



from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, 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.models.layoutlm.modeling_tf_layoutlm import (
				    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMForMaskedLM,
				    TFLayoutLMForQuestionAnswering,
				    TFLayoutLMForSequenceClassification,
				    TFLayoutLMForTokenClassification,
				    TFLayoutLMModel,
				)






class     __a			:



	def __init__(    self				:     Optional[int]					,						snake_case_				:     Dict					,						snake_case_				:     List[str]=13					,						snake_case_				:     Dict=7					,						snake_case_				:     Tuple=True					,						snake_case_				:     Dict=True					,						snake_case_				:     int=True					,						snake_case_				:     List[Any]=True					,						snake_case_				:     Optional[Any]=99					,						snake_case_				:     Tuple=32					,						snake_case_				:     List[str]=2					,						snake_case_				:     int=4					,						snake_case_				:     int=37					,						snake_case_				:     int="gelu"					,						snake_case_				:     Optional[Any]=0.1					,						snake_case_				:     str=0.1					,						snake_case_				:     List[Any]=5_12					,						snake_case_				:     int=16					,						snake_case_				:     Union[str, Any]=2					,						snake_case_				:     Optional[Any]=0.0_2					,						snake_case_				:     Optional[int]=3					,						snake_case_				:     Optional[int]=4					,						snake_case_				:     int=None					,						snake_case_				:     Any=10_00					,						)->				Tuple:
						__lowerCAmelCase						 =parent
						__lowerCAmelCase						 =batch_size
						__lowerCAmelCase						 =seq_length
						__lowerCAmelCase						 =is_training
						__lowerCAmelCase						 =use_input_mask
						__lowerCAmelCase						 =use_token_type_ids
						__lowerCAmelCase						 =use_labels
						__lowerCAmelCase						 =vocab_size
						__lowerCAmelCase						 =hidden_size
						__lowerCAmelCase						 =num_hidden_layers
						__lowerCAmelCase						 =num_attention_heads
						__lowerCAmelCase						 =intermediate_size
						__lowerCAmelCase						 =hidden_act
						__lowerCAmelCase						 =hidden_dropout_prob
						__lowerCAmelCase						 =attention_probs_dropout_prob
						__lowerCAmelCase						 =max_position_embeddings
						__lowerCAmelCase						 =type_vocab_size
						__lowerCAmelCase						 =type_sequence_label_size
						__lowerCAmelCase						 =initializer_range
						__lowerCAmelCase						 =num_labels
						__lowerCAmelCase						 =num_choices
						__lowerCAmelCase						 =scope
						__lowerCAmelCase						 =range_bbox



	def 		UpperCamelCase				(    self				:     int)->				List[str]:
						__lowerCAmelCase						 =ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size)

						# convert bbox to numpy since TF does not support item assignment
						__lowerCAmelCase						 =ids_tensor([self.batch_size, self.seq_length, 4]					,						self.range_bbox).numpy()
						# Ensure that bbox is legal
						for i in range(bbox.shape[0]):
											for j in range(bbox.shape[1]):
																if bbox[i, j, 3] < bbox[i, j, 1]:
																					__lowerCAmelCase						 =bbox[i, j, 3]
																					__lowerCAmelCase						 =bbox[i, j, 1]
																					__lowerCAmelCase						 =t
																if bbox[i, j, 2] < bbox[i, j, 0]:
																					__lowerCAmelCase						 =bbox[i, j, 2]
																					__lowerCAmelCase						 =bbox[i, j, 0]
																					__lowerCAmelCase						 =t
						__lowerCAmelCase						 =tf.convert_to_tensor(snake_case_)

						__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						 =LayoutLMConfig(
						    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 config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



	def 		UpperCamelCase				(    self				:     int					,						snake_case_				:     Union[str, Any]					,						snake_case_				:     Optional[int]					,						snake_case_				:     List[Any]					,						snake_case_				:     Tuple					,						snake_case_				:     List[Any]					,						snake_case_				:     int					,						snake_case_				:     Tuple					,						snake_case_				:     Dict)->				str:
						__lowerCAmelCase						 =TFLayoutLMModel(config=snake_case_)

						__lowerCAmelCase						 =model(snake_case_					,						snake_case_					,						attention_mask=snake_case_					,						token_type_ids=snake_case_)
						__lowerCAmelCase						 =model(snake_case_					,						snake_case_					,						token_type_ids=snake_case_)
						__lowerCAmelCase						 =model(snake_case_					,						snake_case_)
						self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size))
						self.parent.assertEqual(result.pooler_output.shape					,						(self.batch_size, self.hidden_size))



	def 		UpperCamelCase				(    self				:     List[Any]					,						snake_case_				:     int					,						snake_case_				:     str					,						snake_case_				:     str					,						snake_case_				:     Union[str, Any]					,						snake_case_				:     Any					,						snake_case_				:     Union[str, Any]					,						snake_case_				:     str					,						snake_case_				:     Tuple)->				Optional[int]:
						__lowerCAmelCase						 =TFLayoutLMForMaskedLM(config=snake_case_)

						__lowerCAmelCase						 =model(snake_case_					,						snake_case_					,						attention_mask=snake_case_					,						token_type_ids=snake_case_					,						labels=snake_case_)
						self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size))



	def 		UpperCamelCase				(    self				:     List[str]					,						snake_case_				:     Any					,						snake_case_				:     int					,						snake_case_				:     Any					,						snake_case_				:     str					,						snake_case_				:     Dict					,						snake_case_				:     int					,						snake_case_				:     Dict					,						snake_case_				:     Union[str, Any])->				List[str]:
						__lowerCAmelCase						 =self.num_labels
						__lowerCAmelCase						 =TFLayoutLMForSequenceClassification(config=snake_case_)

						__lowerCAmelCase						 =model(snake_case_					,						snake_case_					,						attention_mask=snake_case_					,						token_type_ids=snake_case_)
						self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_labels))



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     Dict					,						snake_case_				:     List[Any]					,						snake_case_				:     Any					,						snake_case_				:     Optional[int]					,						snake_case_				:     Dict					,						snake_case_				:     List[str]					,						snake_case_				:     Tuple					,						snake_case_				:     List[Any])->				Any:
						__lowerCAmelCase						 =self.num_labels
						__lowerCAmelCase						 =TFLayoutLMForTokenClassification(config=snake_case_)

						__lowerCAmelCase						 =model(snake_case_					,						snake_case_					,						attention_mask=snake_case_					,						token_type_ids=snake_case_					,						labels=snake_case_)
						self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.num_labels))



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     int					,						snake_case_				:     Union[str, Any]					,						snake_case_				:     int					,						snake_case_				:     int					,						snake_case_				:     int					,						snake_case_				:     Tuple					,						snake_case_				:     Dict					,						snake_case_				:     List[str])->				str:
						__lowerCAmelCase						 =TFLayoutLMForQuestionAnswering(config=snake_case_)

						__lowerCAmelCase						 =model(snake_case_					,						snake_case_					,						attention_mask=snake_case_					,						token_type_ids=snake_case_)
						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 		UpperCamelCase				(    self				:     Optional[Any])->				Dict:
						__lowerCAmelCase						 =self.prepare_config_and_inputs()
						(
						    (
						    __lowerCAmelCase
						) ,						(
						    __lowerCAmelCase
						) ,						(
						    __lowerCAmelCase
						) ,						(
						    __lowerCAmelCase
						) ,						(
						    __lowerCAmelCase
						) ,						(
						    __lowerCAmelCase
						) ,						(
						    __lowerCAmelCase
						) ,						(
						    __lowerCAmelCase
						) ,						
						)						 =config_and_inputs
						__lowerCAmelCase						 ={
						    """input_ids""": input_ids,
						    """bbox""": bbox,
						    """token_type_ids""": token_type_ids,
						    """attention_mask""": input_mask,
						}
						return config, inputs_dict






@require_tf
class     __a			(  SCREAMING_SNAKE_CASE						,     SCREAMING_SNAKE_CASE						,     unittest.TestCase					):
	SCREAMING_SNAKE_CASE  	=							(
	    (
	        TFLayoutLMModel,
	        TFLayoutLMForMaskedLM,
	        TFLayoutLMForTokenClassification,
	        TFLayoutLMForSequenceClassification,
	        TFLayoutLMForQuestionAnswering,
	    )
	    if is_tf_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE  	=							(
	    {
	        "feature-extraction": TFLayoutLMModel,
	        "fill-mask": TFLayoutLMForMaskedLM,
	        "text-classification": TFLayoutLMForSequenceClassification,
	        "token-classification": TFLayoutLMForTokenClassification,
	        "zero-shot": TFLayoutLMForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							True
	SCREAMING_SNAKE_CASE  	=							10



	def 		UpperCamelCase				(    self				:     List[Any])->				Tuple:
						__lowerCAmelCase						 =TFLayoutLMModelTester(self)
						__lowerCAmelCase						 =ConfigTester(self					,						config_class=snake_case_					,						hidden_size=37)



	def 		UpperCamelCase				(    self				:     Tuple)->				List[Any]:
						self.config_tester.run_common_tests()



	def 		UpperCamelCase				(    self				:     int)->				List[str]:
						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case_)



	def 		UpperCamelCase				(    self				:     Tuple)->				Tuple:
						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*snake_case_)



	def 		UpperCamelCase				(    self				:     Any)->				Optional[Any]:
						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*snake_case_)



	def 		UpperCamelCase				(    self				:     Tuple)->				Dict:
						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*snake_case_)



	def 		UpperCamelCase				(    self				:     Tuple)->				Optional[Any]:
						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*snake_case_)



	@slow
	def 		UpperCamelCase				(    self				:     Optional[Any])->				str:
						for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCAmelCase						 =TFLayoutLMModel.from_pretrained(snake_case_)
											self.assertIsNotNone(snake_case_)



	@unittest.skip("""Onnx compliancy broke with TF 2.10""")
	def 		UpperCamelCase				(    self				:     Optional[Any])->				Union[str, Any]:
						pass






def        __lowerCAmelCase		(	)	->    int:
					# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
					# fmt: off
					__lowerCAmelCase						 =tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]						)  # noqa: E231
					__lowerCAmelCase						 =tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]						)  # noqa: E231
					__lowerCAmelCase						 =tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]						)  # noqa: E231
					__lowerCAmelCase						 =tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]						)  # noqa: E231
					# these are sequence labels (i.e. at the token level)
					__lowerCAmelCase						 =tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]						)  # noqa: E231
					# fmt: on

					return input_ids, attention_mask, bbox, token_type_ids, labels






@require_tf
class     __a			(  unittest.TestCase					):



	@slow
	def 		UpperCamelCase				(    self				:     Optional[int])->				str:
						__lowerCAmelCase						 =TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased""")

						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =prepare_layoutlm_batch_inputs()

						# forward pass
						__lowerCAmelCase						 =model(input_ids=snake_case_					,						bbox=snake_case_					,						attention_mask=snake_case_					,						token_type_ids=snake_case_)

						# test the sequence output on [0, :3, :3]
						__lowerCAmelCase						 =tf.convert_to_tensor(
						    [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]					,						)

						self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]					,						snake_case_					,						atol=1e-3))

						# test the pooled output on [1, :3]
						__lowerCAmelCase						 =tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2])

						self.assertTrue(np.allclose(outputs.pooler_output[1, :3]					,						snake_case_					,						atol=1e-3))



	@slow
	def 		UpperCamelCase				(    self				:     List[Any])->				List[str]:
						# initialize model with randomly initialized sequence classification head
						__lowerCAmelCase						 =TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""					,						num_labels=2)

						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =prepare_layoutlm_batch_inputs()

						# forward pass
						__lowerCAmelCase						 =model(
						    input_ids=snake_case_					,						bbox=snake_case_					,						attention_mask=snake_case_					,						token_type_ids=snake_case_					,						labels=tf.convert_to_tensor([1, 1])					,						)

						# test whether we get a loss as a scalar
						__lowerCAmelCase						 =outputs.loss
						__lowerCAmelCase						 =(2,)
						self.assertEqual(loss.shape					,						snake_case_)

						# test the shape of the logits
						__lowerCAmelCase						 =outputs.logits
						__lowerCAmelCase						 =(2, 2)
						self.assertEqual(logits.shape					,						snake_case_)



	@slow
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				Union[str, Any]:
						# initialize model with randomly initialized token classification head
						__lowerCAmelCase						 =TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""					,						num_labels=13)

						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =prepare_layoutlm_batch_inputs()

						# forward pass
						__lowerCAmelCase						 =model(
						    input_ids=snake_case_					,						bbox=snake_case_					,						attention_mask=snake_case_					,						token_type_ids=snake_case_					,						labels=snake_case_)

						# test the shape of the logits
						__lowerCAmelCase						 =outputs.logits
						__lowerCAmelCase						 =tf.convert_to_tensor((2, 25, 13))
						self.assertEqual(logits.shape					,						snake_case_)



	@slow
	def 		UpperCamelCase				(    self				:     int)->				Optional[Any]:
						# initialize model with randomly initialized token classification head
						__lowerCAmelCase						 =TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased""")

						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =prepare_layoutlm_batch_inputs()

						# forward pass
						__lowerCAmelCase						 =model(input_ids=snake_case_					,						bbox=snake_case_					,						attention_mask=snake_case_					,						token_type_ids=snake_case_)

						# test the shape of the logits
						__lowerCAmelCase						 =tf.convert_to_tensor((2, 25))
						self.assertEqual(outputs.start_logits.shape					,						snake_case_)
						self.assertEqual(outputs.end_logits.shape					,						snake_case_)









from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase_													=      logging.get_logger(__name__)

lowercase_													=      {
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							"gpt_neo"
	SCREAMING_SNAKE_CASE  	=							["past_key_values"]
	SCREAMING_SNAKE_CASE  	=							{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



	def __init__(    self				:     Optional[int]					,						snake_case_				:     int=5_02_57					,						snake_case_				:     Tuple=20_48					,						snake_case_				:     Optional[int]=20_48					,						snake_case_				:     int=24					,						snake_case_				:     List[Any]=[[["global", "local"], 12]]					,						snake_case_				:     Tuple=16					,						snake_case_				:     Optional[int]=None					,						snake_case_				:     Dict=2_56					,						snake_case_				:     List[str]="gelu_new"					,						snake_case_				:     List[Any]=0.0					,						snake_case_				:     Tuple=0.0					,						snake_case_				:     Tuple=0.0					,						snake_case_				:     str=0.1					,						snake_case_				:     Union[str, Any]=1e-5					,						snake_case_				:     str=0.0_2					,						snake_case_				:     Union[str, Any]=True					,						snake_case_				:     Optional[int]=5_02_56					,						snake_case_				:     Tuple=5_02_56					,						**snake_case_				:     Optional[int]					,						)->				Tuple:
						__lowerCAmelCase						 =vocab_size
						__lowerCAmelCase						 =max_position_embeddings
						__lowerCAmelCase						 =hidden_size
						__lowerCAmelCase						 =num_layers
						__lowerCAmelCase						 =num_heads
						__lowerCAmelCase						 =intermediate_size
						__lowerCAmelCase						 =window_size
						__lowerCAmelCase						 =activation_function
						__lowerCAmelCase						 =resid_dropout
						__lowerCAmelCase						 =embed_dropout
						__lowerCAmelCase						 =attention_dropout
						__lowerCAmelCase						 =classifier_dropout
						__lowerCAmelCase						 =layer_norm_epsilon
						__lowerCAmelCase						 =initializer_range
						__lowerCAmelCase						 =use_cache

						__lowerCAmelCase						 =bos_token_id
						__lowerCAmelCase						 =eos_token_id

						__lowerCAmelCase						 =attention_types
						__lowerCAmelCase						 =self.expand_attention_types_params(snake_case_)

						if len(self.attention_layers) != self.num_layers:
											raise ValueError(
											    """Configuration for convolutional module is incorrect. """
											    """It is required that `len(config.attention_layers)` == `config.num_layers` """
											    F"""but is `len(config.attention_layers) = {len(self.attention_layers)}`, """
											    F"""`config.num_layers = {self.num_layers}`. """
											    """`config.attention_layers` is prepared using `config.attention_types`. """
											    """Please verify the value of `config.attention_types` argument.""")

						super().__init__(bos_token_id=snake_case_					,						eos_token_id=snake_case_					,						**snake_case_)



	@staticmethod
	def 		UpperCamelCase				(    snake_case_				:     Optional[int])->				Any:
						__lowerCAmelCase						 =[]
						for item in attention_types:
											for _ in range(item[1]):
																attentions.extend(item[0])
						return attentions






def        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     int						)	->    int:
					import torch

					__lowerCAmelCase						 =input.size()
					__lowerCAmelCase						 =len(__lowerCamelCase						)
					__lowerCAmelCase						 =shape[dimension]

					__lowerCAmelCase						 =torch.arange(0					, __lowerCamelCase					, __lowerCamelCase						)
					__lowerCAmelCase						 =torch.div(sizedim - size					, __lowerCamelCase					, rounding_mode="""floor"""						) + 1
					__lowerCAmelCase						 =torch.arange(__lowerCamelCase						) + low_indices[:min_length][:, None]

					__lowerCAmelCase						 =[slice(__lowerCamelCase						)] * rank
					__lowerCAmelCase						 =indices
					__lowerCAmelCase						 =input[s]

					__lowerCAmelCase						 =list(range(0					, rank + 1						)						)
					perm.append(perm.pop(dimension + 1						)						)

					return sliced.permute(__lowerCamelCase						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Any						)	->    List[str]:
					import torch

					__lowerCAmelCase						 =torch.arange(1					, __lowerCamelCase						)
					__lowerCAmelCase						 =torch.remainder(__lowerCamelCase					, __lowerCamelCase						)
					__lowerCAmelCase						 =remainders == 0
					__lowerCAmelCase						 =candidates[divisor_indices]
					__lowerCAmelCase						 =torch.max(__lowerCamelCase						)
					return largest_divisor, torch.div(__lowerCamelCase					, __lowerCamelCase					, rounding_mode="""floor"""						)






class     __a			(  SCREAMING_SNAKE_CASE					):



	@property
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				Mapping[str, Mapping[int, str]]:
						__lowerCAmelCase						 =OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}})
						if self.use_past:
											self.fill_with_past_key_values_(snake_case_					,						direction="""inputs""")
											__lowerCAmelCase						 ={0: """batch""", 1: """past_sequence + sequence"""}
						else:
											__lowerCAmelCase						 ={0: """batch""", 1: """sequence"""}

						return common_inputs



	@property
	def 		UpperCamelCase				(    self				:     Optional[int])->				int:
						return self._config.num_heads



	def 		UpperCamelCase				(    self				:     int					,						snake_case_				:     PreTrainedTokenizer					,						snake_case_				:     int = -1					,						snake_case_				:     int = -1					,						snake_case_				:     bool = False					,						snake_case_				:     Optional[TensorType] = None					,						)->				Mapping[str, Any]:
						__lowerCAmelCase						 =super(snake_case_					,						self).generate_dummy_inputs(
						    snake_case_					,						batch_size=snake_case_					,						seq_length=snake_case_					,						is_pair=snake_case_					,						framework=snake_case_)

						# We need to order the input in the way they appears in the forward()
						__lowerCAmelCase						 =OrderedDict({"""input_ids""": common_inputs["""input_ids"""]})

						# Need to add the past_keys
						if self.use_past:
											if not is_torch_available():
																raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""")
											else:
																import torch

																__lowerCAmelCase ,						__lowerCAmelCase						 =common_inputs["""input_ids"""].shape
																# Not using the same length for past_key_values
																__lowerCAmelCase						 =seqlen + 2
																__lowerCAmelCase						 =(
																    batch,
																    self.num_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // self.num_attention_heads,
																)
																__lowerCAmelCase						 =[
																    (torch.zeros(snake_case_), torch.zeros(snake_case_)) for _ in range(self.num_layers)
																]

						__lowerCAmelCase						 =common_inputs["""attention_mask"""]
						if self.use_past:
											__lowerCAmelCase						 =ordered_inputs["""attention_mask"""].dtype
											__lowerCAmelCase						 =torch.cat(
											    [ordered_inputs["""attention_mask"""], torch.ones(snake_case_					,						snake_case_					,						dtype=snake_case_)]					,						dim=1)

						return ordered_inputs



	@property
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				int:
						return 13




