"""simple docstring"""


from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
	import numpy as np
	import tensorflow as tf

	from transformers import (
	    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
	    TF_MODEL_FOR_MASKED_LM_MAPPING,
	    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
	    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
	    TF_MODEL_FOR_PRETRAINING_MAPPING,
	    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
	    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
	    TFSharedEmbeddings,
	)

	if _tf_gpu_memory_limit is not None:
		a							=tf.config.list_physical_devices('GPU')
		for gpu in gpus:
			# Restrict TensorFlow to only allocate x GB of memory on the GPUs
			try:
				tf.config.set_logical_device_configuration(
				    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
				)
				a							=tf.config.list_logical_devices('GPU')
				print('Logical GPUs', logical_gpus)
			except RuntimeError as e:
				# Virtual devices must be set before GPUs have been initialized
				print(e)






@require_tf
class        __UpperCAmelCase						:
				A__				:       Any  							=	None
				A__				:       Dict  							=	()
				A__				:       Dict  							=	()
				A__				:       Tuple  							=	True
				A__				:       Any  							=	True
				A__				:       Tuple  							=	True
				A__				:       int  							=	False

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase=False				):
										lowerCamelCase__ =copy.deepcopy(_lowerCamelCase				)

										if model_class in get_values(_lowerCamelCase				):
																lowerCamelCase__ ={
																    k: tf.tile(tf.expand_dims(_lowerCamelCase     ,			1				)     ,			(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)				)
																    if isinstance(_lowerCamelCase     ,			tf.Tensor				) and v.ndim > 0
																    else v
																    for k, v in inputs_dict.items()
																}

										if return_labels:
																if model_class in get_values(_lowerCamelCase				):
																						lowerCamelCase__ =tf.ones(self.model_tester.batch_size     ,			dtype=tf.intaa				)
																elif model_class in get_values(_lowerCamelCase				):
																						lowerCamelCase__ =tf.zeros(self.model_tester.batch_size     ,			dtype=tf.intaa				)
																						lowerCamelCase__ =tf.zeros(self.model_tester.batch_size     ,			dtype=tf.intaa				)
																elif model_class in [
																    *get_values(_lowerCamelCase				),
																    *get_values(_lowerCamelCase				),
																]:
																						lowerCamelCase__ =tf.zeros(self.model_tester.batch_size     ,			dtype=tf.intaa				)
																elif model_class in get_values(_lowerCamelCase				):
																						lowerCamelCase__ =tf.zeros(self.model_tester.batch_size     ,			dtype=tf.intaa				)
																elif model_class in [
																    *get_values(_lowerCamelCase				),
																    *get_values(_lowerCamelCase				),
																    *get_values(_lowerCamelCase				),
																    *get_values(_lowerCamelCase				),
																    *get_values(_lowerCamelCase				),
																]:
																						lowerCamelCase__ =tf.zeros(
																						    (self.model_tester.batch_size, self.model_tester.seq_length)     ,			dtype=tf.intaa				)
										return inputs_dict

				@slow
				def 		_a     (				self				):
										lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
																lowerCamelCase__ =self._prepare_for_class(_lowerCamelCase     ,			_lowerCamelCase				)
																lowerCamelCase__ =model_class(_lowerCamelCase				)

																@tf.function
																def run_in_graph_mode():
																						return model(_lowerCamelCase				)

																lowerCamelCase__ =run_in_graph_mode()
																self.assertIsNotNone(_lowerCamelCase				)

				@slow
				def 		_a     (				self				):
										lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
																lowerCamelCase__ =self._prepare_for_class(_lowerCamelCase     ,			_lowerCamelCase				)
																lowerCamelCase__ =model_class(_lowerCamelCase				)

																@tf.function(experimental_compile=_lowerCamelCase				)
																def run_in_graph_mode():
																						return model(_lowerCamelCase				)

																lowerCamelCase__ =run_in_graph_mode()
																self.assertIsNotNone(_lowerCamelCase				)

				@slow
				def 		_a     (				self				):
										lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
																lowerCamelCase__ =model_class(_lowerCamelCase				)
																if getattr(_lowerCamelCase     ,			"hf_compute_loss"     ,			_lowerCamelCase				):
																						# Test that model correctly compute the loss with kwargs
																						lowerCamelCase__ =self._prepare_for_class(inputs_dict.copy()     ,			_lowerCamelCase     ,			return_labels=_lowerCamelCase				)
																						# Is there a better way to remove these decoder inputs?
																						lowerCamelCase__ ={
																						    key: val
																						    for key, val in prepared_for_class.items()
																						    if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
																						}

																						lowerCamelCase__ ={
																						    "labels",
																						    "label",
																						    "label_ids",
																						    "start_positions",
																						    "start_position",
																						    "end_positions",
																						    "end_position",
																						    "next_sentence_label",
																						}
																						lowerCamelCase__ =possible_label_cols.intersection(set(_lowerCamelCase				)				)
																						self.assertGreater(len(_lowerCamelCase				)     ,			0     ,			msg="No matching label names found!"				)
																						lowerCamelCase__ ={key: val for key, val in prepared_for_class.items() if key in label_names}
																						lowerCamelCase__ ={key: val for key, val in prepared_for_class.items() if key not in label_names}
																						self.assertGreater(len(_lowerCamelCase				)     ,			0				)

																						# Make sure it works with XLA!
																						model.compile(optimizer=tf.keras.optimizers.SGD(0.0				)     ,			jit_compile=_lowerCamelCase				)
																						# Make sure the model fits without crashing regardless of where we pass the labels
																						lowerCamelCase__ =model.fit(
																						    _lowerCamelCase     ,			validation_data=_lowerCamelCase     ,			steps_per_epoch=1     ,			validation_steps=1     ,			shuffle=_lowerCamelCase     ,			verbose=0     ,			)
																						lowerCamelCase__ =history.history["loss"][0]
																						self.assertTrue(not isnan(_lowerCamelCase				)				)
																						lowerCamelCase__ =history.history["val_loss"][0]
																						self.assertTrue(not isnan(_lowerCamelCase				)				)

																						# Now test it with separate labels, to make sure that path works in XLA too.
																						lowerCamelCase__ =model_class(_lowerCamelCase				)
																						model.compile(optimizer=tf.keras.optimizers.SGD(0.0				)     ,			jit_compile=_lowerCamelCase				)
																						lowerCamelCase__ =model.fit(
																						    _lowerCamelCase     ,			_lowerCamelCase     ,			validation_data=(inputs_minus_labels, labels)     ,			steps_per_epoch=1     ,			validation_steps=1     ,			shuffle=_lowerCamelCase     ,			verbose=0     ,			)

																						lowerCamelCase__ =history.history["loss"][0]
																						self.assertTrue(not isnan(_lowerCamelCase				)				)
																						lowerCamelCase__ =history.history["val_loss"][0]
																						self.assertTrue(not isnan(_lowerCamelCase				)				)

				@slow
				def 		_a     (				self				):
										lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase__ =True
										lowerCamelCase__ =True

										if hasattr(_lowerCamelCase     ,			"use_cache"				):
																lowerCamelCase__ =True

										lowerCamelCase__ =getattr(self.model_tester     ,			"encoder_seq_length"     ,			self.model_tester.seq_length				)
										lowerCamelCase__ =getattr(self.model_tester     ,			"key_length"     ,			_lowerCamelCase				)

										for model_class in self.all_model_classes[:2]:
																lowerCamelCase__ =self._prepare_for_class(_lowerCamelCase     ,			_lowerCamelCase				)
																lowerCamelCase__ =model_class(_lowerCamelCase				)
																model.build()
																lowerCamelCase__ =len(model(_lowerCamelCase				)				)

																for key in list(class_inputs_dict.keys()				):
																						# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
																						if key not in model.input_signature:
																												del class_inputs_dict[key]
																						# Check it's a tensor, in case the inputs dict has some bools in it too
																						elif isinstance(class_inputs_dict[key]     ,			tf.Tensor				) and class_inputs_dict[key].dtype.is_integer:
																												lowerCamelCase__ =tf.cast(class_inputs_dict[key]     ,			tf.intaa				)

																if set(class_inputs_dict.keys()				) != set(model.input_signature.keys()				):
																						continue  # Some models have inputs that the preparation functions don't create, we skip those

																with tempfile.TemporaryDirectory() as tmpdirname:
																						model.save_pretrained(_lowerCamelCase     ,			saved_model=_lowerCamelCase				)
																						lowerCamelCase__ =os.path.join(_lowerCamelCase     ,			"saved_model"     ,			"1"				)
																						lowerCamelCase__ =tf.keras.models.load_model(_lowerCamelCase				)
																						lowerCamelCase__ =model(_lowerCamelCase				)

																						if self.is_encoder_decoder:
																												lowerCamelCase__ =outputs["encoder_hidden_states"]
																												lowerCamelCase__ =outputs["encoder_attentions"]
																						else:
																												lowerCamelCase__ =outputs["hidden_states"]
																												lowerCamelCase__ =outputs["attentions"]

																						self.assertEqual(len(_lowerCamelCase				)     ,			_lowerCamelCase				)

																						lowerCamelCase__ =getattr(
																						    self.model_tester     ,			"expected_num_hidden_layers"     ,			self.model_tester.num_hidden_layers + 1				)

																						self.assertEqual(len(_lowerCamelCase				)     ,			_lowerCamelCase				)
																						self.assertListEqual(
																						    list(output_hidden_states[0].shape[-2:]				)     ,			[self.model_tester.seq_length, self.model_tester.hidden_size]     ,			)

																						self.assertEqual(len(_lowerCamelCase				)     ,			self.model_tester.num_hidden_layers				)
																						self.assertListEqual(
																						    list(output_attentions[0].shape[-3:]				)     ,			[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]     ,			)

				@slow
				def 		_a     (				self				):
										tf.keras.mixed_precision.set_global_policy("mixed_float16"				)

										# try/finally block to ensure subsequent tests run in float32
										try:
																lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
																for model_class in self.all_model_classes[:2]:
																						lowerCamelCase__ =self._prepare_for_class(_lowerCamelCase     ,			_lowerCamelCase				)
																						lowerCamelCase__ =model_class(_lowerCamelCase				)
																						lowerCamelCase__ =model(_lowerCamelCase				)

																						self.assertIsNotNone(_lowerCamelCase				)
										finally:
																tf.keras.mixed_precision.set_global_policy("float32"				)

				@slow
				def 		_a     (				self				):
										lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
										# head_mask and decoder_head_mask has different shapes than other input args
										if "head_mask" in inputs_dict:
																del inputs_dict["head_mask"]
										if "decoder_head_mask" in inputs_dict:
																del inputs_dict["decoder_head_mask"]
										if "cross_attn_head_mask" in inputs_dict:
																del inputs_dict["cross_attn_head_mask"]
										lowerCamelCase__ ={
										    module_member
										    for model_class in self.all_model_classes
										    for module in (import_module(model_class.__module__				),)
										    for module_member_name in dir(_lowerCamelCase				)
										    if module_member_name.endswith("MainLayer"				)
										    for module_member in (getattr(_lowerCamelCase     ,			_lowerCamelCase				),)
										    if isinstance(_lowerCamelCase     ,			_lowerCamelCase				)
										    and tf.keras.layers.Layer in module_member.__bases__
										    and getattr(_lowerCamelCase     ,			"_keras_serializable"     ,			_lowerCamelCase				)
										}

										for main_layer_class in tf_main_layer_classes:
																# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
																if "T5" in main_layer_class.__name__:
																						# Take the same values than in TFT5ModelTester for this shared layer
																						lowerCamelCase__ =TFSharedEmbeddings(self.model_tester.vocab_size     ,			self.model_tester.hidden_size     ,			name="shared"				)
																						lowerCamelCase__ =False
																						lowerCamelCase__ =main_layer_class(_lowerCamelCase     ,			embed_tokens=_lowerCamelCase				)
																else:
																						lowerCamelCase__ =main_layer_class(_lowerCamelCase				)

																lowerCamelCase__ ={
																    name: tf.keras.Input(tensor.shape[1:]     ,			dtype=tensor.dtype				) for name, tensor in inputs_dict.items()
																}

																if hasattr(self.model_tester     ,			"num_labels"				):
																						lowerCamelCase__ =self.model_tester.num_labels
																else:
																						lowerCamelCase__ =2

																lowerCamelCase__ =tf.data.Dataset.from_tensor_slices(
																    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)				))				).batch(1				)

																lowerCamelCase__ =main_layer(_lowerCamelCase				)[0]
																lowerCamelCase__ =tf.keras.layers.Dense(_lowerCamelCase     ,			activation="softmax"     ,			name="outputs"				)(_lowerCamelCase				)
																lowerCamelCase__ =tf.keras.models.Model(inputs=_lowerCamelCase     ,			outputs=[outputs]				)

																model.compile(loss="binary_crossentropy"     ,			optimizer="adam"     ,			metrics=["binary_accuracy"]				)
																model.fit(_lowerCamelCase     ,			epochs=1				)

																with tempfile.TemporaryDirectory() as tmpdirname:
																						lowerCamelCase__ =os.path.join(_lowerCamelCase     ,			"keras_model.h5"				)
																						model.save(_lowerCamelCase				)
																						if "T5" in main_layer_class.__name__:
																												lowerCamelCase__ =tf.keras.models.load_model(
																												    _lowerCamelCase     ,			custom_objects={
																												        main_layer_class.__name__: main_layer_class,
																												        "TFSharedEmbeddings": TFSharedEmbeddings,
																												    }     ,			)
																						else:
																												lowerCamelCase__ =tf.keras.models.load_model(
																												    _lowerCamelCase     ,			custom_objects={main_layer_class.__name__: main_layer_class}				)
																						assert isinstance(_lowerCamelCase     ,			tf.keras.Model				)
																						model(_lowerCamelCase				)

				@slow
				def 		_a     (				self				):
										lowerCamelCase__ ,				lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes[:2]:
																lowerCamelCase__ =model_class(_lowerCamelCase				)

																lowerCamelCase__ =copy.deepcopy(_lowerCamelCase				)

																if not self.is_encoder_decoder:
																						lowerCamelCase__ =inputs["input_ids"]
																						del inputs["input_ids"]
																else:
																						lowerCamelCase__ =inputs["input_ids"]
																						lowerCamelCase__ =inputs.get("decoder_input_ids"     ,			_lowerCamelCase				)
																						del inputs["input_ids"]
																						inputs.pop("decoder_input_ids"     ,			_lowerCamelCase				)

																if not self.is_encoder_decoder:
																						lowerCamelCase__ =model.get_input_embeddings()(_lowerCamelCase				)
																else:
																						lowerCamelCase__ =model.get_input_embeddings()(_lowerCamelCase				)
																						lowerCamelCase__ =model.get_input_embeddings()(_lowerCamelCase				)

																lowerCamelCase__ =self._prepare_for_class(_lowerCamelCase     ,			_lowerCamelCase				)

																@tf.function
																def run_in_graph_mode():
																						return model(_lowerCamelCase				)

																lowerCamelCase__ =run_in_graph_mode()
																self.assertIsNotNone(_lowerCamelCase				)

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										lowerCamelCase__ =[]
										if model.config.bos_token_id is not None:
																special_tokens.append(model.config.bos_token_id				)
										if model.config.pad_token_id is not None:
																special_tokens.append(model.config.pad_token_id				)
										if model.config.eos_token_id is not None:
																special_tokens.append(model.config.eos_token_id				)

										# create random bad tokens that are not special tokens
										lowerCamelCase__ =[]
										while len(_lowerCamelCase				) < num_bad_tokens:
																lowerCamelCase__ =tf.squeeze(ids_tensor((1, 1)     ,			self.model_tester.vocab_size				)     ,			0				).numpy()[0]
																if token not in special_tokens:
																						bad_tokens.append(_lowerCamelCase				)
										return bad_tokens

				def 		_a     (				self     ,			_lowerCamelCase				):
										for token_id in output_ids[0].numpy().tolist():
																self.assertGreaterEqual(_lowerCamelCase     ,			0				)
																self.assertLess(_lowerCamelCase     ,			self.model_tester.vocab_size				)






				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										# for all bad word tokens
										for bad_word_ids in bad_words_ids:
																# for all slices in batch
																for generated_ids_slice in generated_ids:
																						# for all word idx
																						for i in range(len(_lowerCamelCase				)     ,			len(_lowerCamelCase				)				):
																												# if tokens match
																												if generated_ids_slice[i - len(_lowerCamelCase				) : i] == bad_word_ids:
																																		return True
										return False



"""simple docstring"""


from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

a							={
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

a							=datasets.load_dataset('xnli', split='test+validation')

a							=0
a							=0
a							=0
a							=0





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						if spm_diff == list(reversed(__lowerCAmelCase    )    ):
												# AAA -> AA+A vs A+AA case.
												return True
						elif len(__lowerCAmelCase    ) == len(__lowerCAmelCase    ) and fast.decode(__lowerCAmelCase    ) == fast.decode(__lowerCAmelCase    ):
												# Second order OK
												# Barrich -> Barr + ich vs Bar + rich
												return True
						lowerCamelCase__ =slow.encode(slow.decode(__lowerCAmelCase    )    )
						lowerCamelCase__ =fast.encode(fast.decode(__lowerCAmelCase    )    )
						if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
												# Type 3 error.
												# Snehagatha ->
												#       Sne, h, aga, th, a
												#       Sne, ha, gat, ha
												# Encoding the wrong with sp does not even recover what spm gave us
												# It fits tokenizer however...
												return True
						return False





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =fast.encode_plus(__lowerCAmelCase    )[0]
						lowerCamelCase__ =enc.offsets
						lowerCamelCase__ ,				lowerCamelCase__ =offsets[idx], offsets[idx - 1]
						if curr is not None and line[curr[0] : curr[1]] == "\u200f":
												return True
						if prev is not None and line[prev[0] : prev[1]] == "\u200f":
												return True





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Tuple:




						'''simple docstring'''



						for i, (spm_id, tok_id) in enumerate(zip(__lowerCAmelCase       ,   __lowerCAmelCase    )    ):
												if spm_id != tok_id:
																		break
						lowerCamelCase__ =i
						for i, (spm_id, tok_id) in enumerate(zip(reversed(__lowerCAmelCase    )       ,   reversed(__lowerCAmelCase    )    )    ):
												if spm_id != tok_id:
																		break
						lowerCamelCase__ =len(__lowerCAmelCase    ) - i

						lowerCamelCase__ =spm_ids[first:last]
						lowerCamelCase__ =tok_ids[first:last]

						if check_diff(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
												return True

						if check_LTR_mark(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
												return True

						if last - first > 5:
												# We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
												lowerCamelCase__ =Counter(spm_ids[first:last]    )
												lowerCamelCase__ =Counter(tok_ids[first:last]    )

												lowerCamelCase__ ={spm_ for (spm_, si) in spms.items() if toks.get(spm_       ,   0    ) == si}
												lowerCamelCase__ =3
												for i in range(last - first - min_width    ):
																		if all(spm_ids[first + i + j] in removable_tokens for j in range(__lowerCAmelCase    )    ):
																								lowerCamelCase__ =[
																								    k
																								    for k in range(last - first - min_width    )
																								    if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
																								]
																								for j in possible_matches:
																														if check_diff(spm_ids[first : first + i]       ,   tok_ids[first : first + j]       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ) and check_details(
																														    __lowerCAmelCase       ,   spm_ids[first + i : last]       ,   tok_ids[first + j : last]       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   ):
																																				return True

						print(F'''Spm: {[fast.decode([spm_ids[i]]    ) for i in range(__lowerCAmelCase       ,   __lowerCAmelCase    )]}'''    )
						try:
												print(F'''Tok: {[fast.decode([tok_ids[i]]    ) for i in range(__lowerCAmelCase       ,   __lowerCAmelCase    )]}'''    )
						except Exception:
												pass

						lowerCamelCase__ =fast.decode(spm_ids[:first]    )
						lowerCamelCase__ =fast.decode(spm_ids[last:]    )
						lowerCamelCase__ =fast.decode(spm_ids[first:last]    )
						print()
						print(__lowerCAmelCase    )
						return False





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						global perfect
						global imperfect
						global wrong
						global total

						lowerCamelCase__ =slow.encode(__lowerCAmelCase    )
						lowerCamelCase__ =fast.encode(__lowerCAmelCase    )

						lowerCamelCase__ =False
						total += 1

						if slow_ids != fast_ids:
												if check_details(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
																		lowerCamelCase__ =True
																		imperfect += 1
												else:
																		wrong += 1
						else:
												perfect += 1

						if total % 1_0000 == 0:
												print(F'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''    )

						if skip_assert:
												return

						assert (
						    slow_ids == fast_ids
						), F'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__lowerCAmelCase    )}\n{fast.tokenize(__lowerCAmelCase    )}'''





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						global batch_total
						for i in range(len(__lowerCAmelCase    )    ):
												# premise, all languages
												for text in dataset[i]["premise"].values():
																		test_string(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )

												# hypothesis, all languages
												for text in dataset[i]["hypothesis"]["translation"]:
																		test_string(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )


if __name__ == "__main__":
	for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
		a							=list(slow_class.max_model_input_sizes.keys())
		for checkpoint in checkpoint_names:
			a							=0
			a							=0
			a							=0
			a							=0

			print(F'''========================== Checking {name}: {checkpoint} ==========================''')
			a							=slow_class.from_pretrained(checkpoint, force_download=True)
			a							=fast_class.from_pretrained(checkpoint, force_download=True)
			test_tokenizer(slow, fast)
			print(F'''Accuracy {perfect * 100 / total:.2f}''')


