelCase				,		output_type="""np"""				,		return_dict=lowerCAmelCase				,		num_inference_steps=2)[0]

    _snake_case    :  int					=					image[0, -3:, -3:, -1]
    _snake_case    :  Dict					=					image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 24, 24, 3)

    _snake_case    :  Any					=					np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988])

    assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2







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



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			List[str])				->				List[Any]:

    """simple docstring"""







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


   def      UpperCamelCase_  (					self							:			Dict)				->				Tuple:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy""")

    _snake_case    :  List[str]					=					VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq""")
    _snake_case    :  List[Any]					=					pipeline.to(lowerCAmelCase)
    pipeline.set_progress_bar_config(disable=lowerCAmelCase)

    # requires GPU generator for gumbel softmax
    # don't use GPU generator in tests though
    _snake_case    :  Union[str, Any]					=					torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case    :  Tuple					=					pipeline(
        """teddy bear playing in the pool"""				,		num_images_per_prompt=1				,		generator=lowerCAmelCase				,		output_type="""np"""				,		)

    _snake_case    :  List[str]					=					output.images[0]

    assert image.shape == (256, 256, 3)
    assert np.abs(expected_image - image).max() < 2.0





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:
  a__    =	None


a__    =	logging.get_logger(__name__)

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

a__    =	{
    """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""",
    },
}

a__    =	{
    """camembert-base""": 5_12,
}

a__    =	"""▁"""







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		VOCAB_FILES_NAMES
   snake_case_					: str          =		PRETRAINED_VOCAB_FILES_MAP
   snake_case_					: Optional[Any]          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_					: List[Any]          =		["""input_ids""", """attention_mask"""]
   snake_case_					: int          =		CamembertTokenizer

   def __init__(					self							:			Optional[int]				,		lowerCAmelCase							:			Optional[Any]=None				,		lowerCAmelCase							:			int=None				,		lowerCAmelCase							:			List[Any]="<s>"				,		lowerCAmelCase							:			List[str]="</s>"				,		lowerCAmelCase							:			Union[str, Any]="</s>"				,		lowerCAmelCase							:			Dict="<s>"				,		lowerCAmelCase							:			Dict="<unk>"				,		lowerCAmelCase							:			Any="<pad>"				,		lowerCAmelCase							:			List[str]="<mask>"				,		lowerCAmelCase							:			List[str]=["<s>NOTUSED", "</s>NOTUSED"]				,		**lowerCAmelCase							:			Dict				,		)				->				Tuple:

    """simple docstring"""







    _snake_case    :  Dict					=					AddedToken(lowerCAmelCase				,		lstrip=lowerCAmelCase				,		rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase				,		lowerCAmelCase) else mask_token

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

    _snake_case    :  List[str]					=					vocab_file
    _snake_case    :  Optional[Any]					=					False if not self.vocab_file else True

   def      UpperCamelCase_  (					self							:			Optional[int]				,		lowerCAmelCase							:			List[int]				,		lowerCAmelCase							:			Optional[List[int]] = None)				->				List[int]:

    """simple docstring"""







    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    _snake_case    :  Optional[Any]					=					[self.cls_token_id]
    _snake_case    :  List[Any]					=					[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			List[int]				,		lowerCAmelCase							:			Optional[List[int]] = None)				->				List[int]:

    """simple docstring"""







    _snake_case    :  str					=					[self.sep_token_id]
    _snake_case    :  List[str]					=					[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							:			str				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Optional[str] = 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(lowerCAmelCase):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
     return
    _snake_case    :  Optional[int]					=					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,)
