






import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
	from tokenizers import Tokenizer
	from tokenizers.models import WordPiece


class        lowercase__       (     unittest.TestCase		):

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  list(tokenizer_class.max_model_input_sizes.keys()    )
							for model_name in sa_models[:1]:
													lowerCAmelCase__	 =  tokenizer_class.from_pretrained(lowercase__    )
													self.assertIsNotNone(lowercase__    )
													self.assertIsInstance(lowercase__						,      lowercase__    )
													self.assertIsInstance(lowercase__						,      lowercase__    )

													for special_tok in tokenizer.all_special_tokens:
																			self.assertIsInstance(lowercase__						,      lowercase__    )
																			lowerCAmelCase__	 =  tokenizer.convert_tokens_to_ids(lowercase__    )
																			self.assertIsInstance(lowercase__						,      lowercase__    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase = None    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  pickle.dumps(lowercase__    )
							self.assertIsNotNone(lowercase__    )

							lowerCAmelCase__	 =  pickle.loads(lowercase__    )

							# Ensure is_fast is correctly restored
							self.assertEqual(be_restored.is_fast						,      be_original.is_fast    )

							# Ensure encodings are potentially correctly restored
							if be_original.is_fast:
													self.assertIsNotNone(be_restored.encodings    )
							else:
													self.assertIsNone(be_restored.encodings    )

							# Ensure the keys are the same
							for original_v, restored_v in zip(be_original.values()						,      be_restored.values()    ):
													if equal_op:
																			self.assertTrue(equal_op(lowercase__						,      lowercase__    )    )
													else:
																			self.assertEqual(lowercase__						,      lowercase__    )

	@slow
	def 						UpperCAmelCase					(							self    )->   str:







							'''simple docstring'''





							self.check_tokenizer_from_pretrained(lowercase__    )

	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							self.assertEqual(TensorType("tf"    )						,      TensorType.TENSORFLOW    )
							self.assertEqual(TensorType("pt"    )						,      TensorType.PYTORCH    )
							self.assertEqual(TensorType("np"    )						,      TensorType.NUMPY    )

	@require_tokenizers
	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							import numpy as np

							lowerCAmelCase__	 =  BertTokenizer.from_pretrained("bert-base-cased"    )
							lowerCAmelCase__	 =  BertTokenizerFast.from_pretrained("bert-base-cased"    )

							# Python no tensor
							with self.subTest("BatchEncoding (Python, return_tensors=None)"    ):
													self.assert_dump_and_restore(tokenizer_p("Small example to encode"    )    )

							with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"    ):
													self.assert_dump_and_restore(
													    tokenizer_p("Small example to encode"						,      return_tensors=TensorType.NUMPY    )						,      np.array_equal    )

							with self.subTest("BatchEncoding (Rust, return_tensors=None)"    ):
													self.assert_dump_and_restore(tokenizer_r("Small example to encode"    )    )

							with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"    ):
													self.assert_dump_and_restore(
													    tokenizer_r("Small example to encode"						,      return_tensors=TensorType.NUMPY    )						,      np.array_equal    )

	@require_tf
	@require_tokenizers
	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							import tensorflow as tf

							def tf_array_equals(__UpperCAmelCase						,      __UpperCAmelCase    ):
													return tf.reduce_all(tf.equal(lowercase__						,      lowercase__    )    )

							lowerCAmelCase__	 =  BertTokenizer.from_pretrained("bert-base-cased"    )
							lowerCAmelCase__	 =  BertTokenizerFast.from_pretrained("bert-base-cased"    )

							with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"    ):
													self.assert_dump_and_restore(
													    tokenizer_p("Small example to encode"						,      return_tensors=TensorType.TENSORFLOW    )						,      lowercase__    )

							with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"    ):
													self.assert_dump_and_restore(
													    tokenizer_r("Small example to encode"						,      return_tensors=TensorType.TENSORFLOW    )						,      lowercase__    )

	@require_torch
	@require_tokenizers
	def 						UpperCAmelCase					(							self    )->   Tuple:







							'''simple docstring'''





							import torch

							lowerCAmelCase__	 =  BertTokenizer.from_pretrained("bert-base-cased"    )
							lowerCAmelCase__	 =  BertTokenizerFast.from_pretrained("bert-base-cased"    )

							with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"    ):
													self.assert_dump_and_restore(
													    tokenizer_p("Small example to encode"						,      return_tensors=TensorType.PYTORCH    )						,      torch.equal    )

							with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"    ):
													self.assert_dump_and_restore(
													    tokenizer_r("Small example to encode"						,      return_tensors=TensorType.PYTORCH    )						,      torch.equal    )

	@require_tokenizers
	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  BertTokenizer.from_pretrained("bert-base-cased"    )
							lowerCAmelCase__	 =  BertTokenizerFast.from_pretrained("bert-base-cased"    )

							with self.subTest("Python Tokenizer"    ):
													self.assertFalse(tokenizer_p("Small example to_encode"    ).is_fast    )

							with self.subTest("Rust Tokenizer"    ):
													self.assertTrue(tokenizer_r("Small example to_encode"    ).is_fast    )

	@require_tokenizers
	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  BertTokenizerFast.from_pretrained("bert-base-cased"    )
							lowerCAmelCase__	 =  tokenizer_r(["Test", "\xad", "test"]						,      is_split_into_words=lowercase__    )

							self.assertEqual(encoded.word_to_tokens(0    )						,      TokenSpan(start=1						,      end=2    )    )
							self.assertEqual(encoded.word_to_tokens(1    )						,      lowercase__    )
							self.assertEqual(encoded.word_to_tokens(2    )						,      TokenSpan(start=2						,      end=3    )    )

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
							lowerCAmelCase__	 =  batch.convert_to_tensors(tensor_type="np"    )
							self.assertEqual(tensor_batch["inputs"].shape						,      (2, 3)    )
							self.assertEqual(tensor_batch["labels"].shape						,      (2,)    )
							# test converting the converted
							with CaptureStderr() as cs:
													lowerCAmelCase__	 =  batch.convert_to_tensors(tensor_type="np"    )
							self.assertFalse(len(cs.err    )						,      msg=F"should have no warning, but got {cs.err}"    )

							lowerCAmelCase__	 =  BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
							lowerCAmelCase__	 =  batch.convert_to_tensors(tensor_type="np"						,      prepend_batch_axis=lowercase__    )
							self.assertEqual(tensor_batch["inputs"].shape						,      (1, 3)    )
							self.assertEqual(tensor_batch["labels"].shape						,      (1,)    )

	@require_torch
	def 						UpperCAmelCase					(							self    )->   str:







							'''simple docstring'''





							lowerCAmelCase__	 =  BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
							lowerCAmelCase__	 =  batch.convert_to_tensors(tensor_type="pt"    )
							self.assertEqual(tensor_batch["inputs"].shape						,      (2, 3)    )
							self.assertEqual(tensor_batch["labels"].shape						,      (2,)    )
							# test converting the converted
							with CaptureStderr() as cs:
													lowerCAmelCase__	 =  batch.convert_to_tensors(tensor_type="pt"    )
							self.assertFalse(len(cs.err    )						,      msg=F"should have no warning, but got {cs.err}"    )

							lowerCAmelCase__	 =  BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
							lowerCAmelCase__	 =  batch.convert_to_tensors(tensor_type="pt"						,      prepend_batch_axis=lowercase__    )
							self.assertEqual(tensor_batch["inputs"].shape						,      (1, 3)    )
							self.assertEqual(tensor_batch["labels"].shape						,      (1,)    )

	@require_tf
	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
							lowerCAmelCase__	 =  batch.convert_to_tensors(tensor_type="tf"    )
							self.assertEqual(tensor_batch["inputs"].shape						,      (2, 3)    )
							self.assertEqual(tensor_batch["labels"].shape						,      (2,)    )
							# test converting the converted
							with CaptureStderr() as cs:
													lowerCAmelCase__	 =  batch.convert_to_tensors(tensor_type="tf"    )
							self.assertFalse(len(cs.err    )						,      msg=F"should have no warning, but got {cs.err}"    )

							lowerCAmelCase__	 =  BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
							lowerCAmelCase__	 =  batch.convert_to_tensors(tensor_type="tf"						,      prepend_batch_axis=lowercase__    )
							self.assertEqual(tensor_batch["inputs"].shape						,      (1, 3)    )
							self.assertEqual(tensor_batch["labels"].shape						,      (1,)    )

	@require_flax
	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
							lowerCAmelCase__	 =  batch.convert_to_tensors(tensor_type="jax"    )
							self.assertEqual(tensor_batch["inputs"].shape						,      (2, 3)    )
							self.assertEqual(tensor_batch["labels"].shape						,      (2,)    )
							# test converting the converted
							with CaptureStderr() as cs:
													lowerCAmelCase__	 =  batch.convert_to_tensors(tensor_type="jax"    )
							self.assertFalse(len(cs.err    )						,      msg=F"should have no warning, but got {cs.err}"    )

							lowerCAmelCase__	 =  BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
							lowerCAmelCase__	 =  batch.convert_to_tensors(tensor_type="jax"						,      prepend_batch_axis=lowercase__    )
							self.assertEqual(tensor_batch["inputs"].shape						,      (1, 3)    )
							self.assertEqual(tensor_batch["labels"].shape						,      (1,)    )

	def 						UpperCAmelCase					(							self    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  [{"input_ids": np.array([0, 1, 2]    )}, {"input_ids": np.array([0, 1, 2, 3]    )}]
							lowerCAmelCase__	 =  BertTokenizer.from_pretrained("bert-base-cased"    )

							lowerCAmelCase__	 =  tokenizer.pad(lowercase__						,      padding=lowercase__    )
							self.assertTrue(isinstance(batch["input_ids"]						,      np.ndarray    )    )
							self.assertEqual(batch["input_ids"].tolist()						,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
							lowerCAmelCase__	 =  tokenizer.pad(lowercase__						,      padding=lowercase__						,      return_tensors="np"    )
							self.assertTrue(isinstance(batch["input_ids"]						,      np.ndarray    )    )
							self.assertEqual(batch["input_ids"].tolist()						,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

	@require_torch
	def 						UpperCAmelCase					(							self    )->   List[Any]:







							'''simple docstring'''





							import torch

							lowerCAmelCase__	 =  [{"input_ids": torch.tensor([0, 1, 2]    )}, {"input_ids": torch.tensor([0, 1, 2, 3]    )}]
							lowerCAmelCase__	 =  BertTokenizer.from_pretrained("bert-base-cased"    )

							lowerCAmelCase__	 =  tokenizer.pad(lowercase__						,      padding=lowercase__    )
							self.assertTrue(isinstance(batch["input_ids"]						,      torch.Tensor    )    )
							self.assertEqual(batch["input_ids"].tolist()						,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
							lowerCAmelCase__	 =  tokenizer.pad(lowercase__						,      padding=lowercase__						,      return_tensors="pt"    )
							self.assertTrue(isinstance(batch["input_ids"]						,      torch.Tensor    )    )
							self.assertEqual(batch["input_ids"].tolist()						,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

	@require_tf
	def 						UpperCAmelCase					(							self    )->   Union[str, Any]:







							'''simple docstring'''





							import tensorflow as tf

							lowerCAmelCase__	 =  [{"input_ids": tf.constant([0, 1, 2]    )}, {"input_ids": tf.constant([0, 1, 2, 3]    )}]
							lowerCAmelCase__	 =  BertTokenizer.from_pretrained("bert-base-cased"    )

							lowerCAmelCase__	 =  tokenizer.pad(lowercase__						,      padding=lowercase__    )
							self.assertTrue(isinstance(batch["input_ids"]						,      tf.Tensor    )    )
							self.assertEqual(batch["input_ids"].numpy().tolist()						,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
							lowerCAmelCase__	 =  tokenizer.pad(lowercase__						,      padding=lowercase__						,      return_tensors="tf"    )
							self.assertTrue(isinstance(batch["input_ids"]						,      tf.Tensor    )    )
							self.assertEqual(batch["input_ids"].numpy().tolist()						,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

	@require_tokenizers
	def 						UpperCAmelCase					(							self    )->   Tuple:







							'''simple docstring'''





							lowerCAmelCase__	 =  Tokenizer(WordPiece(unk_token="[UNK]"    )    )
							PreTrainedTokenizerFast(tokenizer_object=lowercase__    )


	@require_tokenizers
	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  Tokenizer(WordPiece(unk_token="[UNK]"    )    )
							with tempfile.TemporaryDirectory() as tmpdirname:
													bert_tokenizer.save(os.path.join(lowercase__						,      "tokenizer.json"    )    )
													PreTrainedTokenizerFast(tokenizer_file=os.path.join(lowercase__						,      "tokenizer.json"    )    )














import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


a_							=      collections.namedtuple(
    '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
)


def     _a					(       UpperCamelCase_   :			str				)	->				CheckpointState:
						"""simple docstring"""







						print(F"Reading saved model from {model_file}"				)
						lowerCAmelCase__	 =  torch.load(UpperCamelCase_      ,				map_location=lambda UpperCamelCase_      ,				UpperCamelCase_				: default_restore_location(UpperCamelCase_      ,				"cpu"				)				)
						return CheckpointState(**UpperCamelCase_				)


class        lowercase__       :

	def __init__(							self						,      __UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  src_file

	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							raise NotImplementedError


	@staticmethod
	def 						UpperCAmelCase					(							__UpperCAmelCase						,      *__UpperCAmelCase						,      **__UpperCAmelCase    )->   "DPRState":







							'''simple docstring'''





							if comp_type.startswith("c"    ):
													return DPRContextEncoderState(*__UpperCAmelCase						,      **__UpperCAmelCase    )
							if comp_type.startswith("q"    ):
													return DPRQuestionEncoderState(*__UpperCAmelCase						,      **__UpperCAmelCase    )
							if comp_type.startswith("r"    ):
													return DPRReaderState(*__UpperCAmelCase						,      **__UpperCAmelCase    )
							else:
													raise ValueError("Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'."    )





class        lowercase__       (     _UpperCAmelCase		):

	def 						UpperCAmelCase					(							self    )->   str:







							'''simple docstring'''





							lowerCAmelCase__	 =  DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"    )[0]    )    )
							print(F"Loading DPR biencoder from {self.src_file}"    )
							lowerCAmelCase__	 =  load_states_from_checkpoint(self.src_file    )
							lowerCAmelCase__ ,							lowerCAmelCase__	 =  model.ctx_encoder, "ctx_model."
							# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
							lowerCAmelCase__	 =  {"bert_model.embeddings.position_ids": model.ctx_encoder.bert_model.embeddings.position_ids}
							for key, value in saved_state.model_dict.items():
													if key.startswith(__UpperCAmelCase    ):
																			lowerCAmelCase__	 =  key[len(__UpperCAmelCase    ) :]
																			if not key.startswith("encode_proj."    ):
																									lowerCAmelCase__	 =  "bert_model." + key
																			lowerCAmelCase__	 =  value
							encoder.load_state_dict(__UpperCAmelCase    )
							return model





class        lowercase__       (     _UpperCAmelCase		):

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"    )[0]    )    )
							print(F"Loading DPR biencoder from {self.src_file}"    )
							lowerCAmelCase__	 =  load_states_from_checkpoint(self.src_file    )
							lowerCAmelCase__ ,							lowerCAmelCase__	 =  model.question_encoder, "question_model."
							# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
							lowerCAmelCase__	 =  {"bert_model.embeddings.position_ids": model.question_encoder.bert_model.embeddings.position_ids}
							for key, value in saved_state.model_dict.items():
													if key.startswith(__UpperCAmelCase    ):
																			lowerCAmelCase__	 =  key[len(__UpperCAmelCase    ) :]
																			if not key.startswith("encode_proj."    ):
																									lowerCAmelCase__	 =  "bert_model." + key
																			lowerCAmelCase__	 =  value
							encoder.load_state_dict(__UpperCAmelCase    )
							return model





class        lowercase__       (     _UpperCAmelCase		):

	def 						UpperCAmelCase					(							self    )->   List[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"    )[0]    )    )
							print(F"Loading DPR reader from {self.src_file}"    )
							lowerCAmelCase__	 =  load_states_from_checkpoint(self.src_file    )
							# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
							lowerCAmelCase__	 =  {
							    "encoder.bert_model.embeddings.position_ids": model.span_predictor.encoder.bert_model.embeddings.position_ids
							}
							for key, value in saved_state.model_dict.items():
													if key.startswith("encoder."    ) and not key.startswith("encoder.encode_proj"    ):
																			lowerCAmelCase__	 =  "encoder.bert_model." + key[len("encoder."    ) :]
													lowerCAmelCase__	 =  value
							model.span_predictor.load_state_dict(__UpperCAmelCase    )
							return model





def     _a					(       UpperCamelCase_   :			str      ,				UpperCamelCase_   :			Path      ,				UpperCamelCase_   :			Path				)	->				Any:
						"""simple docstring"""







						lowerCAmelCase__	 =  Path(UpperCamelCase_				)
						dest_dir.mkdir(exist_ok=UpperCamelCase_				)

						lowerCAmelCase__	 =  DPRState.from_type(UpperCamelCase_      ,				src_file=UpperCamelCase_				)
						lowerCAmelCase__	 =  dpr_state.load_dpr_model()
						model.save_pretrained(UpperCamelCase_				)
						model.from_pretrained(UpperCamelCase_				)  # sanity check


if __name__ == "__main__":
	a_							=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--type''', type=str, help='''Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''
	)
	parser.add_argument(
	    '''--src''',
	    type=str,
	    help=(
	        '''Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'''
	        ''' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'''
	        ''' \'retriever\' checkpoints.'''
	    ),
	)
	parser.add_argument('''--dest''', type=str, default=None, help='''Path to the output PyTorch model directory.''')
	a_							=      parser.parse_args()

	a_							=      Path(args.src)
	a_							=      F"converted-{src_file.name}" if args.dest is None else args.dest
	a_							=      Path(dest_dir)
	assert src_file.exists()
	assert (
	    args.type is not None
	), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
	convert(args.type, src_file, dest_dir)






