

'''simple docstring'''







import unittest

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

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 (
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
    )

class   _snake_case:






 def __init__(self					:     List[str]						,		a					:     int						,		a					:     List[str]=13						,		a					:     int=7						,		a					:     str=True						,		a					:     Optional[Any]=True						,		a					:     Any=True						,		a					:     List[Any]=True						,		a					:     int=99						,		a					:     List[str]=32						,		a					:     Dict=5						,		a					:     Any=4						,		a					:     List[Any]=37						,		a					:     List[str]="gelu"						,		a					:     Any=0.1						,		a					:     Union[str, Any]=0.1						,		a					:     str=5_12						,		a					:     Optional[int]=16						,		a					:     Optional[int]=2						,		a					:     Union[str, Any]=0.02						,		a					:     Dict=3						,		a					:     Tuple=4						,		a					:     Union[str, Any]=None						,		a					:     Optional[int]=10_00						,		)					->      Optional[Any]:






    """simple docstring"""


    A__         =    parent
    A__         =    batch_size
    A__         =    seq_length
    A__         =    is_training
    A__         =    use_input_mask
    A__         =    use_token_type_ids
    A__         =    use_labels
    A__         =    vocab_size
    A__         =    hidden_size
    A__         =    num_hidden_layers
    A__         =    num_attention_heads
    A__         =    intermediate_size
    A__         =    hidden_act
    A__         =    hidden_dropout_prob
    A__         =    attention_probs_dropout_prob
    A__         =    max_position_embeddings
    A__         =    type_vocab_size
    A__         =    type_sequence_label_size
    A__         =    initializer_range
    A__         =    num_labels
    A__         =    num_choices
    A__         =    scope
    A__         =    range_bbox






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






    """simple docstring"""


    A__         =    ids_tensor([self.batch_size, self.seq_length]						,		self.vocab_size						)

    A__         =    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]:
             A__         =    bbox[i, j, 3]
             A__         =    bbox[i, j, 1]
             A__         =    t
          if bbox[i, j, 2] < bbox[i, j, 0]:
             A__         =    bbox[i, j, 2]
             A__         =    bbox[i, j, 0]
             A__         =    t

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

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

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

    A__         =    self.get_config()

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






 def 	_UpperCamelCase   (self					:     Tuple						)					->      Any:






    """simple docstring"""


    return 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						,		)






 def 	_UpperCamelCase   (self					:     Tuple						,		a					:     List[str]						,		a					:     List[Any]						,		a					:     Dict						,		a					:     Any						,		a					:     Optional[int]						,		a					:     List[Any]						,		a					:     str						,		a					:     Any						)					->      Optional[Any]:






    """simple docstring"""


    A__         =    LayoutLMModel(config=a						)
    model.to(a						)
    model.eval()
    A__         =    model(a						,		a						,		attention_mask=a						,		token_type_ids=a						)
    A__         =    model(a						,		a						,		token_type_ids=a						)
    A__         =    model(a						,		a						)
    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					:     Any						,		a					:     str						,		a					:     str						,		a					:     Any						,		a					:     Any						,		a					:     Any						,		a					:     Dict						,		a					:     Optional[int]						,		a					:     int						)					->      str:






    """simple docstring"""


    A__         =    LayoutLMForMaskedLM(config=a						)
    model.to(a						)
    model.eval()
    A__         =    model(a						,		a						,		attention_mask=a						,		token_type_ids=a						,		labels=a						)
    self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.seq_length, self.vocab_size)						)






 def 	_UpperCamelCase   (self					:     Any						,		a					:     Union[str, Any]						,		a					:     Optional[int]						,		a					:     List[str]						,		a					:     Any						,		a					:     Optional[Any]						,		a					:     str						,		a					:     Tuple						,		a					:     List[str]						)					->      Any:






    """simple docstring"""


    A__         =    self.num_labels
    A__         =    LayoutLMForSequenceClassification(a						)
    model.to(a						)
    model.eval()
    A__         =    model(
        a						,		a						,		attention_mask=a						,		token_type_ids=a						,		labels=a						)
    self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels)						)






 def 	_UpperCamelCase   (self					:     Dict						,		a					:     Optional[Any]						,		a					:     Optional[int]						,		a					:     List[str]						,		a					:     Any						,		a					:     Union[str, Any]						,		a					:     Any						,		a					:     Union[str, Any]						,		a					:     Dict						)					->      Any:






    """simple docstring"""


    A__         =    self.num_labels
    A__         =    LayoutLMForTokenClassification(config=a						)
    model.to(a						)
    model.eval()
    A__         =    model(a						,		a						,		attention_mask=a						,		token_type_ids=a						,		labels=a						)
    self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.seq_length, self.num_labels)						)






 def 	_UpperCamelCase   (self					:     Dict						,		a					:     Tuple						,		a					:     Any						,		a					:     str						,		a					:     Optional[Any]						,		a					:     int						,		a					:     List[Any]						,		a					:     Optional[Any]						,		a					:     Tuple						)					->      List[Any]:






    """simple docstring"""


    A__         =    LayoutLMForQuestionAnswering(config=a						)
    model.to(a						)
    model.eval()
    A__         =    model(
        a						,		bbox=a						,		attention_mask=a						,		token_type_ids=a						,		start_positions=a						,		end_positions=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 	_UpperCamelCase   (self					:     Dict						)					->      int:






    """simple docstring"""


    A__         =    self.prepare_config_and_inputs()
    (
        (
        A__ 
    )     , (
        A__ 
    )     , (
        A__ 
    )     , (
        A__ 
    )     , (
        A__ 
    )     , (
        A__ 
    )     , (
        A__ 
    )     , (
        A__ 
    )     ,  
    )         =    config_and_inputs
    A__         =    {
        'input_ids': input_ids,
        'bbox': bbox,
        'token_type_ids': token_type_ids,
        'attention_mask': input_mask,
    }
    return config, inputs_dict




@require_torch
class   _snake_case( UpperCAmelCase					,   UpperCAmelCase					,   unittest.TestCase			):
 __snake_case:       List[str]									=				(
     (
         LayoutLMModel,
         LayoutLMForMaskedLM,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 __snake_case:       Dict									=				(
     {
         '''document-question-answering''': LayoutLMForQuestionAnswering,
         '''feature-extraction''': LayoutLMModel,
         '''fill-mask''': LayoutLMForMaskedLM,
         '''text-classification''': LayoutLMForSequenceClassification,
         '''token-classification''': LayoutLMForTokenClassification,
         '''zero-shot''': LayoutLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __snake_case:       str									=				True






 def 	_UpperCamelCase   (self					:     List[str]						)					->      Tuple:






    """simple docstring"""


    A__         =    LayoutLMModelTester(self						)
    A__         =    ConfigTester(self						,		config_class=a						,		hidden_size=37						)






 def 	_UpperCamelCase   (self					:     int						)					->      str:






    """simple docstring"""


    self.config_tester.run_common_tests()






 def 	_UpperCamelCase   (self					:     Dict						)					->      List[Any]:






    """simple docstring"""


    A__         =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a						)






 def 	_UpperCamelCase   (self					:     int						)					->      Any:






    """simple docstring"""


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






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






    """simple docstring"""


    A__         =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*a						)






 def 	_UpperCamelCase   (self					:     Dict						)					->      Dict:






    """simple docstring"""


    A__         =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*a						)






 def 	_UpperCamelCase   (self					:     Any						)					->      Optional[int]:






    """simple docstring"""


    A__         =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*a						)






 def 	_UpperCamelCase   (self					:     Any						)					->      List[str]:






    """simple docstring"""


    A__         =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*a						)






 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
 def 	_UpperCamelCase   (self					:     List[Any]						)					->      Optional[Any]:






    """simple docstring"""


    pass







def 	_A			(   ):






   '''simple docstring'''







   A__         =    torch.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]]			,device=UpperCAmelCase	)  # noqa: E231
   A__         =    torch.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],]			,device=UpperCAmelCase	)  # noqa: E231
   A__         =    torch.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]]]			,device=UpperCAmelCase	)  # noqa: E231
   A__         =    torch.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]]			,device=UpperCAmelCase	)  # noqa: E231
   # these are sequence labels (i.e. at the token level)
   A__         =    torch.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]]			,device=UpperCAmelCase	)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class   _snake_case( unittest.TestCase			):






 @slow
 def 	_UpperCamelCase   (self					:     Tuple						)					->      Union[str, Any]:






    """simple docstring"""


    A__         =    LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'						).to(a						)

    A__     , A__     , A__     , A__     , A__         =    prepare_layoutlm_batch_inputs()

    # forward pass
    A__         =    model(input_ids=a						,		bbox=a						,		attention_mask=a						,		token_type_ids=a						)

    # test the sequence output on [0, :3, :3]
    A__         =    torch.tensor(
        [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]						,		device=a						,		)

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

    # test the pooled output on [1, :3]
    A__         =    torch.tensor([-0.6580, -0.0214, 0.8552]						,		device=a						)

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






 @slow
 def 	_UpperCamelCase   (self					:     List[Any]						)					->      Dict:






    """simple docstring"""


    A__         =    LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'						,		num_labels=2						).to(
        a						)

    A__     , A__     , A__     , A__     , A__         =    prepare_layoutlm_batch_inputs()

    # forward pass
    A__         =    model(
        input_ids=a						,		bbox=a						,		attention_mask=a						,		token_type_ids=a						,		labels=torch.tensor([1, 1]						,		device=a						)						,		)

    # test whether we get a loss as a scalar
    A__         =    outputs.loss
    A__         =    torch.Size([]						)
    self.assertEqual(loss.shape						,		a						)

    # test the shape of the logits
    A__         =    outputs.logits
    A__         =    torch.Size((2, 2)						)
    self.assertEqual(logits.shape						,		a						)






 @slow
 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      str:






    """simple docstring"""


    A__         =    LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'						,		num_labels=13						).to(
        a						)

    A__     , A__     , A__     , A__     , A__         =    prepare_layoutlm_batch_inputs()

    # forward pass
    A__         =    model(
        input_ids=a						,		bbox=a						,		attention_mask=a						,		token_type_ids=a						,		labels=a						)

    # test the loss calculation to be around 2.65
    # expected_loss = torch.tensor(2.65, device=torch_device)

    # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
    # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

    # test the shape of the logits
    A__         =    outputs.logits
    A__         =    torch.Size((2, 25, 13)						)
    self.assertEqual(logits.shape						,		a						)






 @slow
 def 	_UpperCamelCase   (self					:     List[str]						)					->      Optional[Any]:






    """simple docstring"""


    A__         =    LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'						).to(a						)

    A__     , A__     , A__     , A__     , A__         =    prepare_layoutlm_batch_inputs()

    # forward pass
    A__         =    model(input_ids=a						,		bbox=a						,		attention_mask=a						,		token_type_ids=a						)

    # test the shape of the logits
    A__         =    torch.Size((2, 25)						)
    self.assertEqual(outputs.start_logits.shape						,		a						)
    self.assertEqual(outputs.end_logits.shape						,		a						)








'''simple docstring'''







import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


lowerCAmelCase_    	=							2 << 2_0  # 2mb
lowerCAmelCase_    	=							get_logger(__name__)

class   _snake_case( UpperCAmelCase			):






 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      Optional[Any]:






    """simple docstring"""


    A__         =    self._options.get_all_options().get('runner'						)
    return runner in [None, "DirectRunner", "PortableRunner"]







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase=False	):






   '''simple docstring'''







   A__         =    FileSystems
   if fs.exists(UpperCAmelCase	):
      if force_upload:
         logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""	)
      else:
         logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""	)
         return
   with fs.create(UpperCAmelCase	) as remote_file:
      with open(UpperCAmelCase			,'rb'	) as local_file:
         A__         =    local_file.read(UpperCAmelCase	)
         while chunk:
            remote_file.write(UpperCAmelCase	)
            A__         =    local_file.read(UpperCAmelCase	)







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase=False	):






   '''simple docstring'''







   A__         =    FileSystems
   if os.path.exists(UpperCAmelCase	):
      if force_download:
         logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""	)
      else:
         logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""	)
         return
   with fs.open(UpperCAmelCase	) as remote_file:
      with open(UpperCAmelCase			,'wb'	) as local_file:
         A__         =    remote_file.read(UpperCAmelCase	)
         while chunk:
            local_file.write(UpperCAmelCase	)
            A__         =    remote_file.read(UpperCAmelCase	)


