
'''simple docstring'''

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

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


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

UpperCAmelCase__				:	List[Any]					=						{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

UpperCAmelCase__				:	List[Any]					=						{
    "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"},
}

UpperCAmelCase__				:	Tuple					=						{
    "microsoft/biogpt": 10_24,
}







def 					A       ( UpperCamelCase_   :       Optional[Any] )   ->    Optional[Any]:







			'''simple docstring'''



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






class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :List[str]				       =       VOCAB_FILES_NAMES
	snake_case__ :int				       =       PRETRAINED_VOCAB_FILES_MAP
	snake_case__ :Tuple				       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__ :int				       =       ['input_ids', 'attention_mask']





	def __init__(   self     :    Dict ,							__magic_name__     :    List[Any] ,							__magic_name__     :    List[Any] ,							__magic_name__     :    List[str]="<unk>" ,							__magic_name__     :    Optional[Any]="<s>" ,							__magic_name__     :    Dict="</s>" ,							__magic_name__     :    Optional[Any]="</s>" ,							__magic_name__     :    str="<pad>" ,							**__magic_name__     :    Union[str, Any] ,							):


				"""simple docstring"""

				super().__init__(
				    bos_token=__magic_name__ ,							eos_token=__magic_name__ ,							sep_token=__magic_name__ ,							unk_token=__magic_name__ ,							pad_token=__magic_name__ ,							**__magic_name__ ,							)

				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(__magic_name__ ,							encoding="utf-8"							) as vocab_handle:
							lowerCAmelCase__									=							json.load(__magic_name__							)
				lowerCAmelCase__									=							{v: k for k, v in self.encoder.items()}
				with open(__magic_name__ ,							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(__magic_name__ ,							range(len(__magic_name__							)							)							)							)
				lowerCAmelCase__									=							{}





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				return len(self.encoder							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				return dict(self.encoder ,							**self.added_tokens_encoder							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    Optional[Any]							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int] ,							__magic_name__     :    List[Any] ,							__magic_name__     :    Dict							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							tuple(token[:-1]							) + (token[-1] + "</w>",)
				if token in self.cache:
							return self.cache[token]
				lowerCAmelCase__									=							get_pairs(__magic_name__							)

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

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

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    int=False							):


				"""simple docstring"""

				if bypass_tokenizer:
							lowerCAmelCase__									=							text.split()
				else:
							lowerCAmelCase__									=							self.moses_tokenize(__magic_name__ ,							self.lang							)

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

				return split_tokens





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any] ,							__magic_name__     :    Tuple							):


				"""simple docstring"""

				return self.encoder.get(__magic_name__ ,							self.encoder.get(self.unk_token							)							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Tuple							):


				"""simple docstring"""

				return self.decoder.get(__magic_name__ ,							self.unk_token							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							[t.replace(" " ,							""							).replace("</w>" ,							" "							) for t in tokens]
				lowerCAmelCase__									=							"".join(__magic_name__							).split()
				# detokenize
				lowerCAmelCase__									=							self.moses_detokenize(__magic_name__ ,							self.lang							)
				return text





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							__magic_name__     :    List[int] ,							__magic_name__     :    Optional[List[int]] = None							):


				"""simple docstring"""

				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 							__SCREAMING_SNAKE_CASE					(   self     :    Any ,							__magic_name__     :    List[int] ,							__magic_name__     :    Optional[List[int]] = None ,							__magic_name__     :    bool = False							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str ,							__magic_name__     :    List[int] ,							__magic_name__     :    Optional[List[int]] = None							):


				"""simple docstring"""

				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 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any] ,							__magic_name__     :    str ,							__magic_name__     :    Optional[str] = None							):


				"""simple docstring"""

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

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

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

				return vocab_file, merge_file





	def __getstate__(   self     :    Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.__dict__.copy()
				lowerCAmelCase__									=							None
				return state







	def __setstate__(   self     :    int ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

				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'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :torch.FloatTensor






class 				A       (		SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__      ):





	@register_to_config
	def __init__(   self     :    Tuple ,							__magic_name__     :    Optional[Union[int, Tuple[int, int]]] = None ,							__magic_name__     :    int = 3 ,							__magic_name__     :    int = 3 ,							__magic_name__     :    bool = False ,							__magic_name__     :    str = "positional" ,							__magic_name__     :    int = 0 ,							__magic_name__     :    bool = True ,							__magic_name__     :    Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D") ,							__magic_name__     :    Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D") ,							__magic_name__     :    Tuple[int] = (224, 448, 672, 896) ,							__magic_name__     :    int = 2 ,							__magic_name__     :    float = 1 ,							__magic_name__     :    int = 1 ,							__magic_name__     :    str = "conv" ,							__magic_name__     :    str = "conv" ,							__magic_name__     :    str = "silu" ,							__magic_name__     :    Optional[int] = 8 ,							__magic_name__     :    int = 32 ,							__magic_name__     :    float = 1E-5 ,							__magic_name__     :    str = "default" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[str] = None ,							__magic_name__     :    Optional[int] = None ,							):


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__									=							sample_size
				lowerCAmelCase__									=							block_out_channels[0] * 4

				# Check inputs
				if len(__magic_name__							) != len(__magic_name__							):
							raise ValueError(
							    f"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."""							)

				if len(__magic_name__							) != len(__magic_name__							):
							raise ValueError(
							    f"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."""							)

				# input
				lowerCAmelCase__									=							nn.Convad(__magic_name__ ,							block_out_channels[0] ,							kernel_size=3 ,							padding=(1, 1)							)

				# time
				if time_embedding_type == "fourier":
							lowerCAmelCase__									=							GaussianFourierProjection(embedding_size=block_out_channels[0] ,							scale=16							)
							lowerCAmelCase__									=							2 * block_out_channels[0]
				elif time_embedding_type == "positional":
							lowerCAmelCase__									=							Timesteps(block_out_channels[0] ,							__magic_name__ ,							__magic_name__							)
							lowerCAmelCase__									=							block_out_channels[0]

				lowerCAmelCase__									=							TimestepEmbedding(__magic_name__ ,							__magic_name__							)

				# class embedding
				if class_embed_type is None and num_class_embeds is not None:
							lowerCAmelCase__									=							nn.Embedding(__magic_name__ ,							__magic_name__							)
				elif class_embed_type == "timestep":
							lowerCAmelCase__									=							TimestepEmbedding(__magic_name__ ,							__magic_name__							)
				elif class_embed_type == "identity":
							lowerCAmelCase__									=							nn.Identity(__magic_name__ ,							__magic_name__							)
				else:
							lowerCAmelCase__									=							None

				lowerCAmelCase__									=							nn.ModuleList([]							)
				lowerCAmelCase__									=							None
				lowerCAmelCase__									=							nn.ModuleList([]							)

				# down
				lowerCAmelCase__									=							block_out_channels[0]
				for i, down_block_type in enumerate(__magic_name__							):
							lowerCAmelCase__									=							output_channel
							lowerCAmelCase__									=							block_out_channels[i]
							lowerCAmelCase__									=							i == len(__magic_name__							) - 1

							lowerCAmelCase__									=							get_down_block(
							    __magic_name__ ,							num_layers=__magic_name__ ,							in_channels=__magic_name__ ,							out_channels=__magic_name__ ,							temb_channels=__magic_name__ ,							add_downsample=not is_final_block ,							resnet_eps=__magic_name__ ,							resnet_act_fn=__magic_name__ ,							resnet_groups=__magic_name__ ,							attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel ,							downsample_padding=__magic_name__ ,							resnet_time_scale_shift=__magic_name__ ,							downsample_type=__magic_name__ ,							)
							self.down_blocks.append(__magic_name__							)

				# mid
				lowerCAmelCase__									=							UNetMidBlockaD(
				    in_channels=block_out_channels[-1] ,							temb_channels=__magic_name__ ,							resnet_eps=__magic_name__ ,							resnet_act_fn=__magic_name__ ,							output_scale_factor=__magic_name__ ,							resnet_time_scale_shift=__magic_name__ ,							attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1] ,							resnet_groups=__magic_name__ ,							add_attention=__magic_name__ ,							)

				# up
				lowerCAmelCase__									=							list(reversed(__magic_name__							)							)
				lowerCAmelCase__									=							reversed_block_out_channels[0]
				for i, up_block_type in enumerate(__magic_name__							):
							lowerCAmelCase__									=							output_channel
							lowerCAmelCase__									=							reversed_block_out_channels[i]
							lowerCAmelCase__									=							reversed_block_out_channels[min(i + 1 ,							len(__magic_name__							) - 1							)]

							lowerCAmelCase__									=							i == len(__magic_name__							) - 1

							lowerCAmelCase__									=							get_up_block(
							    __magic_name__ ,							num_layers=layers_per_block + 1 ,							in_channels=__magic_name__ ,							out_channels=__magic_name__ ,							prev_output_channel=__magic_name__ ,							temb_channels=__magic_name__ ,							add_upsample=not is_final_block ,							resnet_eps=__magic_name__ ,							resnet_act_fn=__magic_name__ ,							resnet_groups=__magic_name__ ,							attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel ,							resnet_time_scale_shift=__magic_name__ ,							upsample_type=__magic_name__ ,							)
							self.up_blocks.append(__magic_name__							)
							lowerCAmelCase__									=							output_channel

				# out
				lowerCAmelCase__									=							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,							32							)
				lowerCAmelCase__									=							nn.GroupNorm(num_channels=block_out_channels[0] ,							num_groups=__magic_name__ ,							eps=__magic_name__							)
				lowerCAmelCase__									=							nn.SiLU()
				lowerCAmelCase__									=							nn.Convad(block_out_channels[0] ,							__magic_name__ ,							kernel_size=3 ,							padding=1							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    torch.FloatTensor ,							__magic_name__     :    Union[torch.Tensor, float, int] ,							__magic_name__     :    Optional[torch.Tensor] = None ,							__magic_name__     :    bool = True ,							):


				"""simple docstring"""

				if self.config.center_input_sample:
							lowerCAmelCase__									=							2 * sample - 1.0

				# 1. time
				lowerCAmelCase__									=							timestep
				if not torch.is_tensor(__magic_name__							):
							lowerCAmelCase__									=							torch.tensor([timesteps] ,							dtype=torch.long ,							device=sample.device							)
				elif torch.is_tensor(__magic_name__							) and len(timesteps.shape							) == 0:
							lowerCAmelCase__									=							timesteps[None].to(sample.device							)

				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
				lowerCAmelCase__									=							timesteps * torch.ones(sample.shape[0] ,							dtype=timesteps.dtype ,							device=timesteps.device							)

				lowerCAmelCase__									=							self.time_proj(__magic_name__							)

				# timesteps does not contain any weights and will always return f32 tensors
				# but time_embedding might actually be running in fp16. so we need to cast here.
				# there might be better ways to encapsulate this.
				lowerCAmelCase__									=							t_emb.to(dtype=self.dtype							)
				lowerCAmelCase__									=							self.time_embedding(__magic_name__							)

				if self.class_embedding is not None:
							if class_labels is None:
										raise ValueError("class_labels should be provided when doing class conditioning"							)

							if self.config.class_embed_type == "timestep":
										lowerCAmelCase__									=							self.time_proj(__magic_name__							)

							lowerCAmelCase__									=							self.class_embedding(__magic_name__							).to(dtype=self.dtype							)
							lowerCAmelCase__									=							emb + class_emb

				# 2. pre-process
				lowerCAmelCase__									=							sample
				lowerCAmelCase__									=							self.conv_in(__magic_name__							)

				# 3. down
				lowerCAmelCase__									=							(sample,)
				for downsample_block in self.down_blocks:
							if hasattr(__magic_name__ ,							"skip_conv"							):
										lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__									=							downsample_block(
										    hidden_states=__magic_name__ ,							temb=__magic_name__ ,							skip_sample=__magic_name__							)
							else:
										lowerCAmelCase__		,lowerCAmelCase__									=							downsample_block(hidden_states=__magic_name__ ,							temb=__magic_name__							)

							down_block_res_samples += res_samples

				# 4. mid
				lowerCAmelCase__									=							self.mid_block(__magic_name__ ,							__magic_name__							)

				# 5. up
				lowerCAmelCase__									=							None
				for upsample_block in self.up_blocks:
							lowerCAmelCase__									=							down_block_res_samples[-len(upsample_block.resnets							) :]
							lowerCAmelCase__									=							down_block_res_samples[: -len(upsample_block.resnets							)]

							if hasattr(__magic_name__ ,							"skip_conv"							):
										lowerCAmelCase__		,lowerCAmelCase__									=							upsample_block(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__							)
							else:
										lowerCAmelCase__									=							upsample_block(__magic_name__ ,							__magic_name__ ,							__magic_name__							)

        # 6. post-process
				lowerCAmelCase__									=							self.conv_norm_out(__magic_name__							)
				lowerCAmelCase__									=							self.conv_act(__magic_name__							)
				lowerCAmelCase__									=							self.conv_out(__magic_name__							)

				if skip_sample is not None:
							sample += skip_sample

				if self.config.time_embedding_type == "fourier":
							lowerCAmelCase__									=							timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]							)))							)
							lowerCAmelCase__									=							sample / timesteps

				if not return_dict:
							return (sample,)

				return UNetaDOutput(sample=__magic_name__							)





