

"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin





class     snake_case	(				__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      Tuple         =  CanineTokenizer
				SCREAMING_SNAKE_CASE_   :      Tuple         =  False

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



						'''simple docstring'''


						super().setUp()
						__lowerCAmelCase:       Tuple          =			CanineTokenizer()
						tokenizer.save_pretrained(self.tmpdirname)

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



						'''simple docstring'''


						return CanineTokenizer.from_pretrained("google/canine-s")

				def 							lowercase_     (	self							:				List[Any]     ,  **UpperCamelCase__							:				Tuple)->   CanineTokenizer:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			self.tokenizer_class.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__)
						__lowerCAmelCase:       Any          =			1_0_2_4
						return tokenizer

				@require_torch
				def 							lowercase_     (	self							:				str)->   str:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.canine_tokenizer
						__lowerCAmelCase:       str          =			["Life is like a box of chocolates.", "You never know what you're gonna get."]
						# fmt: off
						__lowerCAmelCase:       Dict          =			[5_7_3_4_4, 7_6, 1_0_5, 1_0_2, 1_0_1, 3_2, 1_0_5, 1_1_5, 3_2, 1_0_8, 1_0_5, 1_0_7, 1_0_1, 3_2, 9_7, 3_2, 9_8, 1_1_1, 1_2_0, 3_2, 1_1_1, 1_0_2, 3_2, 9_9, 1_0_4, 1_1_1, 9_9, 1_1_1, 1_0_8, 9_7, 1_1_6, 1_0_1, 1_1_5, 4_6, 5_7_3_4_5, 0, 0, 0, 0]
						# fmt: on
						__lowerCAmelCase:       str          =			tokenizer(UpperCamelCase__     ,  padding=UpperCamelCase__     ,  return_tensors="pt")
						self.assertIsInstance(UpperCamelCase__     ,  UpperCamelCase__)

						__lowerCAmelCase:       int          =			list(batch.input_ids.numpy()[0])

						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

						self.assertEqual((2, 3_9)     ,  batch.input_ids.shape)
						self.assertEqual((2, 3_9)     ,  batch.attention_mask.shape)

				@require_torch
				def 							lowercase_     (	self							:				int)->   str:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			self.canine_tokenizer
						__lowerCAmelCase:       List[Any]          =			["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
						__lowerCAmelCase:       List[Any]          =			tokenizer(UpperCamelCase__     ,  padding=UpperCamelCase__     ,  return_tensors="pt")
						# check if input_ids, attention_mask and token_type_ids are returned
						self.assertIn("input_ids"     ,  UpperCamelCase__)
						self.assertIn("attention_mask"     ,  UpperCamelCase__)
						self.assertIn("token_type_ids"     ,  UpperCamelCase__)

				@require_torch
				def 							lowercase_     (	self							:				str)->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			self.canine_tokenizer
						__lowerCAmelCase:       Optional[int]          =			[
						    "What's the weater?",
						    "It's about 25 degrees.",
						]
						__lowerCAmelCase:       Optional[int]          =			tokenizer(
						    text_target=UpperCamelCase__     ,  max_length=3_2     ,  padding="max_length"     ,  truncation=UpperCamelCase__     ,  return_tensors="pt")
						self.assertEqual(3_2     ,  targets["input_ids"].shape[1])

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



						'''simple docstring'''


						__lowerCAmelCase:       int          =			self.get_tokenizers()
						for tokenizer in tokenizers:
								with self.subTest(f"{tokenizer.__class__.__name__}"):
										self.assertNotEqual(tokenizer.model_max_length     ,  4_2)

        # Now let's start the test
						__lowerCAmelCase:       Optional[Any]          =			self.get_tokenizers()
						for tokenizer in tokenizers:
								with self.subTest(f"{tokenizer.__class__.__name__}"):
										# Isolate this from the other tests because we save additional tokens/etc
										__lowerCAmelCase:       Tuple          =			tempfile.mkdtemp()

										__lowerCAmelCase:       List[str]          =			" He is very happy, UNwant\u00E9d,running"
										__lowerCAmelCase:       List[str]          =			tokenizer.encode(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										tokenizer.save_pretrained(UpperCamelCase__)

										__lowerCAmelCase:       List[str]          =			tokenizer.__class__.from_pretrained(UpperCamelCase__)
										__lowerCAmelCase:       Any          =			after_tokenizer.encode(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

										shutil.rmtree(UpperCamelCase__)

						__lowerCAmelCase:       List[Any]          =			self.get_tokenizers(model_max_length=4_2)
						for tokenizer in tokenizers:
								with self.subTest(f"{tokenizer.__class__.__name__}"):
										# Isolate this from the other tests because we save additional tokens/etc
										__lowerCAmelCase:       Any          =			tempfile.mkdtemp()

										__lowerCAmelCase:       Optional[int]          =			" He is very happy, UNwant\u00E9d,running"

										__lowerCAmelCase:       Union[str, Any]          =			tokenizer.additional_special_tokens

										# We can add a new special token for Canine as follows:
										__lowerCAmelCase:       Dict          =			chr(0xE007)
										additional_special_tokens.append(UpperCamelCase__)
										tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
										__lowerCAmelCase:       Tuple          =			tokenizer.encode(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										tokenizer.save_pretrained(UpperCamelCase__)

										__lowerCAmelCase:       Optional[Any]          =			tokenizer.__class__.from_pretrained(UpperCamelCase__)
										__lowerCAmelCase:       List[Any]          =			after_tokenizer.encode(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)
										self.assertIn(UpperCamelCase__     ,  after_tokenizer.additional_special_tokens)
										self.assertEqual(after_tokenizer.model_max_length     ,  4_2)

										__lowerCAmelCase:       Optional[int]          =			tokenizer.__class__.from_pretrained(UpperCamelCase__     ,  model_max_length=4_3)
										self.assertEqual(tokenizer.model_max_length     ,  4_3)

										shutil.rmtree(UpperCamelCase__)

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



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			self.get_tokenizers(do_lower_case=UpperCamelCase__)
						for tokenizer in tokenizers:
								with self.subTest(f"{tokenizer.__class__.__name__}"):
										__lowerCAmelCase      , __lowerCAmelCase:       Optional[int]          =			self.get_clean_sequence(UpperCamelCase__)

										# a special token for Canine can be defined as follows:
										__lowerCAmelCase:       List[str]          =			0xE005
										__lowerCAmelCase:       int          =			chr(UpperCamelCase__)

										tokenizer.add_special_tokens({"cls_token": special_token})
										__lowerCAmelCase:       Tuple          =			tokenizer.encode(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(len(UpperCamelCase__)     ,  1)

										__lowerCAmelCase:       Union[str, Any]          =			tokenizer.decode(ids + encoded_special_token     ,  clean_up_tokenization_spaces=UpperCamelCase__)
										__lowerCAmelCase:       int          =			tokenizer.encode(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)

										__lowerCAmelCase:       int          =			tokenizer.encode(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										__lowerCAmelCase:       str          =			tokenizer.encode(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(UpperCamelCase__     ,  input_encoded + special_token_id)

										__lowerCAmelCase:       Optional[int]          =			tokenizer.decode(UpperCamelCase__     ,  skip_special_tokens=UpperCamelCase__)
										self.assertTrue(special_token not in decoded)

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



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.get_tokenizers(do_lower_case=UpperCamelCase__)
						for tokenizer in tokenizers:
								with self.subTest(f"{tokenizer.__class__.__name__}"):
										__lowerCAmelCase:       Dict          =			chr(0xE005)
										__lowerCAmelCase:       Optional[Any]          =			chr(0xE006)

										# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
										tokenizer.add_tokens([SPECIAL_TOKEN_1]     ,  special_tokens=UpperCamelCase__)
										# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
										# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
										tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]})

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

										self.assertEqual(len(UpperCamelCase__)     ,  1)
										self.assertEqual(len(UpperCamelCase__)     ,  1)
										self.assertEqual(token_a[0]     ,  UpperCamelCase__)
										self.assertEqual(token_a[0]     ,  UpperCamelCase__)

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



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			self.get_tokenizers(do_lower_case=UpperCamelCase__)
						for tokenizer in tokenizers:
								with self.subTest(f"{tokenizer.__class__.__name__}"):
										# a special token for Canine can be defined as follows:
										__lowerCAmelCase:       int          =			0xE006
										__lowerCAmelCase:       Tuple          =			chr(UpperCamelCase__)

										__lowerCAmelCase:       List[Any]          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__)
										tokenizer.add_special_tokens({"additional_special_tokens": [new_token]})

										with tempfile.TemporaryDirectory() as tmp_dir_name:
												tokenizer.save_pretrained(UpperCamelCase__)
												tokenizer.from_pretrained(UpperCamelCase__)

				def 							lowercase_     (	self							:				int)->   str:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			[]
						if self.test_slow_tokenizer:
								tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

						if self.test_rust_tokenizer:
								tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

						for tokenizer_class, tokenizer_utils in tokenizer_list:
								with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer_utils.save_pretrained(UpperCamelCase__)

										with open(os.path.join(UpperCamelCase__     ,  "special_tokens_map.json")     ,  encoding="utf-8") as json_file:
												__lowerCAmelCase:       List[Any]          =			json.load(UpperCamelCase__)

										with open(os.path.join(UpperCamelCase__     ,  "tokenizer_config.json")     ,  encoding="utf-8") as json_file:
												__lowerCAmelCase:       Dict          =			json.load(UpperCamelCase__)

										# a special token for Canine can be defined as follows:
										__lowerCAmelCase:       Dict          =			0xE006
										__lowerCAmelCase:       int          =			chr(UpperCamelCase__)

										__lowerCAmelCase:       Union[str, Any]          =			[new_token_a]
										__lowerCAmelCase:       str          =			[new_token_a]

										with open(os.path.join(UpperCamelCase__     ,  "special_tokens_map.json")     ,  "w"     ,  encoding="utf-8") as outfile:
												json.dump(UpperCamelCase__     ,  UpperCamelCase__)
										with open(os.path.join(UpperCamelCase__     ,  "tokenizer_config.json")     ,  "w"     ,  encoding="utf-8") as outfile:
												json.dump(UpperCamelCase__     ,  UpperCamelCase__)

										# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
										# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
										# "special_tokens_map.json" files
										__lowerCAmelCase:       str          =			tokenizer_class.from_pretrained(UpperCamelCase__     ,  extra_ids=0)
										self.assertIn(UpperCamelCase__     ,  tokenizer_without_change_in_init.additional_special_tokens)
										# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
										self.assertEqual(
										    [new_token_a]     ,  tokenizer_without_change_in_init.convert_ids_to_tokens(
										        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]))     ,  )

										__lowerCAmelCase:       Optional[int]          =			0xE007
										__lowerCAmelCase:       int          =			chr(UpperCamelCase__)
										# Now we test that we can change the value of additional_special_tokens in the from_pretrained
										__lowerCAmelCase:       Dict          =			[AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__)]
										__lowerCAmelCase:       Tuple          =			tokenizer_class.from_pretrained(
										    UpperCamelCase__     ,  additional_special_tokens=UpperCamelCase__     ,  extra_ids=0)

										self.assertIn(UpperCamelCase__     ,  tokenizer.additional_special_tokens)
										# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
										self.assertEqual(
										    [new_token_a]     ,  tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a])))

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



						'''simple docstring'''


						__lowerCAmelCase:       int          =			self.get_tokenizers(do_lower_case=UpperCamelCase__)
						for tokenizer in tokenizers:
								with self.subTest(f"{tokenizer.__class__.__name__}"):
										__lowerCAmelCase:       Optional[Any]          =			"hello world"
										if self.space_between_special_tokens:
												__lowerCAmelCase:       int          =			"[CLS] hello world [SEP]"
										else:
												__lowerCAmelCase:       Any          =			input
										__lowerCAmelCase:       int          =			tokenizer.encode(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__)
										__lowerCAmelCase:       Dict          =			tokenizer.decode(UpperCamelCase__     ,  spaces_between_special_tokens=self.space_between_special_tokens)
										self.assertIn(UpperCamelCase__     ,  [output, output.lower()])

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



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			self.get_tokenizers()
						for tokenizer in tokenizers:
								with self.subTest(f"{tokenizer.__class__.__name__}"):
										__lowerCAmelCase:       Optional[int]          =			[
										    "bos_token",
										    "eos_token",
										    "unk_token",
										    "sep_token",
										    "pad_token",
										    "cls_token",
										    "mask_token",
										]

						__lowerCAmelCase:       List[str]          =			"a"
						__lowerCAmelCase:       Any          =			ord(UpperCamelCase__)

						for attr in attributes_list:
								setattr(UpperCamelCase__     ,  attr + "_id"     ,  UpperCamelCase__)
								self.assertEqual(getattr(UpperCamelCase__     ,  UpperCamelCase__)     ,  UpperCamelCase__)
								self.assertEqual(getattr(UpperCamelCase__     ,  attr + "_id")     ,  UpperCamelCase__)

								setattr(UpperCamelCase__     ,  attr + "_id"     ,  UpperCamelCase__)
								self.assertEqual(getattr(UpperCamelCase__     ,  UpperCamelCase__)     ,  UpperCamelCase__)
								self.assertEqual(getattr(UpperCamelCase__     ,  attr + "_id")     ,  UpperCamelCase__)

						setattr(UpperCamelCase__     ,  "additional_special_tokens_ids"     ,  [])
						self.assertListEqual(getattr(UpperCamelCase__     ,  "additional_special_tokens")     ,  [])
						self.assertListEqual(getattr(UpperCamelCase__     ,  "additional_special_tokens_ids")     ,  [])

						__lowerCAmelCase:       Optional[int]          =			0xE006
						__lowerCAmelCase:       Dict          =			chr(UpperCamelCase__)
						setattr(UpperCamelCase__     ,  "additional_special_tokens_ids"     ,  [additional_special_token_id])
						self.assertListEqual(getattr(UpperCamelCase__     ,  "additional_special_tokens")     ,  [additional_special_token])
						self.assertListEqual(getattr(UpperCamelCase__     ,  "additional_special_tokens_ids")     ,  [additional_special_token_id])

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



						'''simple docstring'''


						pass

				def 							lowercase_     (	self							:				int)->   Tuple:



						'''simple docstring'''


						pass

				def 							lowercase_     (	self							:				Any)->   Dict:



						'''simple docstring'''


						pass

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



						'''simple docstring'''


						pass

				def 							lowercase_     (	self							:				Dict)->   int:



						'''simple docstring'''


						pass

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



						'''simple docstring'''


						pass

				def 							lowercase_     (	self							:				int)->   Any:



						'''simple docstring'''


						pass




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



						'''simple docstring'''


						pass





"""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)


