
'''simple docstring'''






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):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 12
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 12
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 32
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									torch.manual_seed(0      )
									lowerCamelCase					:			Dict									= 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      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
									return tokenizer
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									torch.manual_seed(0      )
									lowerCamelCase					:			List[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(A      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									torch.manual_seed(0      )

									lowerCamelCase					:			Dict									= 12
									lowerCamelCase					:			int									= 12

									lowerCamelCase					:			str									= {
									    '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',
									}

									lowerCamelCase					:			Dict									= TransformeraDModel(**A      )
									return model
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= 'cpu'

									lowerCamelCase					:			Tuple									= self.dummy_vqvae
									lowerCamelCase					:			int									= self.dummy_text_encoder
									lowerCamelCase					:			str									= self.dummy_tokenizer
									lowerCamelCase					:			Tuple									= self.dummy_transformer
									lowerCamelCase					:			Optional[Any]									= VQDiffusionScheduler(self.num_embed      )
									lowerCamelCase					:			Dict									= LearnedClassifierFreeSamplingEmbeddings(learnable=A      )

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

									lowerCamelCase					:			Optional[int]									= 'teddy bear playing in the pool'

									lowerCamelCase					:			List[str]									= torch.Generator(device=A      ).manual_seed(0      )
									lowerCamelCase					:			List[str]									= pipe([prompt],    generator=A,    num_inference_steps=2,    output_type='np'      )
									lowerCamelCase					:			Dict									= output.images

									lowerCamelCase					:			Dict									= torch.Generator(device=A      ).manual_seed(0      )
									lowerCamelCase					:			Optional[Any]									= pipe(
									    [prompt],    generator=A,    output_type='np',    return_dict=A,    num_inference_steps=2      )[0]

									lowerCamelCase					:			int									= image[0, -3:, -3:, -1]
									lowerCamelCase					:			Any									= image_from_tuple[0, -3:, -3:, -1]

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

									lowerCamelCase					:			List[str]									= np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]      )

									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      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= 'cpu'

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

									lowerCamelCase					:			List[str]									= VQDiffusionPipeline(
									    vqvae=A,    text_encoder=A,    tokenizer=A,    transformer=A,    scheduler=A,    learned_classifier_free_sampling_embeddings=A,    )
									lowerCamelCase					:			Union[str, Any]									= pipe.to(A      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Optional[int]									= 'teddy bear playing in the pool'

									lowerCamelCase					:			int									= torch.Generator(device=A      ).manual_seed(0      )
									lowerCamelCase					:			Optional[Any]									= pipe([prompt],    generator=A,    num_inference_steps=2,    output_type='np'      )
									lowerCamelCase					:			Any									= output.images

									lowerCamelCase					:			Optional[Any]									= torch.Generator(device=A      ).manual_seed(0      )
									lowerCamelCase					:			int									= pipe(
									    [prompt],    generator=A,    output_type='np',    return_dict=A,    num_inference_steps=2      )[0]

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

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

									lowerCamelCase					:			int									= np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]      )

									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):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'      )

									lowerCamelCase					:			List[str]									= VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'      )
									lowerCamelCase					:			Optional[int]									= pipeline.to(A      )
									pipeline.set_progress_bar_config(disable=A      )

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

									lowerCamelCase					:			Optional[Any]									= output.images[0]

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


'''simple docstring'''






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A			      =      logging.get_logger(__name__)


A			      =      '▁'

A			      =      {'vocab_file': 'spiece.model'}

A			      =      {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

A			      =      {
    'google/reformer-crime-and-punishment': 52_4288,
}




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	VOCAB_FILES_NAMES
					_lowerCAmelCase			     =	PRETRAINED_VOCAB_FILES_MAP
					_lowerCAmelCase			     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCAmelCase			     =	['''input_ids''', '''attention_mask''']
					def __init__(     self,    A,    A="</s>",    A="<unk>",    A=[],    A = None,    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			str									= {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    eos_token=A,    unk_token=A,    additional_special_tokens=A,    sp_model_kwargs=self.sp_model_kwargs,    **A,    )

									lowerCamelCase					:			Any									= vocab_file
									lowerCamelCase					:			List[Any]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(A      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return self.sp_model.get_piece_size()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= {self.convert_ids_to_tokens(A      ): i for i in range(self.vocab_size      )}
									vocab.update(self.added_tokens_encoder      )
									return vocab
					def __getstate__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.__dict__.copy()
									lowerCamelCase					:			List[Any]									= None
									return state
					def __setstate__(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= d

									# for backward compatibility
									if not hasattr(self,    'sp_model_kwargs'      ):
													lowerCamelCase					:			List[str]									= {}

									lowerCamelCase					:			Optional[int]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(self.vocab_file      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.encode(A,    out_type=A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.piece_to_id(A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									if index < self.sp_model.get_piece_size():
													lowerCamelCase					:			Union[str, Any]									= self.sp_model.IdToPiece(A      )
									return token
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= []
									lowerCamelCase					:			Optional[int]									= ''
									for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																	out_string += self.sp_model.decode(A      ) + token
																	lowerCamelCase					:			Any									= []
													else:
																	current_sub_tokens.append(A      )
									out_string += self.sp_model.decode(A      )
									return out_string.strip()




					def     UpperCAmelCase_							(     self,    A,    A = None      ):

									"""simple docstring"""


									if not os.path.isdir(A      ):
													logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
													return
									lowerCamelCase					:			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      ) and os.path.isfile(self.vocab_file      ):
													copyfile(self.vocab_file,    A      )
									elif not os.path.isfile(self.vocab_file      ):
													with open(A,    'wb'      ) as fi:
																	lowerCamelCase					:			Optional[Any]									= self.sp_model.serialized_model_proto()
																	fi.write(A      )

									return (out_vocab_file,)
