

"""simple docstring"""


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class     snake_case	(				__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      int         =  RobertaTokenizer
				SCREAMING_SNAKE_CASE_   :      List[Any]         =  RobertaTokenizerFast
				SCREAMING_SNAKE_CASE_   :      List[Any]         =  True
				SCREAMING_SNAKE_CASE_   :      List[str]         =  {"""cls_token""": """<s>"""}

				def 							lowercase_     (	self							:				Tuple)->   int:



						'''simple docstring'''


						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						__lowerCAmelCase:       Dict          =			[
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						]
						__lowerCAmelCase:       List[str]          =			dict(zip(UpperCamelCase__     ,  range(len(UpperCamelCase__))))
						__lowerCAmelCase:       Any          =			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						__lowerCAmelCase:       Any          =			{"unk_token": "<unk>"}

						__lowerCAmelCase:       Any          =			os.path.join(self.tmpdirname     ,  VOCAB_FILES_NAMES["vocab_file"])
						__lowerCAmelCase:       List[str]          =			os.path.join(self.tmpdirname     ,  VOCAB_FILES_NAMES["merges_file"])
						with open(self.vocab_file     ,  "w"     ,  encoding="utf-8") as fp:
								fp.write(json.dumps(UpperCamelCase__) + "\n")
						with open(self.merges_file     ,  "w"     ,  encoding="utf-8") as fp:
								fp.write("\n".join(UpperCamelCase__))

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



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return self.tokenizer_class.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Union[str, Any]     ,  **UpperCamelCase__							:				str)->   List[Any]:



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return RobertaTokenizerFast.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				Tuple)->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			"lower newer"
						__lowerCAmelCase:       Optional[Any]          =			"lower newer"
						return input_text, output_text

				def 							lowercase_     (	self							:				Any)->   int:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			self.tokenizer_class(self.vocab_file     ,  self.merges_file     ,  **self.special_tokens_map)
						__lowerCAmelCase:       Optional[Any]          =			"lower newer"
						__lowerCAmelCase:       List[Any]          =			["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
						__lowerCAmelCase:       Dict          =			tokenizer.tokenize(UpperCamelCase__)  # , add_prefix_space=True)
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

						__lowerCAmelCase:       Any          =			tokens + [tokenizer.unk_token]
						__lowerCAmelCase:       Tuple          =			[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__)     ,  UpperCamelCase__)

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



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			self.get_tokenizer()

						self.assertListEqual(tokenizer.encode("Hello world!"     ,  add_special_tokens=UpperCamelCase__)     ,  [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2])
						self.assertListEqual(
						    tokenizer.encode("Hello world! cécé herlolip 418"     ,  add_special_tokens=UpperCamelCase__)     ,  [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]     ,  )

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



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			self.tokenizer_class.from_pretrained("roberta-base")

						__lowerCAmelCase:       int          =			tokenizer.encode("sequence builders"     ,  add_special_tokens=UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			tokenizer.encode("multi-sequence build"     ,  add_special_tokens=UpperCamelCase__)

						__lowerCAmelCase:       Optional[int]          =			tokenizer.encode(
						    "sequence builders"     ,  add_special_tokens=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			tokenizer.encode(
						    "sequence builders"     ,  "multi-sequence build"     ,  add_special_tokens=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__)

						__lowerCAmelCase:       str          =			tokenizer.build_inputs_with_special_tokens(UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			tokenizer.build_inputs_with_special_tokens(UpperCamelCase__     ,  UpperCamelCase__)

						assert encoded_sentence == encoded_text_from_decode
						assert encoded_pair == encoded_pair_from_decode

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



						'''simple docstring'''


						__lowerCAmelCase:       int          =			self.get_tokenizer()

						__lowerCAmelCase:       Optional[int]          =			"Encode this sequence."
						__lowerCAmelCase:       List[str]          =			tokenizer.byte_encoder[" ".encode("utf-8")[0]]

						# Testing encoder arguments
						__lowerCAmelCase:       Dict          =			tokenizer.encode(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			tokenizer.convert_ids_to_tokens(encoded[0])[0]
						self.assertNotEqual(UpperCamelCase__     ,  UpperCamelCase__)

						__lowerCAmelCase:       Any          =			tokenizer.encode(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__)
						__lowerCAmelCase:       List[str]          =			tokenizer.convert_ids_to_tokens(encoded[0])[0]
						self.assertEqual(UpperCamelCase__     ,  UpperCamelCase__)

						tokenizer.add_special_tokens({"bos_token": "<s>"})
						__lowerCAmelCase:       Any          =			tokenizer.encode(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			tokenizer.convert_ids_to_tokens(encoded[1])[0]
						self.assertNotEqual(UpperCamelCase__     ,  UpperCamelCase__)

						# Testing spaces after special tokens
						__lowerCAmelCase:       Union[str, Any]          =			"<mask>"
						tokenizer.add_special_tokens(
						    {"mask_token": AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__)})  # mask token has a left space
						__lowerCAmelCase:       str          =			tokenizer.convert_tokens_to_ids(UpperCamelCase__)

						__lowerCAmelCase:       Optional[int]          =			"Encode <mask> sequence"
						__lowerCAmelCase:       str          =			"Encode <mask>sequence"

						__lowerCAmelCase:       str          =			tokenizer.encode(UpperCamelCase__)
						__lowerCAmelCase:       Optional[Any]          =			encoded.index(UpperCamelCase__)
						__lowerCAmelCase:       Optional[Any]          =			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
						self.assertEqual(UpperCamelCase__     ,  UpperCamelCase__)

						__lowerCAmelCase:       Dict          =			tokenizer.encode(UpperCamelCase__)
						__lowerCAmelCase:       List[str]          =			encoded.index(UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
						self.assertNotEqual(UpperCamelCase__     ,  UpperCamelCase__)

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



						'''simple docstring'''


						pass

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



						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
										__lowerCAmelCase:       Union[str, Any]          =			self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,  **UpperCamelCase__)
										__lowerCAmelCase:       Any          =			self.tokenizer_class.from_pretrained(UpperCamelCase__     ,  **UpperCamelCase__)
										__lowerCAmelCase:       Any          =			"A, <mask> AllenNLP sentence."
										__lowerCAmelCase:       Dict          =			tokenizer_r.encode_plus(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__     ,  return_token_type_ids=UpperCamelCase__)
										__lowerCAmelCase:       Optional[int]          =			tokenizer_p.encode_plus(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__     ,  return_token_type_ids=UpperCamelCase__)

										# token_type_ids should put 0 everywhere
										self.assertEqual(sum(tokens_r["token_type_ids"])     ,  sum(tokens_p["token_type_ids"]))

										# attention_mask should put 1 everywhere, so sum over length should be 1
										self.assertEqual(
										    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"])     ,  sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"])     ,  )

										__lowerCAmelCase:       List[Any]          =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
										__lowerCAmelCase:       Dict          =			tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

										# Rust correctly handles the space before the mask while python doesnt
										self.assertSequenceEqual(tokens_p["input_ids"]     ,  [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])
										self.assertSequenceEqual(tokens_r["input_ids"]     ,  [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])

										self.assertSequenceEqual(
										    UpperCamelCase__     ,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
										self.assertSequenceEqual(
										    UpperCamelCase__     ,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])

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



						'''simple docstring'''


						for trim_offsets, add_prefix_space in itertools.product([True, False]     ,  repeat=2):
								__lowerCAmelCase:       Any          =			self.rust_tokenizer_class.from_pretrained(
								    self.tmpdirname     ,  use_fast=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__     ,  trim_offsets=UpperCamelCase__)

								__lowerCAmelCase:       List[Any]          =			json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
								__lowerCAmelCase:       Union[str, Any]          =			json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())

								self.assertEqual(pre_tokenizer_state["add_prefix_space"]     ,  UpperCamelCase__)

								self.assertEqual(post_processor_state["add_prefix_space"]     ,  UpperCamelCase__)
								self.assertEqual(post_processor_state["trim_offsets"]     ,  UpperCamelCase__)




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



						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
										__lowerCAmelCase:       Any          =			"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
										__lowerCAmelCase:       str          =			f"{text_of_1_token} {text_of_1_token}"

										__lowerCAmelCase:       Union[str, Any]          =			self.rust_tokenizer_class.from_pretrained(
										    UpperCamelCase__     ,  use_fast=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__     ,  trim_offsets=UpperCamelCase__)
										__lowerCAmelCase:       List[Any]          =			tokenizer_r(UpperCamelCase__     ,  return_offsets_mapping=UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(encoding.offset_mapping[0]     ,  (0, len(UpperCamelCase__)))
										self.assertEqual(
										    encoding.offset_mapping[1]     ,  (len(UpperCamelCase__) + 1, len(UpperCamelCase__) + 1 + len(UpperCamelCase__))     ,  )

										__lowerCAmelCase:       Optional[int]          =			self.rust_tokenizer_class.from_pretrained(
										    UpperCamelCase__     ,  use_fast=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__     ,  trim_offsets=UpperCamelCase__)
										__lowerCAmelCase:       Union[str, Any]          =			tokenizer_r(UpperCamelCase__     ,  return_offsets_mapping=UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(encoding.offset_mapping[0]     ,  (0, len(UpperCamelCase__)))
										self.assertEqual(
										    encoding.offset_mapping[1]     ,  (len(UpperCamelCase__) + 1, len(UpperCamelCase__) + 1 + len(UpperCamelCase__))     ,  )

										__lowerCAmelCase:       List[str]          =			self.rust_tokenizer_class.from_pretrained(
										    UpperCamelCase__     ,  use_fast=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__     ,  trim_offsets=UpperCamelCase__)
										__lowerCAmelCase:       Any          =			tokenizer_r(UpperCamelCase__     ,  return_offsets_mapping=UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(encoding.offset_mapping[0]     ,  (0, len(UpperCamelCase__)))
										self.assertEqual(
										    encoding.offset_mapping[1]     ,  (len(UpperCamelCase__), len(UpperCamelCase__) + 1 + len(UpperCamelCase__))     ,  )

										__lowerCAmelCase:       int          =			self.rust_tokenizer_class.from_pretrained(
										    UpperCamelCase__     ,  use_fast=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__     ,  trim_offsets=UpperCamelCase__)
										__lowerCAmelCase:       Optional[Any]          =			tokenizer_r(UpperCamelCase__     ,  return_offsets_mapping=UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(encoding.offset_mapping[0]     ,  (0, len(UpperCamelCase__)))
										self.assertEqual(
										    encoding.offset_mapping[1]     ,  (len(UpperCamelCase__), len(UpperCamelCase__) + 1 + len(UpperCamelCase__))     ,  )

										__lowerCAmelCase:       str          =			f" {text}"

										# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
										#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
										# )
										# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
										# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
										# self.assertEqual(
										#     encoding.offset_mapping[1],
										#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
										# )

										__lowerCAmelCase:       Tuple          =			self.rust_tokenizer_class.from_pretrained(
										    UpperCamelCase__     ,  use_fast=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__     ,  trim_offsets=UpperCamelCase__)
										__lowerCAmelCase:       str          =			tokenizer_r(UpperCamelCase__     ,  return_offsets_mapping=UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(encoding.offset_mapping[0]     ,  (1, 1 + len(UpperCamelCase__)))
										self.assertEqual(
										    encoding.offset_mapping[1]     ,  (1 + len(UpperCamelCase__) + 1, 1 + len(UpperCamelCase__) + 1 + len(UpperCamelCase__))     ,  )

										__lowerCAmelCase:       Union[str, Any]          =			self.rust_tokenizer_class.from_pretrained(
										    UpperCamelCase__     ,  use_fast=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__     ,  trim_offsets=UpperCamelCase__)
										__lowerCAmelCase:       str          =			tokenizer_r(UpperCamelCase__     ,  return_offsets_mapping=UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(encoding.offset_mapping[0]     ,  (0, 1 + len(UpperCamelCase__)))
										self.assertEqual(
										    encoding.offset_mapping[1]     ,  (1 + len(UpperCamelCase__), 1 + len(UpperCamelCase__) + 1 + len(UpperCamelCase__))     ,  )

										__lowerCAmelCase:       List[str]          =			self.rust_tokenizer_class.from_pretrained(
										    UpperCamelCase__     ,  use_fast=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__     ,  trim_offsets=UpperCamelCase__)
										__lowerCAmelCase:       List[Any]          =			tokenizer_r(UpperCamelCase__     ,  return_offsets_mapping=UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(encoding.offset_mapping[0]     ,  (0, 1 + len(UpperCamelCase__)))
										self.assertEqual(
										    encoding.offset_mapping[1]     ,  (1 + len(UpperCamelCase__), 1 + len(UpperCamelCase__) + 1 + len(UpperCamelCase__))     ,  )





"""simple docstring"""


import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Optional[int]:
		# Construct model
		if gpta_config_file == "":
				__lowerCAmelCase:       Optional[int]          =			GPTaConfig()
		else:
				__lowerCAmelCase:       List[str]          =			GPTaConfig.from_json_file(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Optional[int]          =			GPTaModel(__SCREAMING_SNAKE_CASE )

		# Load weights from numpy
		load_tf_weights_in_gpta(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )

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


if __name__ == "__main__":
			__A						   =			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			parser.add_argument(
			    "--gpt2_config_file",
			    default="",
			    type=str,
			    help=(
			        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
			        "This specifies the model architecture."
			    ),
			)
			__A						   =			parser.parse_args()
			convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


