import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


lowercase__     :							Optional[int]            =      logging.get_logger(__name__)


lowercase__     :							Any            =      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all BART models at https://huggingface.co/models?filter=bart
lowercase__     :							List[str]            =      {
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
}

lowercase__     :							Dict            =      {
    '''facebook/bart-base''': 1_0_2_4,
    '''facebook/bart-large''': 1_0_2_4,
    '''facebook/bart-large-mnli''': 1_0_2_4,
    '''facebook/bart-large-cnn''': 1_0_2_4,
    '''facebook/bart-large-xsum''': 1_0_2_4,
    '''yjernite/bart_eli5''': 1_0_2_4,
}

@lru_cache()
def   SCREAMING_SNAKE_CASE_	(		)	->      Optional[int]:
							lowerCAmelCase      						=						(
							    list(range(ord('''!'''				)  ,    ord('''~'''				) + 1				)				) + list(range(ord('''¡'''				)  ,    ord('''¬'''				) + 1				)				) + list(range(ord('''®'''				)  ,    ord('''ÿ'''				) + 1				)				)
							)
							lowerCAmelCase      						=						bs[:]
							lowerCAmelCase      						=						0
							for b in range(2**8				):
														if b not in bs:
																					bs.append(__snake_case				)
																					cs.append(2**8 + n				)
																					n += 1
							lowerCAmelCase      						=						[chr(__snake_case				) for n in cs]
							return dict(zip(__snake_case  ,    __snake_case				)				)

def   SCREAMING_SNAKE_CASE_	(		snake_case__				)	->      int:
							lowerCAmelCase      						=						set()
							lowerCAmelCase      						=						word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)				)
														lowerCAmelCase      						=						char
							return pairs






class   lowercase_    (    __lowercase		):



	"""simple docstring"""





	UpperCAmelCase_    :   Optional[int]  =							VOCAB_FILES_NAMES
	UpperCAmelCase_    :   Union[str, Any]  =							PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase_    :   Optional[int]  =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase_    :   Dict  =							["""input_ids""", """attention_mask"""]


	def __init__(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE="replace"						,     __SCREAMING_SNAKE_CASE="<s>"						,     __SCREAMING_SNAKE_CASE="</s>"						,     __SCREAMING_SNAKE_CASE="</s>"						,     __SCREAMING_SNAKE_CASE="<s>"						,     __SCREAMING_SNAKE_CASE="<unk>"						,     __SCREAMING_SNAKE_CASE="<pad>"						,     __SCREAMING_SNAKE_CASE="<mask>"						,     __SCREAMING_SNAKE_CASE=False						,     **__SCREAMING_SNAKE_CASE						,     )		->Tuple:
								lowerCAmelCase      						=						AddedToken(_A						,     lstrip=_A						,     rstrip=_A							) if isinstance(_A						,     _A							) else bos_token
								lowerCAmelCase      						=						AddedToken(_A						,     lstrip=_A						,     rstrip=_A							) if isinstance(_A						,     _A							) else eos_token
								lowerCAmelCase      						=						AddedToken(_A						,     lstrip=_A						,     rstrip=_A							) if isinstance(_A						,     _A							) else sep_token
								lowerCAmelCase      						=						AddedToken(_A						,     lstrip=_A						,     rstrip=_A							) if isinstance(_A						,     _A							) else cls_token
								lowerCAmelCase      						=						AddedToken(_A						,     lstrip=_A						,     rstrip=_A							) if isinstance(_A						,     _A							) else unk_token
								lowerCAmelCase      						=						AddedToken(_A						,     lstrip=_A						,     rstrip=_A							) if isinstance(_A						,     _A							) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								lowerCAmelCase      						=						AddedToken(_A						,     lstrip=_A						,     rstrip=_A							) if isinstance(_A						,     _A							) else mask_token

								super().__init__(
								    errors=_A						,     bos_token=_A						,     eos_token=_A						,     unk_token=_A						,     sep_token=_A						,     cls_token=_A						,     pad_token=_A						,     mask_token=_A						,     add_prefix_space=_A						,     **_A						,     )

								with open(_A						,     encoding='''utf-8'''							) as vocab_handle:
															lowerCAmelCase      						=						json.load(_A							)
								lowerCAmelCase      						=						{v: k for k, v in self.encoder.items()}
								lowerCAmelCase      						=						errors  # how to handle errors in decoding
								lowerCAmelCase      						=						bytes_to_unicode()
								lowerCAmelCase      						=						{v: k for k, v in self.byte_encoder.items()}
								with open(_A						,     encoding='''utf-8'''							) as merges_handle:
															lowerCAmelCase      						=						merges_handle.read().split('''\n'''							)[1:-1]
								lowerCAmelCase      						=						[tuple(merge.split()							) for merge in bpe_merges]
								lowerCAmelCase      						=						dict(zip(_A						,     range(len(_A							)							)							)							)
								lowerCAmelCase      						=						{}
								lowerCAmelCase      						=						add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								lowerCAmelCase      						=						re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''							)


	@property
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[Any]:
								return len(self.encoder							)


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Dict:
								return dict(self.encoder						,     **self.added_tokens_encoder							)


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE							)		->Union[str, Any]:
								if token in self.cache:
															return self.cache[token]
								lowerCAmelCase      						=						tuple(_A							)
								lowerCAmelCase      						=						get_pairs(_A							)

								if not pairs:
															return token

								while True:
															lowerCAmelCase      						=						min(_A						,     key=lambda __SCREAMING_SNAKE_CASE							: self.bpe_ranks.get(_A						,     float('''inf'''							)							)							)
															if bigram not in self.bpe_ranks:
																						break
															lowerCAmelCase						, lowerCAmelCase      						=						bigram
															lowerCAmelCase      						=						[]
															lowerCAmelCase      						=						0
															while i < len(_A							):
																						try:
																													lowerCAmelCase      						=						word.index(_A						,     _A							)
																						except ValueError:
																													new_word.extend(word[i:]							)
																													break
																						else:
																													new_word.extend(word[i:j]							)
																													lowerCAmelCase      						=						j

																						if word[i] == first and i < len(_A							) - 1 and word[i + 1] == second:
																													new_word.append(first + second							)
																													i += 2
																						else:
																													new_word.append(word[i]							)
																													i += 1
															lowerCAmelCase      						=						tuple(_A							)
															lowerCAmelCase      						=						new_word
															if len(_A							) == 1:
																						break
															else:
																						lowerCAmelCase      						=						get_pairs(_A							)
								lowerCAmelCase      						=						''' '''.join(_A							)
								lowerCAmelCase      						=						word
								return word


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE							)		->Tuple:
								lowerCAmelCase      						=						[]
								for token in re.findall(self.pat						,     _A							):
															lowerCAmelCase      						=						''''''.join(
															    self.byte_encoder[b] for b in token.encode('''utf-8'''							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A							).split(''' '''							)							)
								return bpe_tokens


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE							)		->Union[str, Any]:
								return self.encoder.get(_A						,     self.encoder.get(self.unk_token							)							)


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE							)		->List[Any]:
								return self.decoder.get(_A							)


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE							)		->List[str]:
								lowerCAmelCase      						=						''''''.join(_A							)
								lowerCAmelCase      						=						bytearray([self.byte_decoder[c] for c in text]							).decode('''utf-8'''						,     errors=self.errors							)
								return text


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE = None							)		->Tuple:
								if not os.path.isdir(_A							):
															logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
															return
								lowerCAmelCase      						=						os.path.join(
								    _A						,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
								lowerCAmelCase      						=						os.path.join(
								    _A						,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']							)

								with open(_A						,     '''w'''						,     encoding='''utf-8'''							) as f:
															f.write(json.dumps(self.encoder						,     indent=2						,     sort_keys=_A						,     ensure_ascii=_A							) + '''\n'''							)

								lowerCAmelCase      						=						0
								with open(_A						,     '''w'''						,     encoding='''utf-8'''							) as writer:
															writer.write('''#version: 0.2\n'''							)
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,     key=lambda __SCREAMING_SNAKE_CASE							: kv[1]							):
																						if index != token_index:
																													logger.warning(
																													    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																													    ''' Please check that the tokenizer is not corrupted!'''							)
																													lowerCAmelCase      						=						token_index
																						writer.write(''' '''.join(_A							) + '''\n'''							)
																						index += 1

								return vocab_file, merge_file


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE = None							)		->Dict:
								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowerCAmelCase      						=						[self.cls_token_id]
								lowerCAmelCase      						=						[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE = None						,     __SCREAMING_SNAKE_CASE = False							)		->Optional[int]:
								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      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE = None							)		->Any:
								lowerCAmelCase      						=						[self.sep_token_id]
								lowerCAmelCase      						=						[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      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE=False						,     **__SCREAMING_SNAKE_CASE							)		->Dict:
								lowerCAmelCase      						=						kwargs.pop('''add_prefix_space'''						,     self.add_prefix_space							)
								if (is_split_into_words or add_prefix_space) and (len(_A							) > 0 and not text[0].isspace()):
															lowerCAmelCase      						=						''' ''' + text
								return (text, kwargs)




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     lowerCAmelCase_     (       __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     ShapEPipeline
							UpperCAmelCase				   =     ["prompt"]
							UpperCAmelCase				   =     ["prompt"]
							UpperCAmelCase				   =     [
							    "num_images_per_prompt",
							    "num_inference_steps",
							    "generator",
							    "latents",
							    "guidance_scale",
							    "frame_size",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase				   =     False







							@property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												return 32







							@property
							def  UpperCamelCase_						(   self      :     int		):
												return 32







							@property
							def  UpperCamelCase_						(   self      :     List[str]		):
												return self.time_input_dim * 4







							@property
							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												return 8







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







							@property
							def  UpperCamelCase_						(   self      :     List[Any]		):
												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=1000 ,			)
												return CLIPTextModelWithProjection(_A		)







							@property
							def  UpperCamelCase_						(   self      :     int		):
												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      :     Union[str, Any]		):
												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      :     str		):
												_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=1024 ,			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      :     Tuple ,			_A      :     Tuple ,			_A      :     Optional[int]=0		):
												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		):
												_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      :     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  UpperCamelCase_						(   self      :     Any		):
												_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      :     Any		):
												_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     lowerCAmelCase_     (       unittest.TestCase ):







							def  UpperCamelCase_						(   self      :     str		):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()







							def  UpperCamelCase_						(   self      :     List[str]		):
												_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		)
