


'''simple docstring'''


import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
						import tensorflow as tf

						tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
						import torch

if is_datasets_available():
						from datasets import load_dataset


UpperCAmelCase_		:       Union[str, Any]							       =		5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors







def       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				return PTtoTFCommand(
				    args.model_name							,				args.local_dir							,				args.max_error							,				args.new_weights							,				args.no_pr							,				args.push							,				args.extra_commit_description							,				args.override_model_class							,				)







class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							@staticmethod
							def UpperCAmelCase_     (   __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	str							=				parser.add_parser(
											    """pt-to-tf"""   , help=(
											        """CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."""
											        """ Can also be used to validate existing weights without opening PRs, with --no-pr."""
											    )   , )
											train_parser.add_argument(
											    """--model-name"""   , type=__snake_case   , required=__snake_case   , help="""The model name, including owner/organization, as seen on the hub."""   , )
											train_parser.add_argument(
											    """--local-dir"""   , type=__snake_case   , default=""""""   , help="""Optional local directory of the model repository. Defaults to /tmp/{model_name}"""   , )
											train_parser.add_argument(
											    """--max-error"""   , type=__snake_case   , default=__snake_case   , help=(
											        f"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
											    )   , )
											train_parser.add_argument(
											    """--new-weights"""   , action="""store_true"""   , help="""Optional flag to create new TensorFlow weights, even if they already exist."""   , )
											train_parser.add_argument(
											    """--no-pr"""   , action="""store_true"""   , help="""Optional flag to NOT open a PR with converted weights."""  )
											train_parser.add_argument(
											    """--push"""   , action="""store_true"""   , help="""Optional flag to push the weights directly to `main` (requires permissions)"""   , )
											train_parser.add_argument(
											    """--extra-commit-description"""   , type=__snake_case   , default=""""""   , help="""Optional additional commit description to use when opening a PR (e.g. to tag the owner)."""   , )
											train_parser.add_argument(
											    """--override-model-class"""   , type=__snake_case   , default=__snake_case   , help="""If you think you know better than the auto-detector, you can specify the model class here. """
											    """Can be either an AutoModel class or a specific model class like BertForSequenceClassification."""   , )
											train_parser.set_defaults(func=__snake_case  )






							@staticmethod
							def UpperCAmelCase_     (   __snake_case   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				set(pt_outputs.keys()  )
											_SCREAMING_SNAKE_CASE			:	List[str]							=				set(tf_outputs.keys()  )
											if pt_out_attrs != tf_out_attrs:
															raise ValueError(
															    f"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
															    f""" {tf_out_attrs})"""  )

											# 2. For each output attribute, computes the difference
											def _find_pt_tf_differences(__snake_case   , __snake_case   , __snake_case   , __snake_case=""  ):
															# If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
															# recursivelly, keeping the name of the attribute.
															if isinstance(__snake_case   , torch.Tensor  ):
																			_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				np.max(np.abs(pt_out.numpy() - tf_out.numpy()  )  )
																			_SCREAMING_SNAKE_CASE			:	str							=				tensor_difference
															else:
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				attr_name
																			for i, pt_item in enumerate(__snake_case  ):
																							# If it is a named attribute, we keep the name. Otherwise, just its index.
																							if isinstance(__snake_case   , __snake_case  ):
																											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				root_name + pt_item
																											_SCREAMING_SNAKE_CASE			:	List[Any]							=				tf_out[pt_item]
																											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				pt_out[pt_item]
																							else:
																											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				root_name + f"""[{i}]"""
																											_SCREAMING_SNAKE_CASE			:	str							=				tf_out[i]
																							_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				_find_pt_tf_differences(__snake_case   , __snake_case   , __snake_case   , __snake_case  )

															return differences

											return _find_pt_tf_differences(__snake_case   , __snake_case   , {}  )






							def __init__(   self   , __snake_case   , __snake_case   , __snake_case   , __snake_case   , __snake_case   , __snake_case   , __snake_case   , __snake_case   , *__snake_case   , ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				logging.get_logger("""transformers-cli/pt_to_tf"""  )
											_SCREAMING_SNAKE_CASE			:	str							=				model_name
											_SCREAMING_SNAKE_CASE			:	List[str]							=				local_dir if local_dir else os.path.join("""/tmp"""   , __snake_case  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				max_error
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				new_weights
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				no_pr
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				push
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				extra_commit_description
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				override_model_class






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case  ):

											def _get_audio_input():
															_SCREAMING_SNAKE_CASE			:	List[str]							=				load_dataset("""hf-internal-testing/librispeech_asr_dummy"""   , """clean"""   , split="""validation"""  )
															_SCREAMING_SNAKE_CASE			:	str							=				ds.sort("""id"""  ).select(range(2  )  )[:2]["""audio"""]
															_SCREAMING_SNAKE_CASE			:	Dict							=				[x["""array"""] for x in speech_samples]
															return raw_samples

											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				type(pt_model.config  )
											if model_config_class in PROCESSOR_MAPPING:
															_SCREAMING_SNAKE_CASE			:	int							=				AutoProcessor.from_pretrained(self._local_dir  )
															if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
																			_SCREAMING_SNAKE_CASE			:	Dict							=				processor.tokenizer.eos_token
											elif model_config_class in IMAGE_PROCESSOR_MAPPING:
															_SCREAMING_SNAKE_CASE			:	Any							=				AutoImageProcessor.from_pretrained(self._local_dir  )
											elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				AutoFeatureExtractor.from_pretrained(self._local_dir  )
											elif model_config_class in TOKENIZER_MAPPING:
															_SCREAMING_SNAKE_CASE			:	Optional[int]							=				AutoTokenizer.from_pretrained(self._local_dir  )
															if processor.pad_token is None:
																			_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				processor.eos_token
											else:
															raise ValueError(f"""Unknown data processing type (model config type: {model_config_class})"""  )

											_SCREAMING_SNAKE_CASE			:	Any							=				set(inspect.signature(pt_model.forward  ).parameters.keys()  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				{}
											if "input_ids" in model_forward_signature:
															processor_inputs.update(
															    {
															        """text""": ["""Hi there!""", """I am a batch with more than one row and different input lengths."""],
															        """padding""": True,
															        """truncation""": True,
															    }  )
											if "pixel_values" in model_forward_signature:
															_SCREAMING_SNAKE_CASE			:	Tuple							=				load_dataset("""cifar10"""   , """plain_text"""   , split="""test"""  )[:2]["""img"""]
															processor_inputs.update({"""images""": sample_images}  )
											if "input_features" in model_forward_signature:
															_SCREAMING_SNAKE_CASE			:	int							=				inspect.signature(processor.feature_extractor  ).parameters
															# Pad to the largest input length by default but take feature extractor default
															# padding value if it exists e.g. "max_length" and is not False or None
															if "padding" in feature_extractor_signature:
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				feature_extractor_signature["""padding"""].default
																			if default_strategy is not False and default_strategy is not None:
																							_SCREAMING_SNAKE_CASE			:	int							=				default_strategy
																			else:
																							_SCREAMING_SNAKE_CASE			:	str							=				True
															else:
																			_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				True
															processor_inputs.update({"""audio""": _get_audio_input(), """padding""": padding_strategy}  )
											if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
															processor_inputs.update({"""audio""": _get_audio_input(), """padding""": True}  )
											_SCREAMING_SNAKE_CASE			:	Tuple							=				processor(**__snake_case   , return_tensors="""pt"""  )
											_SCREAMING_SNAKE_CASE			:	str							=				processor(**__snake_case   , return_tensors="""tf"""  )

											# Extra input requirements, in addition to the input modality
											if (
											    config.is_encoder_decoder
											    or (hasattr(__snake_case   , """encoder"""  ) and hasattr(__snake_case   , """decoder"""  ))
											    or "decoder_input_ids" in tf_dummy_inputs
											):
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				np.asarray([[1], [1]]   , dtype=__snake_case  ) * (pt_model.config.decoder_start_token_id or 0)
															pt_input.update({"""decoder_input_ids""": torch.tensor(__snake_case  )}  )
															tf_input.update({"""decoder_input_ids""": tf.convert_to_tensor(__snake_case  )}  )

											return pt_input, tf_input






							def UpperCAmelCase_     (   self  ):
											# hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
											if version.parse(huggingface_hub.__version__  ) < version.parse("""0.9.0"""  ):
															raise ImportError(
															    """The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"""
															    """ installation."""  )
											else:
															from huggingface_hub import Repository, create_commit
															from huggingface_hub._commit_api import CommitOperationAdd

											# Fetch remote data
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				Repository(local_dir=self._local_dir   , clone_from=self._model_name  )

											# Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				AutoConfig.from_pretrained(self._local_dir  )
											_SCREAMING_SNAKE_CASE			:	Any							=				config.architectures
											if self._override_model_class is not None:
															if self._override_model_class.startswith("""TF"""  ):
																			_SCREAMING_SNAKE_CASE			:	Dict							=				[self._override_model_class[2:]]
															else:
																			_SCREAMING_SNAKE_CASE			:	str							=				[self._override_model_class]
															try:
																			_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				getattr(import_module("""transformers"""  )   , architectures[0]  )
															except AttributeError:
																			raise ValueError(f"""Model class {self._override_model_class} not found in transformers."""  )
															try:
																			_SCREAMING_SNAKE_CASE			:	Tuple							=				getattr(import_module("""transformers"""  )   , """TF""" + architectures[0]  )
															except AttributeError:
																			raise ValueError(f"""TF model class TF{self._override_model_class} not found in transformers."""  )
											elif architectures is None:  # No architecture defined -- use auto classes
															_SCREAMING_SNAKE_CASE			:	str							=				getattr(import_module("""transformers"""  )   , """AutoModel"""  )
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				getattr(import_module("""transformers"""  )   , """TFAutoModel"""  )
															self._logger.warning("""No detected architecture, using AutoModel/TFAutoModel"""  )
											else:  # Architecture defined -- use it
															if len(__snake_case  ) > 1:
																			raise ValueError(f"""More than one architecture was found, aborting. (architectures = {architectures})"""  )
															self._logger.warning(f"""Detected architecture: {architectures[0]}"""  )
															_SCREAMING_SNAKE_CASE			:	Dict							=				getattr(import_module("""transformers"""  )   , architectures[0]  )
															try:
																			_SCREAMING_SNAKE_CASE			:	Tuple							=				getattr(import_module("""transformers"""  )   , """TF""" + architectures[0]  )
															except AttributeError:
																			raise AttributeError(f"""The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."""  )

        # Check the TF dummy inputs to see what keys we need in the forward pass
											_SCREAMING_SNAKE_CASE			:	str							=				tf_class.from_config(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	int							=				tf_from_pt_model.dummy_inputs

											del tf_from_pt_model  # Try to keep only one model in memory at a time

											# Load the model and get some basic inputs
											_SCREAMING_SNAKE_CASE			:	int							=				pt_class.from_pretrained(self._local_dir  )
											pt_model.eval()

											_SCREAMING_SNAKE_CASE			:	int							=				self.get_inputs(__snake_case   , __snake_case   , __snake_case  )

											with torch.no_grad():
															_SCREAMING_SNAKE_CASE			:	int							=				pt_model(**__snake_case   , output_hidden_states=__snake_case  )
											del pt_model  # will no longer be used, and may have a large memory footprint

											_SCREAMING_SNAKE_CASE			:	int							=				tf_class.from_pretrained(self._local_dir   , from_pt=__snake_case  )
											_SCREAMING_SNAKE_CASE			:	str							=				tf_from_pt_model(**__snake_case   , output_hidden_states=__snake_case   , training=__snake_case  )

											# Confirms that cross loading PT weights into TF worked.
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.find_pt_tf_differences(__snake_case   , __snake_case  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				{k: v for k, v in crossload_differences.items() if """hidden""" not in k}
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				{k: v for k, v in crossload_differences.items() if """hidden""" in k}
											if len(__snake_case  ) == 0 and architectures is not None:
															raise ValueError(
															    f"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
															    """ output was found. All outputs start with 'hidden'"""  )
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				max(output_differences.values()  ) if output_differences else 0.0
											_SCREAMING_SNAKE_CASE			:	Any							=				max(hidden_differences.values()  )
											if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
															raise ValueError(
															    """The cross-loaded TensorFlow model has different outputs, something went wrong!\n"""
															    + f"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
															    + """\n""".join([f"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]  )
															    + f"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
															    + """\n""".join([f"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]  )  )

											# Save the weights in a TF format (if needed) and confirms that the results are still good
											_SCREAMING_SNAKE_CASE			:	Tuple							=				os.path.join(self._local_dir   , __snake_case  )
											_SCREAMING_SNAKE_CASE			:	str							=				os.path.join(self._local_dir   , __snake_case  )
											if (not os.path.exists(__snake_case  ) and not os.path.exists(__snake_case  )) or self._new_weights:
															tf_from_pt_model.save_pretrained(self._local_dir  )
											del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				tf_class.from_pretrained(self._local_dir  )
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				tf_model(**__snake_case   , output_hidden_states=__snake_case  )

											_SCREAMING_SNAKE_CASE			:	int							=				self.find_pt_tf_differences(__snake_case   , __snake_case  )
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				{k: v for k, v in conversion_differences.items() if """hidden""" not in k}
											_SCREAMING_SNAKE_CASE			:	Tuple							=				{k: v for k, v in conversion_differences.items() if """hidden""" in k}
											if len(__snake_case  ) == 0 and architectures is not None:
															raise ValueError(
															    f"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
															    """ output was found. All outputs start with 'hidden'"""  )
											_SCREAMING_SNAKE_CASE			:	str							=				max(output_differences.values()  ) if output_differences else 0.0
											_SCREAMING_SNAKE_CASE			:	Dict							=				max(hidden_differences.values()  )
											if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
															raise ValueError(
															    """The converted TensorFlow model has different outputs, something went wrong!\n"""
															    + f"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
															    + """\n""".join([f"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]  )
															    + f"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
															    + """\n""".join([f"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]  )  )

											_SCREAMING_SNAKE_CASE			:	Tuple							=				"""Update TF weights""" if self._new_weights else """Add TF weights"""
											if self._push:
															repo.git_add(auto_lfs_track=__snake_case  )
															repo.git_commit(__snake_case  )
															repo.git_push(blocking=__snake_case  )  # this prints a progress bar with the upload
															self._logger.warning(f"""TF weights pushed into {self._model_name}"""  )
											elif not self._no_pr:
															self._logger.warning("""Uploading the weights into a new PR..."""  )
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				(
															    """Model converted by the [`transformers`' `pt_to_tf`"""
															    """ CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). """
															    """All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"""
															    f"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
															    f"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
															    f"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
															    f"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
															)
															if self._max_error > MAX_ERROR:
																			commit_descrition += (
																			    f"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
																			)
															if self._extra_commit_description:
																			commit_descrition += "\n\n" + self._extra_commit_description

															# sharded model -> adds all related files (index and .h5 shards)
															if os.path.exists(__snake_case  ):
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				[
																			    CommitOperationAdd(path_in_repo=__snake_case   , path_or_fileobj=__snake_case  )
																			]
																			for shard_path in tf.io.gfile.glob(self._local_dir + """/tf_model-*.h5"""  ):
																							operations += [
																							    CommitOperationAdd(path_in_repo=os.path.basename(__snake_case  )   , path_or_fileobj=__snake_case  )
																							]
															else:
																			_SCREAMING_SNAKE_CASE			:	List[str]							=				[CommitOperationAdd(path_in_repo=__snake_case   , path_or_fileobj=__snake_case  )]

															_SCREAMING_SNAKE_CASE			:	int							=				create_commit(
															    repo_id=self._model_name   , operations=__snake_case   , commit_message=__snake_case   , commit_description=__snake_case   , repo_type="""model"""   , create_pr=__snake_case   , ).pr_url
															self._logger.warning(f"""PR open in {hub_pr_url}"""  )








'''simple docstring'''


import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





@require_tokenizers
class    lowercase__  (						unittest.TestCase ):





							'''simple docstring'''


							A_							:				Optional[Any]				     =  EsmTokenizer






							def UpperCAmelCase_     (   self  ):
											super().setUp()
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				tempfile.mkdtemp()
											# fmt: off
											_SCREAMING_SNAKE_CASE			:	List[str]							=				["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
											# fmt: on
											_SCREAMING_SNAKE_CASE			:	Any							=				os.path.join(self.tmpdirname   , VOCAB_FILES_NAMES["""vocab_file"""]  )
											with open(self.vocab_file   , """w"""   , encoding="""utf-8"""  ) as vocab_writer:
															vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]  )  )






							def UpperCAmelCase_     (   self   , **__snake_case  ):
											return [self.get_tokenizer(**__snake_case  )]






							def UpperCAmelCase_     (   self   , **__snake_case  ):
											return self.tokenizer_class.from_pretrained(self.tmpdirname   , **__snake_case  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[str]							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	int							=				tokenizer.tokenize("""LAGVS"""  )
											self.assertListEqual(__snake_case   , ["""L""", """A""", """G""", """V""", """S"""]  )
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case  )   , [4, 5, 6, 7, 8]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	int							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				"""LAGVS"""
											self.assertListEqual(tokenizer.encode(__snake_case  )   , [0, 4, 5, 6, 7, 8, 2]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Any							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	List[Any]							=				["""LAGVS""", """WCB"""]
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				tokenizer(__snake_case   , padding=__snake_case  )["""input_ids"""]

											self.assertListEqual(__snake_case   , [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	str							=				["""LAGVS""", """WCB"""]
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				tokenizer(__snake_case   , padding=__snake_case  )["""input_ids"""]

											self.assertListEqual(__snake_case   , [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	str							=				self.get_tokenizers(fast=__snake_case  )
											for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""  ):
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				"""<unk>"""
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				"""<mask>"""

																			_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				tokenizer.tokenize(__snake_case  )
																			_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				tokenizer.tokenize(__snake_case  )

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




