






'''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


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





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			str:
							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      (							_lowercase						):




							@staticmethod
							def 						lowerCamelCase_							(				UpperCamelCase__:							ArgumentParser	):
														lowerCamelCase__   :      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=UpperCamelCase__     ,				required=UpperCamelCase__     ,				help="""The model name, including owner/organization, as seen on the hub."""     ,				)
														train_parser.add_argument(
														    """--local-dir"""     ,				type=UpperCamelCase__     ,				default=""""""     ,				help="""Optional local directory of the model repository. Defaults to /tmp/{model_name}"""     ,				)
														train_parser.add_argument(
														    """--max-error"""     ,				type=UpperCamelCase__     ,				default=UpperCamelCase__     ,				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=UpperCamelCase__     ,				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=UpperCamelCase__     ,				default=UpperCamelCase__     ,				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=UpperCamelCase__	)




							@staticmethod
							def 						lowerCamelCase_							(				UpperCamelCase__:							List[Any]     ,				UpperCamelCase__:							List[Any]	):
														lowerCamelCase__   :      str			    =			set(pt_outputs.keys()	)
														lowerCamelCase__   :      Union[str, Any]			    =			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(UpperCamelCase__:							str     ,				UpperCamelCase__:							str     ,				UpperCamelCase__:							int     ,				UpperCamelCase__:							List[Any]=""	):
																					# 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(UpperCamelCase__     ,				torch.Tensor	):
																												lowerCamelCase__   :      List[str]			    =			np.max(np.abs(pt_out.numpy() - tf_out.numpy()	)	)
																												lowerCamelCase__   :      int			    =			tensor_difference
																					else:
																												lowerCamelCase__   :      Optional[int]			    =			attr_name
																												for i, pt_item in enumerate(UpperCamelCase__	):
																																			# If it is a named attribute, we keep the name. Otherwise, just its index.
																																			if isinstance(UpperCamelCase__     ,				UpperCamelCase__	):
																																										lowerCamelCase__   :      Tuple			    =			root_name + pt_item
																																										lowerCamelCase__   :      List[Any]			    =			tf_out[pt_item]
																																										lowerCamelCase__   :      Tuple			    =			pt_out[pt_item]
																																			else:
																																										lowerCamelCase__   :      str			    =			root_name + F'''[{i}]'''
																																										lowerCamelCase__   :      Optional[int]			    =			tf_out[i]
																																			lowerCamelCase__   :      int			    =			_find_pt_tf_differences(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__	)

																					return differences

														return _find_pt_tf_differences(UpperCamelCase__     ,				UpperCamelCase__     ,				{}	)




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




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				UpperCamelCase__:							Optional[Any]     ,				UpperCamelCase__:							List[str]     ,				UpperCamelCase__:							Dict	):

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

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

														lowerCamelCase__   :      int			    =			set(inspect.signature(pt_model.forward	).parameters.keys()	)
														lowerCamelCase__   :      Tuple			    =			{}
														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:
																					lowerCamelCase__   :      List[Any]			    =			load_dataset("""cifar10"""     ,				"""plain_text"""     ,				split="""test"""	)[:2]["""img"""]
																					processor_inputs.update({"""images""": sample_images}	)
														if "input_features" in model_forward_signature:
																					lowerCamelCase__   :      Tuple			    =			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:
																												lowerCamelCase__   :      int			    =			feature_extractor_signature["""padding"""].default
																												if default_strategy is not False and default_strategy is not None:
																																			lowerCamelCase__   :      Optional[int]			    =			default_strategy
																												else:
																																			lowerCamelCase__   :      Tuple			    =			True
																					else:
																												lowerCamelCase__   :      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}	)
														lowerCamelCase__   :      Union[str, Any]			    =			processor(**UpperCamelCase__     ,				return_tensors="""pt"""	)
														lowerCamelCase__   :      Union[str, Any]			    =			processor(**UpperCamelCase__     ,				return_tensors="""tf"""	)

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

														return pt_input, tf_input




							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														# 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
														lowerCamelCase__   :      Tuple			    =			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
														lowerCamelCase__   :      str			    =			AutoConfig.from_pretrained(self._local_dir	)
														lowerCamelCase__   :      List[Any]			    =			config.architectures
														if self._override_model_class is not None:
																					if self._override_model_class.startswith("""TF"""	):
																												lowerCamelCase__   :      Dict			    =			[self._override_model_class[2:]]
																					else:
																												lowerCamelCase__   :      str			    =			[self._override_model_class]
																					try:
																												lowerCamelCase__   :      str			    =			getattr(import_module("""transformers"""	)     ,				architectures[0]	)
																					except AttributeError:
																												raise ValueError(F'''Model class {self._override_model_class} not found in transformers.'''	)
																					try:
																												lowerCamelCase__   :      Union[str, Any]			    =			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
																					lowerCamelCase__   :      List[Any]			    =			getattr(import_module("""transformers"""	)     ,				"""AutoModel"""	)
																					lowerCamelCase__   :      List[Any]			    =			getattr(import_module("""transformers"""	)     ,				"""TFAutoModel"""	)
																					self._logger.warning("""No detected architecture, using AutoModel/TFAutoModel"""	)
														else:  # Architecture defined -- use it
																					if len(UpperCamelCase__	) > 1:
																												raise ValueError(F'''More than one architecture was found, aborting. (architectures = {architectures})'''	)
																					self._logger.warning(F'''Detected architecture: {architectures[0]}'''	)
																					lowerCamelCase__   :      int			    =			getattr(import_module("""transformers"""	)     ,				architectures[0]	)
																					try:
																												lowerCamelCase__   :      str			    =			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
														lowerCamelCase__   :      Union[str, Any]			    =			tf_class.from_config(UpperCamelCase__	)
														lowerCamelCase__   :      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
														lowerCamelCase__   :      Tuple			    =			pt_class.from_pretrained(self._local_dir	)
														pt_model.eval()

														lowerCamelCase__    ,						lowerCamelCase__   :      Any			    =			self.get_inputs(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__	)

														with torch.no_grad():
																					lowerCamelCase__   :      List[Any]			    =			pt_model(**UpperCamelCase__     ,				output_hidden_states=UpperCamelCase__	)
														del pt_model  # will no longer be used, and may have a large memory footprint

														lowerCamelCase__   :      int			    =			tf_class.from_pretrained(self._local_dir     ,				from_pt=UpperCamelCase__	)
														lowerCamelCase__   :      List[str]			    =			tf_from_pt_model(**UpperCamelCase__     ,				output_hidden_states=UpperCamelCase__     ,				training=UpperCamelCase__	)

														# Confirms that cross loading PT weights into TF worked.
														lowerCamelCase__   :      Optional[int]			    =			self.find_pt_tf_differences(UpperCamelCase__     ,				UpperCamelCase__	)
														lowerCamelCase__   :      List[str]			    =			{k: v for k, v in crossload_differences.items() if """hidden""" not in k}
														lowerCamelCase__   :      Optional[Any]			    =			{k: v for k, v in crossload_differences.items() if """hidden""" in k}
														if len(UpperCamelCase__	) == 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'"""	)
														lowerCamelCase__   :      List[str]			    =			max(output_differences.values()	) if output_differences else 0.0
														lowerCamelCase__   :      Union[str, 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
														lowerCamelCase__   :      Dict			    =			os.path.join(self._local_dir     ,				UpperCamelCase__	)
														lowerCamelCase__   :      Any			    =			os.path.join(self._local_dir     ,				UpperCamelCase__	)
														if (not os.path.exists(UpperCamelCase__	) and not os.path.exists(UpperCamelCase__	)) 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

														lowerCamelCase__   :      Tuple			    =			tf_class.from_pretrained(self._local_dir	)
														lowerCamelCase__   :      int			    =			tf_model(**UpperCamelCase__     ,				output_hidden_states=UpperCamelCase__	)

														lowerCamelCase__   :      Union[str, Any]			    =			self.find_pt_tf_differences(UpperCamelCase__     ,				UpperCamelCase__	)
														lowerCamelCase__   :      Any			    =			{k: v for k, v in conversion_differences.items() if """hidden""" not in k}
														lowerCamelCase__   :      int			    =			{k: v for k, v in conversion_differences.items() if """hidden""" in k}
														if len(UpperCamelCase__	) == 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'"""	)
														lowerCamelCase__   :      int			    =			max(output_differences.values()	) if output_differences else 0.0
														lowerCamelCase__   :      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]	)	)

														lowerCamelCase__   :      Dict			    =			"""Update TF weights""" if self._new_weights else """Add TF weights"""
														if self._push:
																					repo.git_add(auto_lfs_track=UpperCamelCase__	)
																					repo.git_commit(UpperCamelCase__	)
																					repo.git_push(blocking=UpperCamelCase__	)  # 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..."""	)
																					lowerCamelCase__   :      Tuple			    =			(
																					    """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(UpperCamelCase__	):
																												lowerCamelCase__   :      Any			    =			[
																												    CommitOperationAdd(path_in_repo=UpperCamelCase__     ,				path_or_fileobj=UpperCamelCase__	)
																												]
																												for shard_path in tf.io.gfile.glob(self._local_dir + """/tf_model-*.h5"""	):
																																			operations += [
																																			    CommitOperationAdd(path_in_repo=os.path.basename(UpperCamelCase__	)     ,				path_or_fileobj=UpperCamelCase__	)
																																			]
																					else:
																												lowerCamelCase__   :      Tuple			    =			[CommitOperationAdd(path_in_repo=UpperCamelCase__     ,				path_or_fileobj=UpperCamelCase__	)]

																					lowerCamelCase__   :      Optional[Any]			    =			create_commit(
																					    repo_id=self._model_name     ,				operations=UpperCamelCase__     ,				commit_message=UpperCamelCase__     ,				commit_description=UpperCamelCase__     ,				repo_type="""model"""     ,				create_pr=UpperCamelCase__     ,				).pr_url
																					self._logger.warning(F'''PR open in {hub_pr_url}'''	)









'''simple docstring'''







import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class _lowercase      (							_lowercase							, unittest.TestCase						):
							a							 =			BartTokenizer
							a							 =			BartTokenizerFast
							a							 =			True
							a							 =			filter_roberta_detectors




							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														super().setUp()
														lowerCamelCase__   :      int			    =			[
														    """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__   :      List[str]			    =			["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
														lowerCamelCase__   :      List[Any]			    =			{"""unk_token""": """<unk>"""}

														lowerCamelCase__   :      List[str]			    =			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 						lowerCamelCase_							(				self:							Tuple     ,				**UpperCamelCase__:							Dict	):
														kwargs.update(self.special_tokens_map	)
														return self.tokenizer_class.from_pretrained(self.tmpdirname     ,				**UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				**UpperCamelCase__:							Tuple	):
														kwargs.update(self.special_tokens_map	)
														return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,				**UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							List[str]     ,				UpperCamelCase__:							List[Any]	):
														return "lower newer", "lower newer"




							@cached_property
							def 						lowerCamelCase_							(				self:							str	):
														return BartTokenizer.from_pretrained("""facebook/bart-large"""	)




							@cached_property
							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														return BartTokenizerFast.from_pretrained("""facebook/bart-large"""	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Optional[int]	):
														lowerCamelCase__   :      Any			    =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														lowerCamelCase__   :      List[Any]			    =			[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Tuple			    =			tokenizer(UpperCamelCase__     ,				max_length=len(UpperCamelCase__	)     ,				padding=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					self.assertIsInstance(UpperCamelCase__     ,				UpperCamelCase__	)

																					self.assertEqual((2, 9)     ,				batch.input_ids.shape	)
																					self.assertEqual((2, 9)     ,				batch.attention_mask.shape	)
																					lowerCamelCase__   :      Optional[Any]			    =			batch.input_ids.tolist()[0]
																					self.assertListEqual(UpperCamelCase__     ,				UpperCamelCase__	)
																					# Test that special tokens are reset




							@require_torch
							def 						lowerCamelCase_							(				self:							Tuple	):
														lowerCamelCase__   :      str			    =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(UpperCamelCase__     ,				padding=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					# check if input_ids are returned and no labels
																					self.assertIn("""input_ids"""     ,				UpperCamelCase__	)
																					self.assertIn("""attention_mask"""     ,				UpperCamelCase__	)
																					self.assertNotIn("""labels"""     ,				UpperCamelCase__	)
																					self.assertNotIn("""decoder_attention_mask"""     ,				UpperCamelCase__	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Dict	):
														lowerCamelCase__   :      str			    =			[
														    """Summary of the text.""",
														    """Another summary.""",
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Any			    =			tokenizer(text_target=UpperCamelCase__     ,				max_length=32     ,				padding="""max_length"""     ,				return_tensors="""pt"""	)
																					self.assertEqual(32     ,				targets["""input_ids"""].shape[1]	)




							@require_torch
							def 						lowerCamelCase_							(				self:							str	):
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(
																					    ["""I am a small frog""" * 1_024, """I am a small frog"""]     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					self.assertIsInstance(UpperCamelCase__     ,				UpperCamelCase__	)
																					self.assertEqual(batch.input_ids.shape     ,				(2, 1_024)	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														lowerCamelCase__   :      Optional[Any]			    =			["""A long paragraph for summarization."""]
														lowerCamelCase__   :      Any			    =			[
														    """Summary of the text.""",
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(UpperCamelCase__     ,				return_tensors="""pt"""	)
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(text_target=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					lowerCamelCase__   :      List[str]			    =			inputs["""input_ids"""]
																					lowerCamelCase__   :      Optional[int]			    =			targets["""input_ids"""]
																					self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
																					self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
																					self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
																					self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)




							def 						lowerCamelCase_							(				self:							Optional[int]	):
														pass




							def 						lowerCamelCase_							(				self:							List[str]	):
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
																												lowerCamelCase__   :      int			    =			self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__	)
																												lowerCamelCase__   :      int			    =			self.tokenizer_class.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__	)
																												lowerCamelCase__   :      Dict			    =			"""A, <mask> AllenNLP sentence."""
																												lowerCamelCase__   :      Dict			    =			tokenizer_r.encode_plus(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__	)
																												lowerCamelCase__   :      Union[str, Any]			    =			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__   :      Any			    =			tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]	)
																												lowerCamelCase__   :      Any			    =			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, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
																												self.assertSequenceEqual(tokens_r["""input_ids"""]     ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

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

