




"""simple docstring"""




import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowerCAmelCase							:			Optional[int]     							=							2






class     __magic_name__							:






				'''simple docstring'''





				def __init__(       self		, *,  # begin keyword-only arguments
				    _a="<s>"		, _a="<pad>"		, _a="</s>"		, _a="<unk>"		, _a=None		, ):





							"""simple docstring"""
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					bos, unk, pad, eos
							lowerCamelCase			      =					[]
							lowerCamelCase			      =					[]
							lowerCamelCase			      =					{}
							lowerCamelCase			      =					self.add_symbol(_a )
							lowerCamelCase			      =					self.add_symbol(_a )
							lowerCamelCase			      =					self.add_symbol(_a )
							lowerCamelCase			      =					self.add_symbol(_a )
							if extra_special_symbols:
										for s in extra_special_symbols:
													self.add_symbol(_a )
							lowerCamelCase			      =					len(self.symbols )





				def __eq__(       self		, _a ):





							"""simple docstring"""
							return self.indices == other.indices





				def __getitem__(       self		, _a ):





							"""simple docstring"""
							if idx < len(self.symbols ):
										return self.symbols[idx]
							return self.unk_word





				def __len__(       self ):





							"""simple docstring"""
							return len(self.symbols )





				def __contains__(       self		, _a ):





							"""simple docstring"""
							return sym in self.indices





				@classmethod
				def    _lowerCAmelCase				(       cls		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					cls()
							d.add_from_file(_a )
							return d





				def    _lowerCAmelCase				(       self		, _a		, _a=1		, _a=False ):





							"""simple docstring"""
							if word in self.indices and not overwrite:
										lowerCamelCase			      =					self.indices[word]
										lowerCamelCase			      =					self.count[idx] + n
										return idx
							else:
										lowerCamelCase			      =					len(self.symbols )
										lowerCamelCase			      =					idx
										self.symbols.append(_a )
										self.count.append(_a )
										return idx





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							return 0





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							if isinstance(_a		, _a ):
										try:
													with open(_a		, """r"""		, encoding="""utf-8""" ) as fd:
																self.add_from_file(_a )
										except FileNotFoundError as fnfe:
													raise fnfe
										except UnicodeError:
													raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(_a ) )
										return

							lowerCamelCase			      =					f.readlines()
							lowerCamelCase			      =					self._load_meta(_a )

							for line in lines[indices_start_line:]:
										try:
													lowerCamelCase      ,   lowerCamelCase			      =					line.rstrip().rsplit(""" """		, 1 )
													if field == "#fairseq:overwrite":
																lowerCamelCase			      =					True
																lowerCamelCase      ,   lowerCamelCase			      =					line.rsplit(""" """		, 1 )
													else:
																lowerCamelCase			      =					False
													lowerCamelCase			      =					int(_a )
													lowerCamelCase			      =					line
													if word in self and not overwrite:
																raise RuntimeError(
																    """Duplicate word found when loading Dictionary: '{}'. """
																    """Duplicate words can overwrite earlier ones by adding the """
																    """#fairseq:overwrite flag at the end of the corresponding row """
																    """in the dictionary file. If using the Camembert model, please """
																    """download an updated copy of the model file.""".format(_a ) )
													self.add_symbol(_a		, n=_a		, overwrite=_a )
										except ValueError:
													raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'""" )




def     a__ (				snake_case__				)						->					Tuple:
			# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
			# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
			lowerCamelCase			      =					dict((re.sub(R"""@@$"""       , """"""       , snake_case__				), v) if k.endswith("""@@"""				) else (re.sub(R"""$"""       , """</w>"""       , snake_case__				), v) for k, v in d.items()				)
			lowerCamelCase			      =					"""<s> <pad> </s> <unk>""".split()
			# restore the special tokens
			for k in keep_keys:
						del da[F'{k}</w>']
						lowerCamelCase			      =					d[k]  # restore
			return da



def     a__ (				snake_case__       , snake_case__				)						->					Dict:
			# prep
			if not os.path.exists(snake_case__				):
						raise ValueError(F'path {biogpt_checkpoint_path} does not exist!'				)
			os.makedirs(snake_case__       , exist_ok=snake_case__				)
			print(F'Writing results to {pytorch_dump_folder_path}'				)

			# handle various types of models

			lowerCamelCase			      =					os.path.join(snake_case__       , """checkpoint.pt"""				)
			if not os.path.isfile(snake_case__				):
						raise ValueError(F'path to the file {checkpoint_file} does not exist!'				)
			lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)

			lowerCamelCase			      =					chkpt["""cfg"""]["""model"""]

			# dicts
			lowerCamelCase			      =					os.path.join(snake_case__       , """dict.txt"""				)
			if not os.path.isfile(snake_case__				):
						raise ValueError(F'path to the file {dict_file} does not exist!'				)
			lowerCamelCase			      =					Dictionary.load(snake_case__				)
			lowerCamelCase			      =					rewrite_dict_keys(src_dict.indices				)
			lowerCamelCase			      =					len(snake_case__				)
			lowerCamelCase			      =					os.path.join(snake_case__       , VOCAB_FILES_NAMES["""vocab_file"""]				)
			print(F'Generating {src_vocab_file} of {src_vocab_size} records'				)
			with open(snake_case__       , """w"""       , encoding="""utf-8"""				) as f:
						f.write(json.dumps(snake_case__       , ensure_ascii=snake_case__       , indent=snake_case__				)				)

			# merges_file (bpecodes)
			lowerCamelCase			      =					os.path.join(snake_case__       , """bpecodes"""				)
			if not os.path.isfile(snake_case__				):
						raise ValueError(F'path to the file {bpecodes_file} does not exist!'				)

			lowerCamelCase			      =					os.path.join(snake_case__       , VOCAB_FILES_NAMES["""merges_file"""]				)
			shutil.copyfile(snake_case__       , snake_case__				)

			# model config
			lowerCamelCase			      =					os.path.join(snake_case__       , """config.json"""				)

			lowerCamelCase			      =					{
			    """activation_dropout""": args["""activation_dropout"""],
			    """architectures""": ["""BioGptForCausalLM"""],
			    """attention_probs_dropout_prob""": args["""attention_dropout"""],
			    """bos_token_id""": 0,
			    """eos_token_id""": 2,
			    """hidden_act""": args["""activation_fn"""],
			    """hidden_dropout_prob""": args["""dropout"""],
			    """hidden_size""": args["""decoder_embed_dim"""],
			    """initializer_range""": 0.02,
			    """intermediate_size""": args["""decoder_ffn_embed_dim"""],
			    """layer_norm_eps""": 1E-12,
			    """layerdrop""": args["""decoder_layerdrop"""],
			    """max_position_embeddings""": args["""max_target_positions"""],
			    """model_type""": """biogpt""",
			    """num_attention_heads""": args["""decoder_attention_heads"""],
			    """num_hidden_layers""": args["""decoder_layers"""],
			    """pad_token_id""": 1,
			    """scale_embedding""": not args["""no_scale_embedding"""],
			    """tie_word_embeddings""": args["""share_decoder_input_output_embed"""],
			    """vocab_size""": src_vocab_size,
			}

			# good hparam defaults to start with

			print(F'Generating {biogpt_model_config_file}'				)
			with open(snake_case__       , """w"""       , encoding="""utf-8"""				) as f:
						f.write(json.dumps(snake_case__       , ensure_ascii=snake_case__       , indent=snake_case__				)				)

			# tokenizer config
			lowerCamelCase			      =					os.path.join(snake_case__       , snake_case__				)

			lowerCamelCase			      =					{
			    """bos_token""": """<s>""",
			    """eos_token""": """</s>""",
			    """model_max_length""": 10_24,
			    """pad_token""": """<pad>""",
			    """special_tokens_map_file""": None,
			    """tokenizer_class""": """BioGptTokenizer""",
			    """unk_token""": """<unk>""",
			}

			print(F'Generating {biogpt_tokenizer_config_file}'				)
			with open(snake_case__       , """w"""       , encoding="""utf-8"""				) as f:
						f.write(json.dumps(snake_case__       , ensure_ascii=snake_case__       , indent=snake_case__				)				)

			# model
			lowerCamelCase			      =					chkpt["""model"""]

			# remove unneeded keys
			lowerCamelCase			      =					[
			    """decoder.version""",
			]
			for k in ignore_keys:
						model_state_dict.pop(snake_case__       , snake_case__				)

			lowerCamelCase			      =					list(model_state_dict.keys()				)
			for layer_name in layer_names:
						if layer_name.endswith("""output_projection.weight"""				):
									lowerCamelCase			      =					model_state_dict.pop(snake_case__				)
						else:
									lowerCamelCase			      =					model_state_dict.pop(snake_case__				)

			lowerCamelCase			      =					BioGptConfig.from_pretrained(snake_case__				)
			lowerCamelCase			      =					BioGptForCausalLM(snake_case__				)

			# check that it loads ok
			model_new.load_state_dict(snake_case__				)

			# save
			lowerCamelCase			      =					os.path.join(snake_case__       , snake_case__				)
			print(F'Generating {pytorch_weights_dump_path}'				)
			torch.save(snake_case__       , snake_case__				)

			print("""Conversion is done!"""				)


if __name__ == "__main__":
					lowerCAmelCase							:			Optional[int]     							=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--biogpt_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
					        """ bpecodes, etc."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					lowerCAmelCase							:			Tuple     							=							parser.parse_args()
					convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
					import torch


lowerCAmelCase							:			int     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						["pixel_values"]





				def __init__(       self		, _a = True		, _a = None		, _a = PILImageResampling.BILINEAR		, _a = True		, _a = None		, _a = True		, _a = 1 / 255		, _a = True		, _a = None		, _a = None		, **_a		, ):





							"""simple docstring"""
							super().__init__(**_a )
							lowerCamelCase			      =					size if size is not None else {"""shortest_edge""": 256}
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							lowerCamelCase			      =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							lowerCamelCase			      =					get_size_dict(_a		, param_name="""crop_size""" )
							lowerCamelCase			      =					do_resize
							lowerCamelCase			      =					size
							lowerCamelCase			      =					resample
							lowerCamelCase			      =					do_center_crop
							lowerCamelCase			      =					crop_size
							lowerCamelCase			      =					do_rescale
							lowerCamelCase			      =					rescale_factor
							lowerCamelCase			      =					do_normalize
							lowerCamelCase			      =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCamelCase			      =					image_std if image_std is not None else IMAGENET_STANDARD_STD





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = PILImageResampling.BICUBIC		, _a = None		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							if "shortest_edge" not in size:
										raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
							lowerCamelCase			      =					get_resize_output_image_size(_a		, size=size["""shortest_edge"""]		, default_to_square=_a )
							return resize(_a		, size=_a		, resample=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = None		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					get_size_dict(_a )
							if "height" not in size or "width" not in size:
										raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' )
							return center_crop(_a		, size=(size["""height"""], size["""width"""])		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = None		, **_a ):





							"""simple docstring"""
							return rescale(_a		, scale=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a = None		, **_a		, ):





							"""simple docstring"""
							return normalize(_a		, mean=_a		, std=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = ChannelDimension.FIRST		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					do_resize if do_resize is not None else self.do_resize
							lowerCamelCase			      =					size if size is not None else self.size
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							lowerCamelCase			      =					resample if resample is not None else self.resample
							lowerCamelCase			      =					do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCamelCase			      =					crop_size if crop_size is not None else self.crop_size
							lowerCamelCase			      =					get_size_dict(_a		, param_name="""crop_size""" )
							lowerCamelCase			      =					do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase			      =					rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase			      =					do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase			      =					image_mean if image_mean is not None else self.image_mean
							lowerCamelCase			      =					image_std if image_std is not None else self.image_std

							lowerCamelCase			      =					make_list_of_images(_a )

							if not valid_images(_a ):
										raise ValueError(
										    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
										    """torch.Tensor, tf.Tensor or jax.ndarray.""" )

							if do_resize and size is None:
										raise ValueError("""Size must be specified if do_resize is True.""" )

							if do_center_crop and crop_size is None:
										raise ValueError("""Crop size must be specified if do_center_crop is True.""" )

							if do_rescale and rescale_factor is None:
										raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )

							if do_normalize and (image_mean is None or image_std is None):
										raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )

							# All transformations expect numpy arrays.
							lowerCamelCase			      =					[to_numpy_array(_a ) for image in images]

							if do_resize:
										lowerCamelCase			      =					[self.resize(image=_a		, size=_a		, resample=_a ) for image in images]

							if do_center_crop:
										lowerCamelCase			      =					[self.center_crop(image=_a		, size=_a ) for image in images]

							if do_rescale:
										lowerCamelCase			      =					[self.rescale(image=_a		, scale=_a ) for image in images]

							if do_normalize:
										lowerCamelCase			      =					[self.normalize(image=_a		, mean=_a		, std=_a ) for image in images]

							lowerCamelCase			      =					[to_channel_dimension_format(_a		, _a ) for image in images]

							lowerCamelCase			      =					{"""pixel_values""": images}
							return BatchFeature(data=_a		, tensor_type=_a )





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							lowerCamelCase			      =					outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
										if len(_a ) != len(_a ):
													raise ValueError(
													    """Make sure that you pass in as many target sizes as the batch dimension of the logits""" )

										if is_torch_tensor(_a ):
													lowerCamelCase			      =					target_sizes.numpy()

										lowerCamelCase			      =					[]

										for idx in range(len(_a ) ):
													lowerCamelCase			      =					torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0 )		, size=target_sizes[idx]		, mode="""bilinear"""		, align_corners=_a )
													lowerCamelCase			      =					resized_logits[0].argmax(dim=0 )
													semantic_segmentation.append(_a )
							else:
										lowerCamelCase			      =					logits.argmax(dim=1 )
										lowerCamelCase			      =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

							return semantic_segmentation



