


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

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


__SCREAMING_SNAKE_CASE	:							Any           =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE	:							Union[str, Any]           =  {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

__SCREAMING_SNAKE_CASE	:							Tuple           =  {
    "vocab_file": {
        "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json",
    },
    "merges_file": {"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"},
}

__SCREAMING_SNAKE_CASE	:							Optional[Any]           =  {
    "microsoft/biogpt": 1_024,
}

def 		snake_case_   (				lowercase__     :    Union[str, Any]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=set()
				_lowerCAmelCase  						=word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)     )
								_lowerCAmelCase  						=char
				return pairs





class        __lowerCamelCase      (				_lowerCAmelCase ):


				"""simple docstring"""







				a_:		int					      =	VOCAB_FILES_NAMES
				a_:		List[Any]					      =	PRETRAINED_VOCAB_FILES_MAP
				a_:		Any					      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a_:		Any					      =	["""input_ids""", """attention_mask"""]






				def __init__(				self     :    int	,							lowerCamelCase_     :    Dict	,							lowerCamelCase_     :    str	,							lowerCamelCase_     :    Tuple="<unk>"	,							lowerCamelCase_     :    int="<s>"	,							lowerCamelCase_     :    Union[str, Any]="</s>"	,							lowerCamelCase_     :    str="</s>"	,							lowerCamelCase_     :    List[str]="<pad>"	,							**lowerCamelCase_     :    Dict	,							):
								super().__init__(
								    bos_token=__UpperCAmelCase	,							eos_token=__UpperCAmelCase	,							sep_token=__UpperCAmelCase	,							unk_token=__UpperCAmelCase	,							pad_token=__UpperCAmelCase	,							**__UpperCAmelCase	,							)

								try:
												import sacremoses
								except ImportError:
												raise ImportError(
												    """You need to install sacremoses to use BioGptTokenizer. """
												    """See https://pypi.org/project/sacremoses/ for installation."""					)

								_lowerCAmelCase  						='''en'''
								_lowerCAmelCase  						=sacremoses
								# cache of sm.MosesTokenizer instance
								_lowerCAmelCase  						={}
								_lowerCAmelCase  						={}
								with open(__UpperCAmelCase	,							encoding="""utf-8"""					) as vocab_handle:
												_lowerCAmelCase  						=json.load(__UpperCAmelCase					)
								_lowerCAmelCase  						={v: k for k, v in self.encoder.items()}
								with open(__UpperCAmelCase	,							encoding="""utf-8"""					) as merges_handle:
												_lowerCAmelCase  						=merges_handle.read().split("""\n"""					)[:-1]
								_lowerCAmelCase  						=[tuple(merge.split()[:2]					) for merge in merges]
								_lowerCAmelCase  						=dict(zip(__UpperCAmelCase	,							range(len(__UpperCAmelCase					)					)					)					)
								_lowerCAmelCase  						={}






				@property
				def        lowerCAmelCase__			(				self     :    str					):
								return len(self.encoder					)






				def        lowerCAmelCase__			(				self     :    Union[str, Any]					):
								return dict(self.encoder	,							**self.added_tokens_encoder					)






				def        lowerCAmelCase__			(				self     :    Optional[int]	,							lowerCamelCase_     :    Dict	,							lowerCamelCase_     :    Tuple					):



								if lang not in self.cache_moses_tokenizer:
												_lowerCAmelCase  						=self.sm.MosesTokenizer(lang=__UpperCAmelCase					)
												_lowerCAmelCase  						=moses_tokenizer
								return self.cache_moses_tokenizer[lang].tokenize(
								    __UpperCAmelCase	,							aggressive_dash_splits=__UpperCAmelCase	,							return_str=__UpperCAmelCase	,							escape=__UpperCAmelCase					)






				def        lowerCAmelCase__			(				self     :    Any	,							lowerCamelCase_     :    Dict	,							lowerCamelCase_     :    Optional[int]					):



								if lang not in self.cache_moses_detokenizer:
												_lowerCAmelCase  						=self.sm.MosesDetokenizer(lang=__UpperCAmelCase					)
												_lowerCAmelCase  						=moses_detokenizer
								return self.cache_moses_detokenizer[lang].detokenize(__UpperCAmelCase					)






				def        lowerCAmelCase__			(				self     :    str	,							lowerCamelCase_     :    Union[str, Any]					):
								_lowerCAmelCase  						=tuple(token[:-1]					) + (token[-1] + '''</w>''',)
								if token in self.cache:
												return self.cache[token]
								_lowerCAmelCase  						=get_pairs(__UpperCAmelCase					)

								if not pairs:
												return token + "</w>"

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

																if word[i] == first and i < len(__UpperCAmelCase					) - 1 and word[i + 1] == second:
																				new_word.append(first + second					)
																				i += 2
																else:
																				new_word.append(word[i]					)
																				i += 1
												_lowerCAmelCase  						=tuple(__UpperCAmelCase					)
												_lowerCAmelCase  						=new_word
												if len(__UpperCAmelCase					) == 1:
																break
												else:
																_lowerCAmelCase  						=get_pairs(__UpperCAmelCase					)
								_lowerCAmelCase  						=''' '''.join(__UpperCAmelCase					)
								if word == "\n  </w>":
												_lowerCAmelCase  						='''\n</w>'''
								_lowerCAmelCase  						=word
								return word






				def        lowerCAmelCase__			(				self     :    Dict	,							lowerCamelCase_     :    List[str]	,							lowerCamelCase_     :    Union[str, Any]=False					):



								if bypass_tokenizer:
												_lowerCAmelCase  						=text.split()
								else:
												_lowerCAmelCase  						=self.moses_tokenize(__UpperCAmelCase	,							self.lang					)

								_lowerCAmelCase  						=[]
								for token in text:
												if token:
																split_tokens.extend(list(self.bpe(__UpperCAmelCase					).split(""" """					)					)					)

								return split_tokens






				def        lowerCAmelCase__			(				self     :    List[Any]	,							lowerCamelCase_     :    str					):
								return self.encoder.get(__UpperCAmelCase	,							self.encoder.get(self.unk_token					)					)






				def        lowerCAmelCase__			(				self     :    Dict	,							lowerCamelCase_     :    List[str]					):
								return self.decoder.get(__UpperCAmelCase	,							self.unk_token					)






				def        lowerCAmelCase__			(				self     :    Dict	,							lowerCamelCase_     :    Optional[Any]					):
								_lowerCAmelCase  						=[t.replace(""" """	,							""""""					).replace("""</w>"""	,							""" """					) for t in tokens]
								_lowerCAmelCase  						=''''''.join(__UpperCAmelCase					).split()
								# detokenize
								_lowerCAmelCase  						=self.moses_detokenize(__UpperCAmelCase	,							self.lang					)
								return text






				def        lowerCAmelCase__			(				self     :    str	,							lowerCamelCase_     :    List[int]	,							lowerCamelCase_     :    Optional[List[int]] = None					):



								if token_ids_a is None:
												return [self.sep_token_id] + token_ids_a
								_lowerCAmelCase  						=[self.sep_token_id]
								return sep + token_ids_a + sep + token_ids_a






				def        lowerCAmelCase__			(				self     :    int	,							lowerCamelCase_     :    List[int]	,							lowerCamelCase_     :    Optional[List[int]] = None	,							lowerCamelCase_     :    bool = False					):



								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__UpperCAmelCase	,							token_ids_a=__UpperCAmelCase	,							already_has_special_tokens=__UpperCAmelCase					)
								# no bos used in fairseq
								if token_ids_a is not None:
												return [1] + ([0] * len(__UpperCAmelCase					)) + [1] + ([0] * len(__UpperCAmelCase					))
								return [1] + ([0] * len(__UpperCAmelCase					))






				def        lowerCAmelCase__			(				self     :    Tuple	,							lowerCamelCase_     :    List[int]	,							lowerCamelCase_     :    Optional[List[int]] = None					):
								_lowerCAmelCase  						=[self.sep_token_id]

								# no bos used in fairseq
								if token_ids_a is None:
												return len(token_ids_a + sep					) * [0]
								return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






				def        lowerCAmelCase__			(				self     :    Union[str, Any]	,							lowerCamelCase_     :    str	,							lowerCamelCase_     :    Optional[str] = None					):



								if not os.path.isdir(__UpperCAmelCase					):
												logger.error(F"Vocabulary path ({save_directory}) should be a directory"					)
												return
								_lowerCAmelCase  						=os.path.join(
								    __UpperCAmelCase	,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
								_lowerCAmelCase  						=os.path.join(
								    __UpperCAmelCase	,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]					)

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

								_lowerCAmelCase  						=0
								with open(__UpperCAmelCase	,							"""w"""	,							encoding="""utf-8"""					) as writer:
												for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,							key=lambda lowerCamelCase_					: 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(__UpperCAmelCase					) + """\n"""					)
																index += 1

								return vocab_file, merge_file






				def __getstate__(				self     :    Any					):
								_lowerCAmelCase  						=self.__dict__.copy()
								_lowerCAmelCase  						=None
								return state






				def __setstate__(				self     :    Union[str, Any]	,							lowerCamelCase_     :    str					):
								_lowerCAmelCase  						=d

								try:
												import sacremoses
								except ImportError:
												raise ImportError(
												    """You need to install sacremoses to use XLMTokenizer. """
												    """See https://pypi.org/project/sacremoses/ for installation."""					)

								_lowerCAmelCase  						=sacremoses


"""simple docstring"""




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase						,       _lowerCAmelCase						,       unittest.TestCase							):
				UpperCAmelCase	=      StableDiffusionParadigmsPipeline
				UpperCAmelCase	=      TEXT_TO_IMAGE_PARAMS
				UpperCAmelCase	=      TEXT_TO_IMAGE_BATCH_PARAMS
				UpperCAmelCase	=      TEXT_TO_IMAGE_IMAGE_PARAMS
				UpperCAmelCase	=      TEXT_TO_IMAGE_IMAGE_PARAMS







				def   lowercase_		(    self    :List[Any]  )      ->					Tuple:




									"""simple docstring"""



									torch.manual_seed(0  )
									lowerCamelCase__		:   int  				=					UNetaDConditionModel(
									    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=4      ,out_channels=4      ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,cross_attention_dim=32      ,attention_head_dim=(2, 4)      ,use_linear_projection=__UpperCAmelCase      ,)
									lowerCamelCase__		:   List[Any]  				=					DDIMParallelScheduler(
									    beta_start=0.00_085      ,beta_end=0.012      ,beta_schedule='''scaled_linear'''      ,clip_sample=__UpperCAmelCase      ,set_alpha_to_one=__UpperCAmelCase      ,)
									torch.manual_seed(0  )
									lowerCamelCase__		:   Dict  				=					AutoencoderKL(
									    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,latent_channels=4      ,sample_size=1_28      ,)
									torch.manual_seed(0  )
									lowerCamelCase__		:   Any  				=					CLIPTextConfig(
									    bos_token_id=0      ,eos_token_id=2      ,hidden_size=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,pad_token_id=1      ,vocab_size=10_00      ,hidden_act='''gelu'''      ,projection_dim=5_12      ,)
									lowerCamelCase__		:   Tuple  				=					CLIPTextModel(__UpperCAmelCase  )
									lowerCamelCase__		:   int  				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

									lowerCamelCase__		:   List[Any]  				=					{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components







				def   lowercase_		(    self    :Tuple      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :Dict=0  )      ->					Optional[int]:




									"""simple docstring"""



									if str(__UpperCAmelCase  ).startswith('''mps'''  ):
														lowerCamelCase__		:   Dict  				=					torch.manual_seed(__UpperCAmelCase  )
									else:
														lowerCamelCase__		:   List[str]  				=					torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[int]  				=					{
									    '''prompt''': '''a photograph of an astronaut riding a horse''',
									    '''generator''': generator,
									    '''num_inference_steps''': 10,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									    '''parallel''': 3,
									    '''debug''': True,
									}
									return inputs







				def   lowercase_		(    self    :Optional[int]  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									lowerCamelCase__		:   int  				=					self.get_dummy_components()
									lowerCamelCase__		:   int  				=					StableDiffusionParadigmsPipeline(**__UpperCAmelCase  )
									lowerCamelCase__		:   Union[str, Any]  				=					sd_pipe.to(__UpperCAmelCase  )
									sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

									lowerCamelCase__		:   Dict  				=					self.get_dummy_inputs(__UpperCAmelCase  )
									lowerCamelCase__		:   Tuple  				=					sd_pipe(**__UpperCAmelCase  ).images
									lowerCamelCase__		:   List[Any]  				=					image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									lowerCamelCase__		:   Tuple  				=					np.array([0.4_773, 0.5_417, 0.4_723, 0.4_925, 0.5_631, 0.4_752, 0.5_240, 0.4_935, 0.5_023]  )

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







				def   lowercase_		(    self    :int  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   Tuple  				=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									lowerCamelCase__		:   Optional[int]  				=					self.get_dummy_components()
									torch.manual_seed(0  )
									lowerCamelCase__		:   Any  				=					DDPMParallelScheduler()
									torch.manual_seed(0  )
									lowerCamelCase__		:   str  				=					StableDiffusionParadigmsPipeline(**__UpperCAmelCase  )
									lowerCamelCase__		:   Tuple  				=					sd_pipe.to(__UpperCAmelCase  )
									sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

									lowerCamelCase__		:   int  				=					self.get_dummy_inputs(__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[int]  				=					sd_pipe(**__UpperCAmelCase  ).images
									lowerCamelCase__		:   int  				=					image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									lowerCamelCase__		:   Optional[int]  				=					np.array([0.3_573, 0.4_420, 0.4_960, 0.4_799, 0.3_796, 0.3_879, 0.4_819, 0.4_365, 0.4_468]  )

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







				def   lowercase_		(    self    :List[str]  )      ->					Dict:




									"""simple docstring"""



									super().test_inference_batch_consistent(batch_sizes=[1, 2]  )







				def   lowercase_		(    self    :Any  )      ->					str:




									"""simple docstring"""



									super().test_inference_batch_single_identical(batch_size=2      ,expected_max_diff=3E-3  )







				def   lowercase_		(    self    :int  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									lowerCamelCase__		:   str  				=					self.get_dummy_components()
									lowerCamelCase__		:   int  				=					StableDiffusionParadigmsPipeline(**__UpperCAmelCase  )
									lowerCamelCase__		:   List[Any]  				=					sd_pipe.to(__UpperCAmelCase  )
									sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

									lowerCamelCase__		:   int  				=					self.get_dummy_inputs(__UpperCAmelCase  )
									lowerCamelCase__		:   int  				=					'''french fries'''
									lowerCamelCase__		:   Any  				=					sd_pipe(**__UpperCAmelCase      ,negative_prompt=__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[Any]  				=					output.images
									lowerCamelCase__		:   str  				=					image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									lowerCamelCase__		:   Tuple  				=					np.array([0.4_771, 0.5_420, 0.4_683, 0.4_918, 0.5_636, 0.4_725, 0.5_230, 0.4_923, 0.5_015]  )

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





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







				def   lowercase_		(    self    :Union[str, Any]  )      ->					Optional[int]:




									"""simple docstring"""



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







				def   lowercase_		(    self    :Optional[int]      ,__UpperCAmelCase    :List[str]=0  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
									lowerCamelCase__		:   List[Any]  				=					{
									    '''prompt''': '''a photograph of an astronaut riding a horse''',
									    '''generator''': generator,
									    '''num_inference_steps''': 10,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									    '''parallel''': 3,
									    '''debug''': True,
									}
									return inputs







				def   lowercase_		(    self    :Optional[Any]  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					'''stabilityai/stable-diffusion-2-base'''
									lowerCamelCase__		:   Tuple  				=					DDIMParallelScheduler.from_pretrained(__UpperCAmelCase      ,subfolder='''scheduler'''  )
									lowerCamelCase__		:   Optional[Any]  				=					StableDiffusionParadigmsPipeline.from_pretrained(__UpperCAmelCase      ,scheduler=__UpperCAmelCase      ,safety_checker=__UpperCAmelCase  )
									pipe.to(__UpperCAmelCase  )
									pipe.set_progress_bar_config(disable=__UpperCAmelCase  )
									pipe.enable_attention_slicing()

									lowerCamelCase__		:   Optional[Any]  				=					self.get_inputs()
									lowerCamelCase__		:   Tuple  				=					pipe(**__UpperCAmelCase  ).images
									lowerCamelCase__		:   List[Any]  				=					image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)

									lowerCamelCase__		:   List[str]  				=					np.array([0.9_622, 0.9_602, 0.9_748, 0.9_591, 0.9_630, 0.9_691, 0.9_661, 0.9_631, 0.9_741]  )

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






