

'''simple docstring'''





import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
	from test_module.custom_tokenization_fast import CustomTokenizerFast

class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			mock.Mock()
							A__        =			500
							A__        =			{}
							A__        =			HTTPError
							A__        =			{}

							# Download this model to make sure it's in the cache.
							A__        =			BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"       )

							# Under the mock environment we get a 500 error when trying to reach the tokenizer.
							with mock.patch("requests.Session.request"      ,      return_value=lowercase       ) as mock_head:
											A__        =			BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"       )
											# This check we did call the fake head request
											mock_head.assert_called()


			@require_tokenizers
			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__        =			mock.Mock()
							A__        =			500
							A__        =			{}
							A__        =			HTTPError
							A__        =			{}

							# Download this model to make sure it's in the cache.
							A__        =			GPTaTokenizerFast.from_pretrained("gpt2"       )

							# Under the mock environment we get a 500 error when trying to reach the tokenizer.
							with mock.patch("requests.Session.request"      ,      return_value=lowercase       ) as mock_head:
											A__        =			GPTaTokenizerFast.from_pretrained("gpt2"       )
											# This check we did call the fake head request
											mock_head.assert_called()


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							try:
											A__        =			tempfile.mktemp()
											with open(lowercase      ,      "wb"       ) as f:
															http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"      ,      lowercase       )

											A__        =			AlbertTokenizer.from_pretrained(lowercase       )
							finally:
											os.remove(lowercase       )

							# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
							# the current folder and have the right name.
							if os.path.isfile("tokenizer.json"       ):
											# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
											return
							try:
											with open("tokenizer.json"      ,      "wb"       ) as f:
															http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"      ,      lowercase       )
											A__        =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"       )
											# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
											self.assertEqual(tokenizer.vocab_size      ,      1000       )
											# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

							finally:
											os.remove("tokenizer.json"       )


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"       )

@is_staging_test
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']


			@classmethod
			def      UpperCamelCase		(    cls       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			TOKEN
							HfFolder.save_token(lowercase       )


			@classmethod
			def      UpperCamelCase		(    cls       )  ->						Union[str, Any]:
							'''simple docstring'''






							try:
											delete_repo(token=cls._token      ,      repo_id="test-tokenizer"       )
							except HTTPError:
											pass

							try:
											delete_repo(token=cls._token      ,      repo_id="valid_org/test-tokenizer-org"       )
							except HTTPError:
											pass

							try:
											delete_repo(token=cls._token      ,      repo_id="test-dynamic-tokenizer"       )
							except HTTPError:
											pass


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							with tempfile.TemporaryDirectory() as tmp_dir:
											A__        =			os.path.join(lowercase      ,      "vocab.txt"       )
											with open(lowercase      ,      "w"      ,      encoding="utf-8"       ) as vocab_writer:
															vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]       )       )
											A__        =			BertTokenizer(lowercase       )

							tokenizer.push_to_hub("test-tokenizer"      ,      use_auth_token=self._token       )
							A__        =			BertTokenizer.from_pretrained(F'{USER}/test-tokenizer'       )
							self.assertDictEqual(new_tokenizer.vocab      ,      tokenizer.vocab       )

							# Reset repo
							delete_repo(token=self._token      ,      repo_id="test-tokenizer"       )

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
											tokenizer.save_pretrained(lowercase      ,      repo_id="test-tokenizer"      ,      push_to_hub=lowercase      ,      use_auth_token=self._token       )

							A__        =			BertTokenizer.from_pretrained(F'{USER}/test-tokenizer'       )
							self.assertDictEqual(new_tokenizer.vocab      ,      tokenizer.vocab       )


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							with tempfile.TemporaryDirectory() as tmp_dir:
											A__        =			os.path.join(lowercase      ,      "vocab.txt"       )
											with open(lowercase      ,      "w"      ,      encoding="utf-8"       ) as vocab_writer:
															vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]       )       )
											A__        =			BertTokenizer(lowercase       )

							tokenizer.push_to_hub("valid_org/test-tokenizer-org"      ,      use_auth_token=self._token       )
							A__        =			BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"       )
							self.assertDictEqual(new_tokenizer.vocab      ,      tokenizer.vocab       )

							# Reset repo
							delete_repo(token=self._token      ,      repo_id="valid_org/test-tokenizer-org"       )

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
											tokenizer.save_pretrained(
											    lowercase      ,      repo_id="valid_org/test-tokenizer-org"      ,      push_to_hub=lowercase      ,      use_auth_token=self._token       )

							A__        =			BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"       )
							self.assertDictEqual(new_tokenizer.vocab      ,      tokenizer.vocab       )


			@require_tokenizers
			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							CustomTokenizer.register_for_auto_class()
							with tempfile.TemporaryDirectory() as tmp_dir:
											A__        =			os.path.join(lowercase      ,      "vocab.txt"       )
											with open(lowercase      ,      "w"      ,      encoding="utf-8"       ) as vocab_writer:
															vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]       )       )
											A__        =			CustomTokenizer(lowercase       )

							# No fast custom tokenizer
							tokenizer.push_to_hub("test-dynamic-tokenizer"      ,      use_auth_token=self._token       )

							A__        =			AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer'      ,      trust_remote_code=lowercase       )
							# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
							self.assertEqual(tokenizer.__class__.__name__      ,      "CustomTokenizer"       )

							# Fast and slow custom tokenizer
							CustomTokenizerFast.register_for_auto_class()
							with tempfile.TemporaryDirectory() as tmp_dir:
											A__        =			os.path.join(lowercase      ,      "vocab.txt"       )
											with open(lowercase      ,      "w"      ,      encoding="utf-8"       ) as vocab_writer:
															vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]       )       )

											A__        =			BertTokenizerFast.from_pretrained(lowercase       )
											bert_tokenizer.save_pretrained(lowercase       )
											A__        =			CustomTokenizerFast.from_pretrained(lowercase       )

							tokenizer.push_to_hub("test-dynamic-tokenizer"      ,      use_auth_token=self._token       )

							A__        =			AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer'      ,      trust_remote_code=lowercase       )
							# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
							self.assertEqual(tokenizer.__class__.__name__      ,      "CustomTokenizerFast"       )
							A__        =			AutoTokenizer.from_pretrained(
							    F'{USER}/test-dynamic-tokenizer'      ,      use_fast=lowercase      ,      trust_remote_code=lowercase       )
							# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
							self.assertEqual(tokenizer.__class__.__name__      ,      "CustomTokenizer"       )

class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			Trie()
							trie.add("Hello 友達"       )
							self.assertEqual(trie.data      ,      {"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}}       )
							trie.add("Hello"       )
							trie.data
							self.assertEqual(trie.data      ,      {"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}}       )


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			Trie()
							self.assertEqual(trie.split("[CLS] This is a extra_id_100"       )      ,      ["[CLS] This is a extra_id_100"]       )
							trie.add("[CLS]"       )
							trie.add("extra_id_1"       )
							trie.add("extra_id_100"       )
							self.assertEqual(trie.split("[CLS] This is a extra_id_100"       )      ,      ["[CLS]", " This is a ", "extra_id_100"]       )


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							A__        =			Trie()
							trie.add("A"       )
							self.assertEqual(trie.split("ABC"       )      ,      ["A", "BC"]       )
							self.assertEqual(trie.split("BCA"       )      ,      ["BC", "A"]       )


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__        =			Trie()
							trie.add("TOKEN]"       )
							trie.add("[SPECIAL_TOKEN]"       )
							self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"       )      ,      ["This is something ", "[SPECIAL_TOKEN]"]       )


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			Trie()
							trie.add("A"       )
							trie.add("P"       )
							trie.add("[SPECIAL_TOKEN]"       )
							self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"       )      ,      ["This is something ", "[SPECIAL_TOKEN]"]       )


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			Trie()
							trie.add("AB"       )
							trie.add("B"       )
							trie.add("C"       )
							self.assertEqual(trie.split("ABC"       )      ,      ["AB", "C"]       )


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			Trie()
							trie.add("ABC"       )
							trie.add("B"       )
							trie.add("CD"       )
							self.assertEqual(trie.split("ABCD"       )      ,      ["ABC", "D"]       )


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			Trie()
							A__        =			trie.cut_text("ABC"      ,      [0, 0, 2, 1, 2, 3]       )
							self.assertEqual(lowercase      ,      ["AB", "C"]       )




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Namespace      )			->      Tuple:




				'''simple docstring'''





				return ConvertCommand(
				    args.model_type	,       args.tf_checkpoint	,       args.pytorch_dump_output	,       args.config	,       args.finetuning_task_name      )


lowerCAmelCase__	   = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""





class 					a__						(					snake_case			):
			"""simple docstring"""







			@staticmethod
			def      UpperCamelCase		(    lowercase       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			parser.add_parser(
							    "convert"      ,      help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."      ,      )
							train_parser.add_argument("--model_type"      ,      type=lowercase      ,      required=lowercase      ,      help="Model's type."       )
							train_parser.add_argument(
							    "--tf_checkpoint"      ,      type=lowercase      ,      required=lowercase      ,      help="TensorFlow checkpoint path or folder."       )
							train_parser.add_argument(
							    "--pytorch_dump_output"      ,      type=lowercase      ,      required=lowercase      ,      help="Path to the PyTorch saved model output."       )
							train_parser.add_argument("--config"      ,      type=lowercase      ,      default=""      ,      help="Configuration file path or folder."       )
							train_parser.add_argument(
							    "--finetuning_task_name"      ,      type=lowercase      ,      default=lowercase      ,      help="Optional fine-tuning task name if the TF model was a finetuned model."      ,      )
							train_parser.set_defaults(func=lowercase       )


			def __init__(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      *lowercase      ,      )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			logging.get_logger("transformers-cli/converting"       )

							self._logger.info(F'Loading model {model_type}'       )
							A__        =			model_type
							A__        =			tf_checkpoint
							A__        =			pytorch_dump_output
							A__        =			config
							A__        =			finetuning_task_name


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							if self._model_type == "albert":
											try:
															from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "bert":
											try:
															from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "funnel":
											try:
															from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "t5":
											try:
															from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
											except ImportError:
															raise ImportError(lowercase       )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "gpt":
											from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
											    convert_openai_checkpoint_to_pytorch,
											)

											convert_openai_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "transfo_xl":
											try:
															from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
															    convert_transfo_xl_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											if "ckpt" in self._tf_checkpoint.lower():
															A__        =			self._tf_checkpoint
															A__        =			""
											else:
															A__        =			self._tf_checkpoint
															A__        =			""
											convert_transfo_xl_checkpoint_to_pytorch(
											    lowercase      ,      self._config      ,      self._pytorch_dump_output      ,      lowercase       )
							elif self._model_type == "gpt2":
											try:
															from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
															    convert_gpta_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "xlnet":
											try:
															from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
															    convert_xlnet_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_xlnet_checkpoint_to_pytorch(
											    self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output      ,      self._finetuning_task_name       )
							elif self._model_type == "xlm":
											from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
											    convert_xlm_checkpoint_to_pytorch,
											)

											convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output       )
							elif self._model_type == "lxmert":
											from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
											    convert_lxmert_checkpoint_to_pytorch,
											)

											convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output       )
							elif self._model_type == "rembert":
											from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
											    convert_rembert_tf_checkpoint_to_pytorch,
											)

											convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							else:
											raise ValueError(
											    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"       )

