



'''simple docstring'''

import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						List[str]							)      ->						Union[str, Any]:







	"""simple docstring"""
	if isinstance(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     np.full((len(_SCREAMING_SNAKE_CASE							), sequence_length, 2)							,		_SCREAMING_SNAKE_CASE							)
	else:
		lowerCAmelCase   =     np.full((len(_SCREAMING_SNAKE_CASE							), sequence_length)							,		_SCREAMING_SNAKE_CASE							)

	for i, tensor in enumerate(_SCREAMING_SNAKE_CASE							):
		if padding_side == "right":
			if isinstance(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							):
				lowerCAmelCase   =     tensor[:sequence_length]
			else:
				lowerCAmelCase   =     tensor[:sequence_length]
		else:
			if isinstance(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							):
				lowerCAmelCase   =     tensor[:sequence_length]
			else:
				lowerCAmelCase   =     tensor[:sequence_length]

	return out_tensor.tolist()






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							)      ->						str:







	"""simple docstring"""
	lowerCAmelCase   =     ord(_SCREAMING_SNAKE_CASE							)
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
		return True
	lowerCAmelCase   =     unicodedata.category(_SCREAMING_SNAKE_CASE							)
	if cat.startswith("""P"""							):
		return True
	return False





@dataclass
class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   PreTrainedTokenizerBase
						UpperCAmelCase							:   Union[bool, str, PaddingStrategy]									=		True
						UpperCAmelCase							:   Optional[int]									=		None
						UpperCAmelCase							:   Optional[int]									=		None
						UpperCAmelCase							:   int									=		-1_00
						UpperCAmelCase							:   str									=		"pt"


						def     __snake_case   ( self     ,	A_				)     ->     Optional[int]:
							import torch

							lowerCAmelCase   =     """label""" if """label""" in features[0].keys() else """labels"""
							lowerCAmelCase   =     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
							lowerCAmelCase   =     self.tokenizer.pad(
							    A_     ,	padding=self.padding     ,	max_length=self.max_length     ,	pad_to_multiple_of=self.pad_to_multiple_of     ,	return_tensors="""pt""" if labels is None else None     ,	)

							if labels is None:
								return batch

							lowerCAmelCase   =     torch.tensor(batch["""entity_ids"""]				).shape[1]
							lowerCAmelCase   =     self.tokenizer.padding_side
							if padding_side == "right":
								lowerCAmelCase   =     [
								    list(A_				) + [self.label_pad_token_id] * (sequence_length - len(A_				)) for label in labels
								]
							else:
								lowerCAmelCase   =     [
								    [self.label_pad_token_id] * (sequence_length - len(A_				)) + list(A_				) for label in labels
								]

							lowerCAmelCase   =     [feature["""ner_tags"""] for feature in features]
							lowerCAmelCase   =     padding_tensor(A_     ,	-1     ,	A_     ,	A_				)
							lowerCAmelCase   =     [feature["""original_entity_spans"""] for feature in features]
							lowerCAmelCase   =     padding_tensor(A_     ,	(-1, -1)     ,	A_     ,	A_				)
							lowerCAmelCase   =     {k: torch.tensor(A_     ,	dtype=torch.intaa				) for k, v in batch.items()}

							return batch




'''simple docstring'''

from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TFConvBertForMaskedLM,
				    TFConvBertForMultipleChoice,
				    TFConvBertForQuestionAnswering,
				    TFConvBertForSequenceClassification,
				    TFConvBertForTokenClassification,
				    TFConvBertModel,
				)

class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=13     ,	A_=7     ,	A_=True     ,	A_=True     ,	A_=True     ,	A_=True     ,	A_=99     ,	A_=32     ,	A_=2     ,	A_=4     ,	A_=37     ,	A_="gelu"     ,	A_=0.1     ,	A_=0.1     ,	A_=512     ,	A_=16     ,	A_=2     ,	A_=0.0_2     ,	A_=3     ,	A_=4     ,	A_=None     ,	)     ->     Dict:
							lowerCAmelCase   =     parent
							lowerCAmelCase   =     13
							lowerCAmelCase   =     7
							lowerCAmelCase   =     True
							lowerCAmelCase   =     True
							lowerCAmelCase   =     True
							lowerCAmelCase   =     True
							lowerCAmelCase   =     99
							lowerCAmelCase   =     384
							lowerCAmelCase   =     2
							lowerCAmelCase   =     4
							lowerCAmelCase   =     37
							lowerCAmelCase   =     """gelu"""
							lowerCAmelCase   =     0.1
							lowerCAmelCase   =     0.1
							lowerCAmelCase   =     512
							lowerCAmelCase   =     16
							lowerCAmelCase   =     2
							lowerCAmelCase   =     0.0_2
							lowerCAmelCase   =     3
							lowerCAmelCase   =     4
							lowerCAmelCase   =     128
							lowerCAmelCase   =     2
							lowerCAmelCase   =     9
							lowerCAmelCase   =     1
							lowerCAmelCase   =     None


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size				)

							lowerCAmelCase   =     None
							if self.use_input_mask:
								lowerCAmelCase   =     random_attention_mask([self.batch_size, self.seq_length]				)

							lowerCAmelCase   =     None
							if self.use_token_type_ids:
								lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length]     ,	self.type_vocab_size				)

							lowerCAmelCase   =     None
							lowerCAmelCase   =     None
							lowerCAmelCase   =     None
							if self.use_labels:
								lowerCAmelCase   =     ids_tensor([self.batch_size]     ,	self.type_sequence_label_size				)
								lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length]     ,	self.num_labels				)
								lowerCAmelCase   =     ids_tensor([self.batch_size]     ,	self.num_choices				)

							lowerCAmelCase   =     ConvBertConfig(
							    vocab_size=self.vocab_size     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	max_position_embeddings=self.max_position_embeddings     ,	type_vocab_size=self.type_vocab_size     ,	initializer_range=self.initializer_range     ,	return_dict=A_     ,	)

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     int:
							lowerCAmelCase   =     TFConvBertModel(config=A_				)
							lowerCAmelCase   =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

							lowerCAmelCase   =     [input_ids, input_mask]
							lowerCAmelCase   =     model(A_				)

							lowerCAmelCase   =     model(A_				)

							self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     List[Any]:
							lowerCAmelCase   =     TFConvBertForMaskedLM(config=A_				)
							lowerCAmelCase   =     {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowerCAmelCase   =     model(A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.vocab_size)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Optional[int]:
							lowerCAmelCase   =     self.num_labels
							lowerCAmelCase   =     TFConvBertForSequenceClassification(config=A_				)
							lowerCAmelCase   =     {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}

							lowerCAmelCase   =     model(A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_labels)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Any:
							lowerCAmelCase   =     self.num_choices
							lowerCAmelCase   =     TFConvBertForMultipleChoice(config=A_				)
							lowerCAmelCase   =     tf.tile(tf.expand_dims(A_     ,	1				)     ,	(1, self.num_choices, 1)				)
							lowerCAmelCase   =     tf.tile(tf.expand_dims(A_     ,	1				)     ,	(1, self.num_choices, 1)				)
							lowerCAmelCase   =     tf.tile(tf.expand_dims(A_     ,	1				)     ,	(1, self.num_choices, 1)				)
							lowerCAmelCase   =     {
							    """input_ids""": multiple_choice_inputs_ids,
							    """attention_mask""": multiple_choice_input_mask,
							    """token_type_ids""": multiple_choice_token_type_ids,
							}
							lowerCAmelCase   =     model(A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_choices)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.num_labels
							lowerCAmelCase   =     TFConvBertForTokenClassification(config=A_				)
							lowerCAmelCase   =     {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowerCAmelCase   =     model(A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.num_labels)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Optional[int]:
							lowerCAmelCase   =     TFConvBertForQuestionAnswering(config=A_				)
							lowerCAmelCase   =     {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}

							lowerCAmelCase   =     model(A_				)
							self.parent.assertEqual(result.start_logits.shape     ,	(self.batch_size, self.seq_length)				)
							self.parent.assertEqual(result.end_logits.shape     ,	(self.batch_size, self.seq_length)				)




						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase   =     self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       
							)   =     config_and_inputs
							lowerCAmelCase   =     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
							return config, inputs_dict







@require_tf
class __snake_case(				_lowerCAmelCase		,							_lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Optional[int]									=		(
						    (
						        TFConvBertModel,
						        TFConvBertForMaskedLM,
						        TFConvBertForQuestionAnswering,
						        TFConvBertForSequenceClassification,
						        TFConvBertForTokenClassification,
						        TFConvBertForMultipleChoice,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCAmelCase							:   Union[str, Any]									=		(
						    {
						        "feature-extraction": TFConvBertModel,
						        "fill-mask": TFConvBertForMaskedLM,
						        "question-answering": TFConvBertForQuestionAnswering,
						        "text-classification": TFConvBertForSequenceClassification,
						        "token-classification": TFConvBertForTokenClassification,
						        "zero-shot": TFConvBertForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						UpperCAmelCase							:   Union[str, Any]									=		False
						UpperCAmelCase							:   Optional[int]									=		False
						UpperCAmelCase							:   Dict									=		False


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     TFConvBertModelTester(self				)
							lowerCAmelCase   =     ConfigTester(self     ,	config_class=A_     ,	hidden_size=37				)


						def     __snake_case   ( self				)     ->     Tuple:
							self.config_tester.run_common_tests()


						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_				)


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*A_				)


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*A_				)


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*A_				)


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*A_				)


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*A_				)


						@slow
						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase   =     True
							lowerCAmelCase   =     True

							if hasattr(A_     ,	"""use_cache"""				):
								lowerCAmelCase   =     True

							lowerCAmelCase   =     getattr(self.model_tester     ,	"""encoder_seq_length"""     ,	self.model_tester.seq_length				)
							lowerCAmelCase   =     getattr(self.model_tester     ,	"""key_length"""     ,	A_				)

							for model_class in self.all_model_classes:
								lowerCAmelCase   =     self._prepare_for_class(A_     ,	A_				)
								lowerCAmelCase   =     model_class(A_				)
								lowerCAmelCase   =     len(model(A_				)				)

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

									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(A_				)     ,	A_				)

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

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

									self.assertEqual(len(A_				)     ,	self.model_tester.num_hidden_layers				)
									self.assertListEqual(
									    list(output_attentions[0].shape[-3:]				)     ,	[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]     ,	)


						@slow
						def     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""				)
							self.assertIsNotNone(A_				)




						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase   =     True
							lowerCAmelCase   =     getattr(self.model_tester     ,	"""decoder_seq_length"""     ,	self.model_tester.seq_length				)
							lowerCAmelCase   =     getattr(self.model_tester     ,	"""encoder_seq_length"""     ,	self.model_tester.seq_length				)
							lowerCAmelCase   =     getattr(self.model_tester     ,	"""key_length"""     ,	A_				)
							lowerCAmelCase   =     getattr(self.model_tester     ,	"""key_length"""     ,	A_				)

							def check_decoder_attentions_output(A_				):
								lowerCAmelCase   =     len(A_				)
								self.assertEqual(out_len % 2     ,	0				)
								lowerCAmelCase   =     outputs.decoder_attentions
								self.assertEqual(len(A_				)     ,	self.model_tester.num_hidden_layers				)
								self.assertListEqual(
								    list(decoder_attentions[0].shape[-3:]				)     ,	[self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]     ,	)

							def check_encoder_attentions_output(A_				):
								lowerCAmelCase   =     [
								    t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
								]
								self.assertEqual(len(A_				)     ,	self.model_tester.num_hidden_layers				)
								self.assertListEqual(
								    list(attentions[0].shape[-3:]				)     ,	[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]     ,	)

							for model_class in self.all_model_classes:
								lowerCAmelCase   =     True
								lowerCAmelCase   =     False
								lowerCAmelCase   =     model_class(A_				)
								lowerCAmelCase   =     model(self._prepare_for_class(A_     ,	A_				)				)
								lowerCAmelCase   =     len(A_				)
								self.assertEqual(config.output_hidden_states     ,	A_				)
								check_encoder_attentions_output(A_				)

								if self.is_encoder_decoder:
									lowerCAmelCase   =     model_class(A_				)
									lowerCAmelCase   =     model(self._prepare_for_class(A_     ,	A_				)				)
									self.assertEqual(config.output_hidden_states     ,	A_				)
									check_decoder_attentions_output(A_				)

								# Check that output attentions can also be changed via the config
								del inputs_dict["output_attentions"]
								lowerCAmelCase   =     True
								lowerCAmelCase   =     model_class(A_				)
								lowerCAmelCase   =     model(self._prepare_for_class(A_     ,	A_				)				)
								self.assertEqual(config.output_hidden_states     ,	A_				)
								check_encoder_attentions_output(A_				)

								# Check attention is always last and order is fine
								lowerCAmelCase   =     True
								lowerCAmelCase   =     True
								lowerCAmelCase   =     model_class(A_				)
								lowerCAmelCase   =     model(self._prepare_for_class(A_     ,	A_				)				)

								self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)     ,	len(A_				)				)
								self.assertEqual(model.config.output_hidden_states     ,	A_				)
								check_encoder_attentions_output(A_				)







@require_tf
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase   =     TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""				)
							lowerCAmelCase   =     tf.constant([[0, 1, 2, 3, 4, 5]]				)
							lowerCAmelCase   =     model(A_				)[0]

							lowerCAmelCase   =     [1, 6, 768]
							self.assertEqual(output.shape     ,	A_				)

							lowerCAmelCase   =     tf.constant(
							    [
							        [
							            [-0.0_3_4_7_5_4_9_3, -0.4_6_8_6_0_3_4, -0.3_0_6_3_8_8_3_2],
							            [0.2_2_6_3_7_2_4_8, -0.2_6_9_8_8_6_4_6, -0.7_4_2_3_4_2_4],
							            [0.1_0_3_2_4_8_6_8, -0.4_5_0_1_3_5_0_8, -0.5_8_2_8_0_7_8_4],
							        ]
							    ]				)
							tf.debugging.assert_near(output[:, :3, :3]     ,	A_     ,	atol=1e-4				)