






"""simple docstring"""
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_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   '''▁'''

lowerCamelCase_       			=   {'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCamelCase_       			=   {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

lowerCamelCase_       			=   {
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       VOCAB_FILES_NAMES
	__magic_name__			   =       PRETRAINED_VOCAB_FILES_MAP
	__magic_name__			   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__			   =       ['''input_ids''', '''attention_mask''']





	def __init__(   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				int="<s>"			,			lowerCAmelCase_      :				str="</s>"			,			lowerCAmelCase_      :				List[Any]="</s>"			,			lowerCAmelCase_      :				str="<s>"			,			lowerCAmelCase_      :				int="<unk>"			,			lowerCAmelCase_      :				Any="<pad>"			,			lowerCAmelCase_      :				Optional[Any]="<mask>"			,			lowerCAmelCase_      :				Optional[Dict[str, Any]] = None			,			**lowerCAmelCase_      :				Any			,			)		->    None:
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCAmelCase_  :				Dict      =      AddedToken(lowerCAmelCase_			,			lstrip=lowerCAmelCase_			,			rstrip=lowerCAmelCase_		) if isinstance(lowerCAmelCase_			,			lowerCAmelCase_		) else mask_token

				UpperCAmelCase_  :				Tuple      =      {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=lowerCAmelCase_			,			eos_token=lowerCAmelCase_			,			unk_token=lowerCAmelCase_			,			sep_token=lowerCAmelCase_			,			cls_token=lowerCAmelCase_			,			pad_token=lowerCAmelCase_			,			mask_token=lowerCAmelCase_			,			sp_model_kwargs=self.sp_model_kwargs			,			**lowerCAmelCase_			,			)

				UpperCAmelCase_  :				Any      =      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(str(lowerCAmelCase_		)		)
				UpperCAmelCase_  :				List[str]      =      vocab_file

				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
				# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
				# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

				# Mimic fairseq token-to-id alignment for the first 4 token
				UpperCAmelCase_  :				Union[str, Any]      =      {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				UpperCAmelCase_  :				List[str]      =      1

				UpperCAmelCase_  :				Optional[Any]      =      len(self.sp_model		) + self.fairseq_offset
				UpperCAmelCase_  :				str      =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}





	def __getstate__(   self      :				List[str]		)		->    Dict:
				UpperCAmelCase_  :				Any      =      self.__dict__.copy()
				UpperCAmelCase_  :				int      =      None
				UpperCAmelCase_  :				int      =      self.sp_model.serialized_model_proto()
				return state





	def __setstate__(   self      :				List[str]			,			lowerCAmelCase_      :				Any		)		->    str:
				UpperCAmelCase_  :				Any      =      d

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

				UpperCAmelCase_  :				str      =      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				List[int]			,			lowerCAmelCase_      :				Optional[List[int]] = None		)		->    List[int]:

				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCAmelCase_  :				Dict      =      [self.cls_token_id]
				UpperCAmelCase_  :				Optional[int]      =      [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				List[int]			,			lowerCAmelCase_      :				Optional[List[int]] = None			,			lowerCAmelCase_      :				bool = False		)		->    List[int]:

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

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				List[int]			,			lowerCAmelCase_      :				Optional[List[int]] = None		)		->    List[int]:
				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   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Dict:
				return len(self.sp_model		) + self.fairseq_offset + 1  # Add the <mask> token





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Any:
				UpperCAmelCase_  :				Dict      =      {self.convert_ids_to_tokens(lowerCAmelCase_		): i for i in range(self.vocab_size		)}
				vocab.update(self.added_tokens_encoder		)
				return vocab





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				str		)		->    List[str]:
				return self.sp_model.encode(lowerCAmelCase_			,			out_type=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				int		)		->    List[str]:
				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				UpperCAmelCase_  :				str      =      self.sp_model.PieceToId(lowerCAmelCase_		)

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				List[Any]		)		->    int:
				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   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				Dict		)		->    List[Any]:
				UpperCAmelCase_  :				Tuple      =      "".join(lowerCAmelCase_		).replace(lowerCAmelCase_			,			" "		).strip()
				return out_string





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				Optional[str] = None		)		->    Tuple[str]:
				if not os.path.isdir(lowerCAmelCase_		):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
				UpperCAmelCase_  :				str      =      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_		) and os.path.isfile(self.vocab_file		):
							copyfile(self.vocab_file			,			lowerCAmelCase_		)
				elif not os.path.isfile(self.vocab_file		):
							with open(lowerCAmelCase_			,			"wb"		) as fi:
										UpperCAmelCase_  :				Union[str, Any]      =      self.sp_model.serialized_model_proto()
										fi.write(lowerCAmelCase_		)

				return (out_vocab_file,)











"""simple docstring"""
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     UpperCamelCase_ (__A       ,		unittest.TestCase  ):
	__magic_name__			   =       ShapEPipeline
	__magic_name__			   =       ['''prompt''']
	__magic_name__			   =       ['''prompt''']
	__magic_name__			   =       [
	    '''num_images_per_prompt''',
	    '''num_inference_steps''',
	    '''generator''',
	    '''latents''',
	    '''guidance_scale''',
	    '''frame_size''',
	    '''output_type''',
	    '''return_dict''',
	]
	__magic_name__			   =       False





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Dict:
				return 32





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Tuple:
				return 32





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Optional[int]:
				return self.time_input_dim * 4





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    List[Any]:
				return 8





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    int:
				UpperCAmelCase_  :				str      =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
				return tokenizer





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    List[str]:
				torch.manual_seed(0		)
				UpperCAmelCase_  :				Dict      =      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(lowerCAmelCase_		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Optional[Any]:
				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",
				    "encoder_hid_proj_type": None,
				    "added_emb_type": None,
				}

				UpperCAmelCase_  :				List[Any]      =      PriorTransformer(**lowerCAmelCase_		)
				return model





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    int:
				torch.manual_seed(0		)

				UpperCAmelCase_  :				Any      =      {
				    "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_  :				Tuple      =      ShapERenderer(**lowerCAmelCase_		)
				return model





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Tuple:
				UpperCAmelCase_  :				str      =      self.dummy_prior
				UpperCAmelCase_  :				Dict      =      self.dummy_text_encoder
				UpperCAmelCase_  :				Any      =      self.dummy_tokenizer
				UpperCAmelCase_  :				int      =      self.dummy_renderer

				UpperCAmelCase_  :				Optional[int]      =      HeunDiscreteScheduler(
				    beta_schedule="exp"			,			num_train_timesteps=1_024			,			prediction_type="sample"			,			use_karras_sigmas=lowerCAmelCase_			,			clip_sample=lowerCAmelCase_			,			clip_sample_range=1.0			,			)
				UpperCAmelCase_  :				str      =      {
				    "prior": prior,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "renderer": renderer,
				    "scheduler": scheduler,
				}

				return components





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Any=0		)		->    Union[str, Any]:
				if str(lowerCAmelCase_		).startswith("mps"		):
							UpperCAmelCase_  :				str      =      torch.manual_seed(lowerCAmelCase_		)
				else:
							UpperCAmelCase_  :				Union[str, Any]      =      torch.Generator(device=lowerCAmelCase_		).manual_seed(lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      {
				    "prompt": "horse",
				    "generator": generator,
				    "num_inference_steps": 1,
				    "frame_size": 32,
				    "output_type": "np",
				}
				return inputs





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    List[Any]:
				UpperCAmelCase_  :				Any      =      "cpu"

				UpperCAmelCase_  :				Union[str, Any]      =      self.get_dummy_components()

				UpperCAmelCase_  :				Tuple      =      self.pipeline_class(**lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      pipe.to(lowerCAmelCase_		)

				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

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

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

				UpperCAmelCase_  :				Optional[int]      =      np.array(
				    [
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				    ]		)

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Optional[Any]:
				# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
				self._test_inference_batch_consistent(batch_sizes=[1, 2]		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    List[str]:
				UpperCAmelCase_  :				Optional[Any]      =      torch_device == "cpu"
				UpperCAmelCase_  :				Dict      =      True

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    List[str]:
				UpperCAmelCase_  :				int      =      self.get_dummy_components()
				UpperCAmelCase_  :				Tuple      =      self.pipeline_class(**lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

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

				UpperCAmelCase_  :				int      =      self.get_dummy_inputs(lowerCAmelCase_		)

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

				UpperCAmelCase_  :				int      =      pipe(**lowerCAmelCase_			,			num_images_per_prompt=lowerCAmelCase_		)[0]

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







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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Any:
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Any      =      load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/shap_e/test_shap_e_np_out.npy"		)
				UpperCAmelCase_  :				str      =      ShapEPipeline.from_pretrained("openai/shap-e"		)
				UpperCAmelCase_  :				Union[str, Any]      =      pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_  :				Union[str, Any]      =      torch.Generator(device=lowerCAmelCase_		).manual_seed(0		)

				UpperCAmelCase_  :				Optional[int]      =      pipe(
				    "a shark"			,			generator=lowerCAmelCase_			,			guidance_scale=1_5.0			,			num_inference_steps=64			,			frame_size=64			,			output_type="np"			,			).images[0]

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

				assert_mean_pixel_difference(lowerCAmelCase_			,			lowerCAmelCase_		)



