





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase       :     str   =     logging.get_logger(__name__)

__lowerCamelCase       :     int   =     {"""vocab_file""": """sentencepiece.bpe.model"""}

__lowerCamelCase       :     Any   =     {
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

__lowerCamelCase       :     Optional[Any]   =     {
    """camembert-base""": 512,
}

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



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:List[str]						    =   VOCAB_FILES_NAMES
      _UpperCAmelCase	:Optional[int]						    =   PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase	:Any						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase	:List[str]						    =   ['input_ids', 'attention_mask']





      def __init__( self       ,							A_       ,							A_="<s>"       ,							A_="</s>"       ,							A_="</s>"       ,							A_="<s>"       ,							A_="<unk>"       ,							A_="<pad>"       ,							A_="<mask>"       ,							A_=["<s>NOTUSED", "</s>NOTUSED"]       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   AddedToken(A_       ,							lstrip=A_       ,							rstrip=A_   ) if isinstance(A_       ,							A_   ) else mask_token

       UpperCamelCase	:					Optional[Any]       	=   {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=A_       ,							eos_token=A_       ,							unk_token=A_       ,							sep_token=A_       ,							cls_token=A_       ,							pad_token=A_       ,							mask_token=A_       ,							additional_special_tokens=A_       ,							sp_model_kwargs=self.sp_model_kwargs       ,							**A_       ,							)
       UpperCamelCase	:					Any       	=   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(str(A_   )   )
       UpperCamelCase	:					str       	=   vocab_file
       # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
       # sentencepiece vocabulary (this is the case for <s> and </s>
       UpperCamelCase	:					str       	=   {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
       UpperCamelCase	:					Union[str, Any]       	=   len(self.fairseq_tokens_to_ids   )
       UpperCamelCase	:					Any       	=   len(self.sp_model   ) + len(self.fairseq_tokens_to_ids   )
       UpperCamelCase	:					Dict       	=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None       ,							A_ = False   ):
       '''simple docstring'''




       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=A_       ,							token_ids_a=A_       ,							already_has_special_tokens=A_   )

       if token_ids_a is None:
        return [1] + ([0] * len(A_   )) + [1]
       return [1] + ([0] * len(A_   )) + [1, 1] + ([0] * len(A_   )) + [1]





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   [self.sep_token_id]
       UpperCamelCase	:					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 + sep + token_ids_a + sep   ) * [0]





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return len(self.fairseq_tokens_to_ids   ) + len(self.sp_model   )





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




       UpperCamelCase	:					Optional[int]       	=   {self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return self.sp_model.encode(A_       ,							out_type=A_   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
       elif self.sp_model.PieceToId(A_   ) == 0:
        # Convert sentence piece unk token to fairseq unk token index
        return self.unk_token_id
       return self.fairseq_offset + self.sp_model.PieceToId(A_   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       if index in self.fairseq_ids_to_tokens:
        return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




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





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




       UpperCamelCase	:					Any       	=   self.__dict__.copy()
       UpperCamelCase	:					Any       	=   None
       return state





      def __setstate__( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   d

       # for backward compatibility
       if not hasattr(self       ,							"sp_model_kwargs"   ):
        UpperCamelCase	:					Optional[Any]       	=   {}

       UpperCamelCase	:					str       	=   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.vocab_file   )





      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
       UpperCamelCase	:					Optional[int]       	=   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:
         UpperCamelCase	:					str       	=   self.sp_model.serialized_model_proto()
         fi.write(A_   )

       return (out_vocab_file,)


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		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Tuple       					=ShapEPipeline
							lowercase_       :       List[Any]       					=['''prompt''']
							lowercase_       :       int       					=['''prompt''']
							lowercase_       :       Union[str, Any]       					=[
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_       :       Optional[int]       					=False




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return self.time_input_dim * 4




							@property
							def  A__      (				self):
												return 8




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




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						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=3_7   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_0_0_0   ,)
												return CLIPTextModelWithProjection(A__)




							@property
							def  A__      (				self):
												torch.manual_seed(0)

												lowercase							=						{
												    '''num_attention_heads''': 2,
												    '''attention_head_dim''': 1_6,
												    '''embedding_dim''': self.time_input_dim,
												    '''num_embeddings''': 3_2,
												    '''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,
												}

												lowercase							=						PriorTransformer(**A__)
												return model




							@property
							def  A__      (				self):
												torch.manual_seed(0)

												lowercase							=						{
												    '''param_shapes''': (
												        (self.renderer_dim, 9_3),
												        (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''': 1_2,
												    '''background''': (
												        0.1,
												        0.1,
												        0.1,
												    ),
												}
												lowercase							=						ShapERenderer(**A__)
												return model




							def  A__      (				self):
												lowercase							=						self.dummy_prior
												lowercase							=						self.dummy_text_encoder
												lowercase							=						self.dummy_tokenizer
												lowercase							=						self.dummy_renderer

												lowercase							=						HeunDiscreteScheduler(
												    beta_schedule='''exp'''   ,num_train_timesteps=1_0_2_4   ,prediction_type='''sample'''   ,use_karras_sigmas=A__   ,clip_sample=A__   ,clip_sample_range=1.0   ,)
												lowercase							=						{
												    '''prior''': prior,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''renderer''': renderer,
												    '''scheduler''': scheduler,
												}

												return components




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)
												lowercase							=						{
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''num_inference_steps''': 1,
												    '''frame_size''': 3_2,
												    '''output_type''': '''np''',
												}
												return inputs




							def  A__      (				self):
												lowercase							=						'''cpu'''

												lowercase							=						self.get_dummy_components()

												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)

												pipe.set_progress_bar_config(disable=A__)

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

												assert image.shape == (2_0, 3_2, 3_2, 3)

												lowercase							=						np.array(
												    [
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												    ])

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




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




							def  A__      (				self):
												lowercase							=						torch_device == '''cpu'''
												lowercase							=						True

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





							def  A__      (				self):
												lowercase							=						self.get_dummy_components()
												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						1
												lowercase							=						2

												lowercase							=						self.get_dummy_inputs(A__)

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

												lowercase							=						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  A__      (				self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





							def  A__      (				self):
												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/shap_e/test_shap_e_np_out.npy''')
												lowercase							=						ShapEPipeline.from_pretrained('''openai/shap-e''')
												lowercase							=						pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						torch.Generator(device=A__).manual_seed(0)

												lowercase							=						pipe(
												    '''a shark'''   ,generator=A__   ,guidance_scale=15.0   ,num_inference_steps=6_4   ,frame_size=6_4   ,output_type='''np'''   ,).images[0]

												assert images.shape == (2_0, 6_4, 6_4, 3)

												assert_mean_pixel_difference(A__   ,A__)




