"""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       :    Any									=			5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors



def 				__a       (			_lowercase						):







					"""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        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):







				@staticmethod
				def   lowercase_		(    __UpperCAmelCase    :ArgumentParser  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					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   lowercase_		(    __UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Dict  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					set(pt_outputs.keys()  )
									lowerCamelCase__		:   int  				=					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    :Any      ,__UpperCAmelCase    :Any      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Union[str, 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__		:   Union[str, Any]  				=					np.max(np.abs(pt_out.numpy() - tf_out.numpy()  )  )
																			lowerCamelCase__		:   Tuple  				=					tensor_difference
														else:
																			lowerCamelCase__		:   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__		:   List[str]  				=					root_name + pt_item
																													lowerCamelCase__		:   Dict  				=					tf_out[pt_item]
																													lowerCamelCase__		:   Optional[Any]  				=					pt_out[pt_item]
																								else:
																													lowerCamelCase__		:   List[str]  				=					root_name + F"""[{i}]"""
																													lowerCamelCase__		:   Optional[int]  				=					tf_out[i]
																								lowerCamelCase__		:   List[str]  				=					_find_pt_tf_differences(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase  )

														return differences

									return _find_pt_tf_differences(__UpperCAmelCase      ,__UpperCAmelCase      ,{}  )







				def __init__(    self    :Optional[Any]      ,__UpperCAmelCase    :str      ,__UpperCAmelCase    :str      ,__UpperCAmelCase    :float      ,__UpperCAmelCase    :bool      ,__UpperCAmelCase    :bool      ,__UpperCAmelCase    :bool      ,__UpperCAmelCase    :str      ,__UpperCAmelCase    :str      ,*__UpperCAmelCase    :List[str]      ,)      ->					Any:




									"""simple docstring"""



									lowerCamelCase__		:   List[str]  				=					logging.get_logger('''transformers-cli/pt_to_tf'''  )
									lowerCamelCase__		:   Any  				=					model_name
									lowerCamelCase__		:   Any  				=					local_dir if local_dir else os.path.join('''/tmp'''      ,__UpperCAmelCase  )
									lowerCamelCase__		:   str  				=					max_error
									lowerCamelCase__		:   str  				=					new_weights
									lowerCamelCase__		:   str  				=					no_pr
									lowerCamelCase__		:   Optional[Any]  				=					push
									lowerCamelCase__		:   Tuple  				=					extra_commit_description
									lowerCamelCase__		:   Dict  				=					override_model_class







				def   lowercase_		(    self    :Any      ,__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :Union[str, Any]  )      ->					List[Any]:




									"""simple docstring"""



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

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

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




									"""simple docstring"""



									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__		:   int  				=					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__		:   List[str]  				=					AutoConfig.from_pretrained(self._local_dir  )
									lowerCamelCase__		:   Union[str, Any]  				=					config.architectures
									if self._override_model_class is not None:
														if self._override_model_class.startswith('''TF'''  ):
																			lowerCamelCase__		:   Union[str, Any]  				=					[self._override_model_class[2:]]
														else:
																			lowerCamelCase__		:   List[str]  				=					[self._override_model_class]
														try:
																			lowerCamelCase__		:   List[str]  				=					getattr(import_module('''transformers'''  )      ,architectures[0]  )
														except AttributeError:
																			raise ValueError(F"""Model class {self._override_model_class} not found in transformers."""  )
														try:
																			lowerCamelCase__		:   Dict  				=					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[str]  				=					getattr(import_module('''transformers'''  )      ,'''AutoModel'''  )
														lowerCamelCase__		:   Union[str, 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__		:   int  				=					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__		:   Dict  				=					tf_class.from_config(__UpperCAmelCase  )
									lowerCamelCase__		:   Any  				=					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__		:   Any  				=					pt_class.from_pretrained(self._local_dir  )
									pt_model.eval()

									lowerCamelCase__				,						lowerCamelCase__		:   Optional[int]  				=					self.get_inputs(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase  )

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

									lowerCamelCase__		:   Any  				=					tf_class.from_pretrained(self._local_dir      ,from_pt=__UpperCAmelCase  )
									lowerCamelCase__		:   Union[str, Any]  				=					tf_from_pt_model(**__UpperCAmelCase      ,output_hidden_states=__UpperCAmelCase      ,training=__UpperCAmelCase  )

									# Confirms that cross loading PT weights into TF worked.
									lowerCamelCase__		:   int  				=					self.find_pt_tf_differences(__UpperCAmelCase      ,__UpperCAmelCase  )
									lowerCamelCase__		:   List[Any]  				=					{k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
									lowerCamelCase__		:   List[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__		:   Union[str, Any]  				=					max(output_differences.values()  ) if output_differences else 0.0
									lowerCamelCase__		:   List[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__		:   Any  				=					os.path.join(self._local_dir      ,__UpperCAmelCase  )
									lowerCamelCase__		:   Union[str, 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__		:   Optional[Any]  				=					tf_class.from_pretrained(self._local_dir  )
									lowerCamelCase__		:   List[str]  				=					tf_model(**__UpperCAmelCase      ,output_hidden_states=__UpperCAmelCase  )

									lowerCamelCase__		:   Optional[int]  				=					self.find_pt_tf_differences(__UpperCAmelCase      ,__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[Any]  				=					{k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
									lowerCamelCase__		:   List[Any]  				=					{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__		:   List[str]  				=					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__		:   List[Any]  				=					'''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__		:   int  				=					(
														    '''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__		:   List[str]  				=					[
																			    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__		:   str  				=					[CommitOperationAdd(path_in_repo=__UpperCAmelCase      ,path_or_fileobj=__UpperCAmelCase  )]

														lowerCamelCase__		:   Optional[int]  				=					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 os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
					from tokenizers import Tokenizer
					from tokenizers.models import WordPiece

class        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







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




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					list(tokenizer_class.max_model_input_sizes.keys()  )
									for model_name in sa_models[:1]:
														lowerCamelCase__		:   Tuple  				=					tokenizer_class.from_pretrained(__UpperCAmelCase  )
														self.assertIsNotNone(__UpperCAmelCase  )
														self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )
														self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )

														for special_tok in tokenizer.all_special_tokens:
																			self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )
																			lowerCamelCase__		:   str  				=					tokenizer.convert_tokens_to_ids(__UpperCAmelCase  )
																			self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )







				def   lowercase_		(    self    :Any      ,__UpperCAmelCase    :BatchEncoding      ,__UpperCAmelCase    :Optional[Callable] = None  )      ->					Optional[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   List[str]  				=					pickle.dumps(__UpperCAmelCase  )
									self.assertIsNotNone(__UpperCAmelCase  )

									lowerCamelCase__		:   int  				=					pickle.loads(__UpperCAmelCase  )

									# Ensure is_fast is correctly restored
									self.assertEqual(be_restored.is_fast      ,be_original.is_fast  )

									# Ensure encodings are potentially correctly restored
									if be_original.is_fast:
														self.assertIsNotNone(be_restored.encodings  )
									else:
														self.assertIsNone(be_restored.encodings  )

									# Ensure the keys are the same
									for original_v, restored_v in zip(be_original.values()      ,be_restored.values()  ):
														if equal_op:
																			self.assertTrue(equal_op(__UpperCAmelCase      ,__UpperCAmelCase  )  )
														else:
																			self.assertEqual(__UpperCAmelCase      ,__UpperCAmelCase  )







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




									"""simple docstring"""



									self.check_tokenizer_from_pretrained(__UpperCAmelCase  )







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




									"""simple docstring"""



									self.assertEqual(TensorType('''tf'''  )      ,TensorType.TENSORFLOW  )
									self.assertEqual(TensorType('''pt'''  )      ,TensorType.PYTORCH  )
									self.assertEqual(TensorType('''np'''  )      ,TensorType.NUMPY  )







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




									"""simple docstring"""



									import numpy as np

									lowerCamelCase__		:   Any  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   Optional[int]  				=					BertTokenizerFast.from_pretrained('''bert-base-cased'''  )

									# Python no tensor
									with self.subTest('''BatchEncoding (Python, return_tensors=None)'''  ):
														self.assert_dump_and_restore(tokenizer_p('''Small example to encode'''  )  )

									with self.subTest('''BatchEncoding (Python, return_tensors=NUMPY)'''  ):
														self.assert_dump_and_restore(
														    tokenizer_p('''Small example to encode'''      ,return_tensors=TensorType.NUMPY  )      ,np.array_equal  )

									with self.subTest('''BatchEncoding (Rust, return_tensors=None)'''  ):
														self.assert_dump_and_restore(tokenizer_r('''Small example to encode'''  )  )

									with self.subTest('''BatchEncoding (Rust, return_tensors=NUMPY)'''  ):
														self.assert_dump_and_restore(
														    tokenizer_r('''Small example to encode'''      ,return_tensors=TensorType.NUMPY  )      ,np.array_equal  )







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




									"""simple docstring"""



									import tensorflow as tf

									def tf_array_equals(__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :List[Any]  ):
														return tf.reduce_all(tf.equal(__UpperCAmelCase      ,__UpperCAmelCase  )  )

									lowerCamelCase__		:   str  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   List[Any]  				=					BertTokenizerFast.from_pretrained('''bert-base-cased'''  )

									with self.subTest('''BatchEncoding (Python, return_tensors=TENSORFLOW)'''  ):
														self.assert_dump_and_restore(
														    tokenizer_p('''Small example to encode'''      ,return_tensors=TensorType.TENSORFLOW  )      ,__UpperCAmelCase  )

									with self.subTest('''BatchEncoding (Rust, return_tensors=TENSORFLOW)'''  ):
														self.assert_dump_and_restore(
														    tokenizer_r('''Small example to encode'''      ,return_tensors=TensorType.TENSORFLOW  )      ,__UpperCAmelCase  )







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




									"""simple docstring"""



									import torch

									lowerCamelCase__		:   Optional[int]  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   List[str]  				=					BertTokenizerFast.from_pretrained('''bert-base-cased'''  )

									with self.subTest('''BatchEncoding (Python, return_tensors=PYTORCH)'''  ):
														self.assert_dump_and_restore(
														    tokenizer_p('''Small example to encode'''      ,return_tensors=TensorType.PYTORCH  )      ,torch.equal  )

									with self.subTest('''BatchEncoding (Rust, return_tensors=PYTORCH)'''  ):
														self.assert_dump_and_restore(
														    tokenizer_r('''Small example to encode'''      ,return_tensors=TensorType.PYTORCH  )      ,torch.equal  )







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




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   Any  				=					BertTokenizerFast.from_pretrained('''bert-base-cased'''  )

									with self.subTest('''Python Tokenizer'''  ):
														self.assertFalse(tokenizer_p('''Small example to_encode'''  ).is_fast  )

									with self.subTest('''Rust Tokenizer'''  ):
														self.assertTrue(tokenizer_r('''Small example to_encode'''  ).is_fast  )







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




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					BertTokenizerFast.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   str  				=					tokenizer_r(['''Test''', '''\xad''', '''test''']      ,is_split_into_words=__UpperCAmelCase  )

									self.assertEqual(encoded.word_to_tokens(0  )      ,TokenSpan(start=1      ,end=2  )  )
									self.assertEqual(encoded.word_to_tokens(1  )      ,__UpperCAmelCase  )
									self.assertEqual(encoded.word_to_tokens(2  )      ,TokenSpan(start=2      ,end=3  )  )







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




									"""simple docstring"""



									lowerCamelCase__		:   Optional[int]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   Tuple  				=					batch.convert_to_tensors(tensor_type='''np'''  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(2, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(2,)  )
									# test converting the converted
									with CaptureStderr() as cs:
														lowerCamelCase__		:   List[str]  				=					batch.convert_to_tensors(tensor_type='''np'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   Union[str, Any]  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Dict  				=					batch.convert_to_tensors(tensor_type='''np'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







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




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   Any  				=					batch.convert_to_tensors(tensor_type='''pt'''  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(2, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(2,)  )
									# test converting the converted
									with CaptureStderr() as cs:
														lowerCamelCase__		:   Optional[int]  				=					batch.convert_to_tensors(tensor_type='''pt'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   Optional[int]  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Optional[Any]  				=					batch.convert_to_tensors(tensor_type='''pt'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







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




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   Union[str, Any]  				=					batch.convert_to_tensors(tensor_type='''tf'''  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(2, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(2,)  )
									# test converting the converted
									with CaptureStderr() as cs:
														lowerCamelCase__		:   Optional[Any]  				=					batch.convert_to_tensors(tensor_type='''tf'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   int  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Dict  				=					batch.convert_to_tensors(tensor_type='''tf'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







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




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   str  				=					batch.convert_to_tensors(tensor_type='''jax'''  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(2, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(2,)  )
									# test converting the converted
									with CaptureStderr() as cs:
														lowerCamelCase__		:   Any  				=					batch.convert_to_tensors(tensor_type='''jax'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   List[Any]  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Tuple  				=					batch.convert_to_tensors(tensor_type='''jax'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







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




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					[{'''input_ids''': np.array([0, 1, 2]  )}, {'''input_ids''': np.array([0, 1, 2, 3]  )}]
									lowerCamelCase__		:   Optional[Any]  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )

									lowerCamelCase__		:   Dict  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase  )
									self.assertTrue(isinstance(batch['''input_ids''']      ,np.ndarray  )  )
									self.assertEqual(batch['''input_ids'''].tolist()      ,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
									lowerCamelCase__		:   Optional[Any]  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,return_tensors='''np'''  )
									self.assertTrue(isinstance(batch['''input_ids''']      ,np.ndarray  )  )
									self.assertEqual(batch['''input_ids'''].tolist()      ,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )







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




									"""simple docstring"""



									import torch

									lowerCamelCase__		:   Union[str, Any]  				=					[{'''input_ids''': torch.tensor([0, 1, 2]  )}, {'''input_ids''': torch.tensor([0, 1, 2, 3]  )}]
									lowerCamelCase__		:   Any  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )

									lowerCamelCase__		:   Tuple  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase  )
									self.assertTrue(isinstance(batch['''input_ids''']      ,torch.Tensor  )  )
									self.assertEqual(batch['''input_ids'''].tolist()      ,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
									lowerCamelCase__		:   Optional[int]  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,return_tensors='''pt'''  )
									self.assertTrue(isinstance(batch['''input_ids''']      ,torch.Tensor  )  )
									self.assertEqual(batch['''input_ids'''].tolist()      ,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )







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




									"""simple docstring"""



									import tensorflow as tf

									lowerCamelCase__		:   List[Any]  				=					[{'''input_ids''': tf.constant([0, 1, 2]  )}, {'''input_ids''': tf.constant([0, 1, 2, 3]  )}]
									lowerCamelCase__		:   str  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )

									lowerCamelCase__		:   Tuple  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase  )
									self.assertTrue(isinstance(batch['''input_ids''']      ,tf.Tensor  )  )
									self.assertEqual(batch['''input_ids'''].numpy().tolist()      ,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
									lowerCamelCase__		:   Tuple  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,return_tensors='''tf'''  )
									self.assertTrue(isinstance(batch['''input_ids''']      ,tf.Tensor  )  )
									self.assertEqual(batch['''input_ids'''].numpy().tolist()      ,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )







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




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					Tokenizer(WordPiece(unk_token='''[UNK]'''  )  )
									PreTrainedTokenizerFast(tokenizer_object=__UpperCAmelCase  )







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




									"""simple docstring"""



									lowerCamelCase__		:   int  				=					Tokenizer(WordPiece(unk_token='''[UNK]'''  )  )
									with tempfile.TemporaryDirectory() as tmpdirname:
														bert_tokenizer.save(os.path.join(__UpperCAmelCase      ,'''tokenizer.json'''  )  )
														PreTrainedTokenizerFast(tokenizer_file=os.path.join(__UpperCAmelCase      ,'''tokenizer.json'''  )  )






