

import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    LiltForQuestionAnswering,
					    LiltForSequenceClassification,
					    LiltForTokenClassification,
					    LiltModel,
					)
					from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST







class  lowercase				:




				def __init__(					self      ,   snake_case      ,   snake_case=13      ,   snake_case=7      ,   snake_case=True      ,   snake_case=True      ,   snake_case=True      ,   snake_case=True      ,   snake_case=99      ,   snake_case=24      ,   snake_case=2      ,   snake_case=6      ,   snake_case=37      ,   snake_case="gelu"      ,   snake_case=0.1      ,   snake_case=0.1      ,   snake_case=512      ,   snake_case=16      ,   snake_case=2      ,   snake_case=0.02      ,   snake_case=3      ,   snake_case=None      ,   snake_case=1000      ,   ):
											snake_case_							=    parent
											snake_case_							=    batch_size
											snake_case_							=    seq_length
											snake_case_							=    is_training
											snake_case_							=    use_input_mask
											snake_case_							=    use_token_type_ids
											snake_case_							=    use_labels
											snake_case_							=    vocab_size
											snake_case_							=    hidden_size
											snake_case_							=    num_hidden_layers
											snake_case_							=    num_attention_heads
											snake_case_							=    intermediate_size
											snake_case_							=    hidden_act
											snake_case_							=    hidden_dropout_prob
											snake_case_							=    attention_probs_dropout_prob
											snake_case_							=    max_position_embeddings
											snake_case_							=    type_vocab_size
											snake_case_							=    type_sequence_label_size
											snake_case_							=    initializer_range
											snake_case_							=    num_labels
											snake_case_							=    scope
											snake_case_							=    range_bbox




				def     a							(					self       ):
											snake_case_							=    ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size       )

											snake_case_							=    ids_tensor([self.batch_size, self.seq_length, 4]      ,   self.range_bbox       )
											# 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]:
																																snake_case_							=    bbox[i, j, 3]
																																snake_case_							=    bbox[i, j, 1]
																																snake_case_							=    t
																									if bbox[i, j, 2] < bbox[i, j, 0]:
																																snake_case_							=    bbox[i, j, 2]
																																snake_case_							=    bbox[i, j, 0]
																																snake_case_							=    t

											snake_case_							=    None
											if self.use_input_mask:
																		snake_case_							=    ids_tensor([self.batch_size, self.seq_length]      ,   vocab_size=2       )

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

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

											snake_case_							=    self.get_config()

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




				def     a							(					self       ):
											return LiltConfig(
											    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      ,   )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   ):
											snake_case_							=    LiltModel(config=snake_case       )
											model.to(snake_case       )
											model.eval()
											snake_case_							=    model(snake_case      ,   bbox=snake_case      ,   attention_mask=snake_case      ,   token_type_ids=snake_case       )
											snake_case_							=    model(snake_case      ,   bbox=snake_case      ,   token_type_ids=snake_case       )
											snake_case_							=    model(snake_case      ,   bbox=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     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   ):
											snake_case_							=    self.num_labels
											snake_case_							=    LiltForTokenClassification(config=snake_case       )
											model.to(snake_case       )
											model.eval()
											snake_case_							=    model(
											    snake_case      ,   bbox=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     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   ):
											snake_case_							=    LiltForQuestionAnswering(config=snake_case       )
											model.to(snake_case       )
											model.eval()
											snake_case_							=    model(
											    snake_case      ,   bbox=snake_case      ,   attention_mask=snake_case      ,   token_type_ids=snake_case      ,   start_positions=snake_case      ,   end_positions=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     a							(					self       ):
											snake_case_							=    self.prepare_config_and_inputs()
											(
											    (
											    snake_case_
											)				,    (
											    snake_case_
											)				,    (
											    snake_case_
											)				,    (
											    snake_case_
											)				,    (
											    snake_case_
											)				,    (
											    snake_case_
											)				,    (
											    snake_case_
											)				,    
											)							=    config_and_inputs
											snake_case_							=    {
											    'input_ids': input_ids,
											    'bbox': bbox,
											    'token_type_ids': token_type_ids,
											    'attention_mask': input_mask,
											}
											return config, inputs_dict







@require_torch
class  lowercase				(       lowercase_	,  lowercase_	,  lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: str       			=			(
				    (
				        LiltModel,
				        LiltForSequenceClassification,
				        LiltForTokenClassification,
				        LiltForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				__SCREAMING_SNAKE_CASE					: int       			=			(
				    {
				        '''feature-extraction''': LiltModel,
				        '''question-answering''': LiltForQuestionAnswering,
				        '''text-classification''': LiltForSequenceClassification,
				        '''token-classification''': LiltForTokenClassification,
				        '''zero-shot''': LiltForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__SCREAMING_SNAKE_CASE					: Tuple       			=			False
				__SCREAMING_SNAKE_CASE					: int       			=			False




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											return True




				def     a							(					self       ):
											snake_case_							=    LiltModelTester(self       )
											snake_case_							=    ConfigTester(self      ,   config_class=snake_case      ,   hidden_size=37       )




				def     a							(					self       ):
											self.config_tester.run_common_tests()




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																		snake_case_							=    type
																		self.model_tester.create_and_check_model(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*snake_case       )




				@slow
				def     a							(					self       ):
											for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_							=    LiltModel.from_pretrained(snake_case       )
																		self.assertIsNotNone(snake_case       )







@require_torch
@slow
class  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											snake_case_							=    LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'       ).to(snake_case       )

											snake_case_							=    torch.tensor([[1, 2]]      ,   device=snake_case       )
											snake_case_							=    torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]      ,   device=snake_case       )

											# forward pass
											with torch.no_grad():
																		snake_case_							=    model(input_ids=snake_case      ,   bbox=snake_case       )

											snake_case_							=    torch.Size([1, 2, 768]       )
											snake_case_							=    torch.tensor(
											    [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]      ,   device=snake_case      ,   )

											self.assertTrue(outputs.last_hidden_state.shape      ,   snake_case       )
											self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]      ,   snake_case      ,   atol=1e-3       )       )







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    {}
							snake_case_							=    tokenizer(example['content']   ,							truncation=UpperCamelCase__      )['input_ids']
							snake_case_							=    len(example['content']      ) / len(output['input_ids']      )
							return output


_UpperCAmelCase		:   Dict										=       HfArgumentParser(PretokenizationArguments)
_UpperCAmelCase		:   List[Any]										=       parser.parse_args()
if args.num_workers is None:
					_UpperCAmelCase		:   Union[str, Any]										=       multiprocessing.cpu_count()
_UpperCAmelCase		:   int										=       AutoTokenizer.from_pretrained(args.tokenizer_dir)

_UpperCAmelCase		:   Optional[int]										=       time.time()
_UpperCAmelCase		:   List[str]										=       load_dataset(args.dataset_name, split="""train""")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

_UpperCAmelCase		:   Tuple										=       time.time()
_UpperCAmelCase		:   Union[str, Any]										=       ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

_UpperCAmelCase		:   Dict										=       time.time()
ds.push_to_hub(args.tokenized_data_repo)


print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')




