import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


lowerCamelCase_				:		Tuple       						=       """block_records.npy"""


lowerCamelCase_				:		Tuple       						=       logging.get_logger(__name__)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					import tensorflow.compat.va as tf

					__a							=   tf.data.TFRecordDataset(__lowerCamelCase      ,				buffer_size=512 * 1024 * 1024						)
					__a							=   blocks_dataset.batch(__lowerCamelCase      ,				drop_remainder=__lowerCamelCase						)
					__a							=   next(blocks_dataset.take(1						).as_numpy_iterator()						)

					return np_record


class 						a__							:



			def __init__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase=2      ,						UpperCAmelCase=1_0_0_0      ,						UpperCAmelCase=1_0_0      ,						UpperCAmelCase=1_0_0_0_0_0      ,						) ->    Optional[Any]:
								from scann.scann_ops.py.scann_ops_pybind import builder as Builder

								__a							=   Builder(db=UpperCAmelCase      ,						num_neighbors=UpperCAmelCase      ,						distance_measure='dot_product'    )
								__a							=   builder.tree(
								    num_leaves=UpperCAmelCase      ,						num_leaves_to_search=UpperCAmelCase      ,						training_sample_size=UpperCAmelCase    )
								__a							=   builder.score_ah(dimensions_per_block=UpperCAmelCase    )

								__a							=   builder.build()







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    List[str]:
								__a					,      __a							=   self.searcher.search_batched(question_projection.detach().cpu()    )
								return retrieved_block_ids.astype('int64'    )






class 						a__							:



			def __init__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Optional[int]:
								super().__init__()
								__a							=   block_records
								__a							=   tokenizer



			def __call__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase=None      ,						UpperCAmelCase="pt"    ) ->    Tuple:
								__a							=   np.take(self.block_records      ,						indices=UpperCAmelCase      ,						axis=0    )

								__a							=   self.tokenizer.decode(question_input_ids[0]      ,						skip_special_tokens=UpperCAmelCase    )

								__a							=   []
								__a							=   []
								for retrieved_block in retrieved_blocks:
													text.append(UpperCAmelCase    )
													text_pair.append(retrieved_block.decode()    )

								__a							=   self.tokenizer(
								    UpperCAmelCase      ,						UpperCAmelCase      ,						padding=UpperCAmelCase      ,						truncation=UpperCAmelCase      ,						return_special_tokens_mask=UpperCAmelCase      ,						max_length=UpperCAmelCase    )
								__a							=   concat_inputs.convert_to_tensors(UpperCAmelCase    )

								if answer_ids is not None:
													return self.block_has_answer(UpperCAmelCase      ,						UpperCAmelCase    ) + (concat_inputs_tensors,)
								else:
													return (None, None, None, concat_inputs_tensors)



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls      ,						UpperCAmelCase      ,						*UpperCAmelCase      ,						**UpperCAmelCase    ) ->    int:
								if os.path.isdir(UpperCAmelCase    ):
													__a							=   os.path.join(UpperCAmelCase      ,						_REALM_BLOCK_RECORDS_FILENAME    )
								else:
													__a							=   hf_hub_download(
													    repo_id=UpperCAmelCase      ,						filename=_REALM_BLOCK_RECORDS_FILENAME      ,						**UpperCAmelCase    )
								__a							=   np.load(UpperCAmelCase      ,						allow_pickle=UpperCAmelCase    )

								__a							=   AutoTokenizer.from_pretrained(UpperCAmelCase      ,						*UpperCAmelCase      ,						**UpperCAmelCase    )

								return cls(UpperCAmelCase      ,						UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Union[str, Any]:
								# save block records
								np.save(os.path.join(UpperCAmelCase      ,						_REALM_BLOCK_RECORDS_FILENAME    )      ,						self.block_records    )
								# save tokenizer
								self.tokenizer.save_pretrained(UpperCAmelCase    )







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Optional[Any]:
								__a							=   []
								__a							=   []
								__a							=   []
								__a							=   0

								for input_id in concat_inputs.input_ids:
													__a							=   input_id.tolist()
													# Check answers between two [SEP] tokens
													__a							=   input_id_list.index(self.tokenizer.sep_token_id    )
													__a							=   first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id    )

													start_pos.append([]    )
													end_pos.append([]    )
													for answer in answer_ids:
																		for idx in range(first_sep_idx + 1      ,						UpperCAmelCase    ):
																							if answer[0] == input_id_list[idx]:
																												if input_id_list[idx : idx + len(UpperCAmelCase    )] == answer:
																																	start_pos[-1].append(UpperCAmelCase    )
																																	end_pos[-1].append(idx + len(UpperCAmelCase    ) - 1    )

													if len(start_pos[-1]    ) == 0:
																		has_answers.append(UpperCAmelCase    )
													else:
																		has_answers.append(UpperCAmelCase    )
																		if len(start_pos[-1]    ) > max_answers:
																							__a							=   len(start_pos[-1]    )

        # Pad -1 to max_answers
								for start_pos_, end_pos_ in zip(UpperCAmelCase      ,						UpperCAmelCase    ):
													if len(start_pos_    ) < max_answers:
																		__a							=   [-1] * (max_answers - len(start_pos_    ))
																		start_pos_ += padded
																		end_pos_ += padded
								return has_answers, start_pos, end_pos











"""simple docstring"""


import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase					:		Optional[int]  =				{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				MODELS[model_size]  # set model hyperparameters
      _UpperCamelCase					:		str  =				ImageGPTConfig(n_embd=lowercase_						,n_layer=lowercase_						,n_head=lowercase_	)
      _UpperCamelCase					:		Dict  =				ImageGPTForCausalLM(lowercase_	)

      # Load weights from numpy
      load_tf_weights_in_imagegpt(lowercase_						,lowercase_						,lowercase_	)

      # Save pytorch-model
      _UpperCamelCase					:		str  =				pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      _UpperCamelCase					:		Any  =				pytorch_dump_folder_path + "/" + CONFIG_NAME
      print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
      torch.save(model.state_dict()						,lowercase_	)
      print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
      with open(lowercase_						,"w"						,encoding="utf-8"	) as f:
            f.write(config.to_json_string()	)


if __name__ == "__main__":
  lowerCamelCase__      =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--imagegpt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help="Path to the TensorFlow checkpoint path.",
  )
  parser.add_argument(
      "--model_size",
      default=None,
      type=str,
      required=True,
      help="Size of the model (can be either 'small', 'medium' or 'large').",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase__      =      parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )
