





'''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  a_					:

    '''simple docstring'''


    def __init__(		self			,		A			,		A=13			,		A=7			,		A=True			,		A=True			,		A=True			,		A=True			,		A=99			,		A=32			,		A=5			,		A=4			,		A=37			,		A="gelu"			,		A=0.1			,		A=0.1			,		A=512			,		A=16			,		A=2			,		A=0.02			,		A=3			,		A=4			,		A=None			,		A=1000			,		)		->       Union[str, Any]:
        _SCREAMING_SNAKE_CASE     =      parent
        _SCREAMING_SNAKE_CASE     =      batch_size
        _SCREAMING_SNAKE_CASE     =      seq_length
        _SCREAMING_SNAKE_CASE     =      is_training
        _SCREAMING_SNAKE_CASE     =      use_input_mask
        _SCREAMING_SNAKE_CASE     =      use_token_type_ids
        _SCREAMING_SNAKE_CASE     =      use_labels
        _SCREAMING_SNAKE_CASE     =      vocab_size
        _SCREAMING_SNAKE_CASE     =      hidden_size
        _SCREAMING_SNAKE_CASE     =      num_hidden_layers
        _SCREAMING_SNAKE_CASE     =      num_attention_heads
        _SCREAMING_SNAKE_CASE     =      intermediate_size
        _SCREAMING_SNAKE_CASE     =      hidden_act
        _SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
        _SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE     =      max_position_embeddings
        _SCREAMING_SNAKE_CASE     =      type_vocab_size
        _SCREAMING_SNAKE_CASE     =      type_sequence_label_size
        _SCREAMING_SNAKE_CASE     =      initializer_range
        _SCREAMING_SNAKE_CASE     =      num_labels
        _SCREAMING_SNAKE_CASE     =      num_choices
        _SCREAMING_SNAKE_CASE     =      scope
        _SCREAMING_SNAKE_CASE     =      range_bbox


    def       snake_case_(		self	)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size	)

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

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

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

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

        _SCREAMING_SNAKE_CASE     =      self.get_config()

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


    def       snake_case_(		self	)		->       Any:
        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       snake_case_(		self			,		A			,		A			,		A			,		A			,		A			,		A			,		A			,		A	)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      LayoutLMModel(config=A	)
        model.to(A	)
        model.eval()
        _SCREAMING_SNAKE_CASE     =      model(A			,		A			,		attention_mask=A			,		token_type_ids=A	)
        _SCREAMING_SNAKE_CASE     =      model(A			,		A			,		token_type_ids=A	)
        _SCREAMING_SNAKE_CASE     =      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       snake_case_(		self			,		A			,		A			,		A			,		A			,		A			,		A			,		A			,		A	)		->       str:
        _SCREAMING_SNAKE_CASE     =      LayoutLMForMaskedLM(config=A	)
        model.to(A	)
        model.eval()
        _SCREAMING_SNAKE_CASE     =      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       snake_case_(		self			,		A			,		A			,		A			,		A			,		A			,		A			,		A			,		A	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.num_labels
        _SCREAMING_SNAKE_CASE     =      LayoutLMForSequenceClassification(A	)
        model.to(A	)
        model.eval()
        _SCREAMING_SNAKE_CASE     =      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       snake_case_(		self			,		A			,		A			,		A			,		A			,		A			,		A			,		A			,		A	)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      self.num_labels
        _SCREAMING_SNAKE_CASE     =      LayoutLMForTokenClassification(config=A	)
        model.to(A	)
        model.eval()
        _SCREAMING_SNAKE_CASE     =      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       snake_case_(		self			,		A			,		A			,		A			,		A			,		A			,		A			,		A			,		A	)		->       Any:
        _SCREAMING_SNAKE_CASE     =      LayoutLMForQuestionAnswering(config=A	)
        model.to(A	)
        model.eval()
        _SCREAMING_SNAKE_CASE     =      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       snake_case_(		self	)		->       List[Any]:
        _SCREAMING_SNAKE_CASE     =      self.prepare_config_and_inputs()
        (
            (
            _SCREAMING_SNAKE_CASE
        )    ,	(
            _SCREAMING_SNAKE_CASE
        )    ,	(
            _SCREAMING_SNAKE_CASE
        )    ,	(
            _SCREAMING_SNAKE_CASE
        )    ,	(
            _SCREAMING_SNAKE_CASE
        )    ,	(
            _SCREAMING_SNAKE_CASE
        )    ,	(
            _SCREAMING_SNAKE_CASE
        )    ,	(
            _SCREAMING_SNAKE_CASE
        )    ,	
        )     =      config_and_inputs
        _SCREAMING_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  a_					(    snake_case_				,   snake_case_				,   unittest.TestCase	):

    '''simple docstring'''
    UpperCamelCase			   =			(
        (
            LayoutLMModel,
            LayoutLMForMaskedLM,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMForQuestionAnswering,
        )
        if is_torch_available()
        else None
    )
    UpperCamelCase			   =			(
        {
            '''document-question-answering''': LayoutLMForQuestionAnswering,
            '''feature-extraction''': LayoutLMModel,
            '''fill-mask''': LayoutLMForMaskedLM,
            '''text-classification''': LayoutLMForSequenceClassification,
            '''token-classification''': LayoutLMForTokenClassification,
            '''zero-shot''': LayoutLMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    UpperCamelCase			   =			True


    def       snake_case_(		self	)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      LayoutLMModelTester(self	)
        _SCREAMING_SNAKE_CASE     =      ConfigTester(self			,		config_class=A			,		hidden_size=37	)


    def       snake_case_(		self	)		->       Dict:
        self.config_tester.run_common_tests()


    def       snake_case_(		self	)		->       List[Any]:
        _SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A	)


    def       snake_case_(		self	)		->       List[Any]:
        _SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            _SCREAMING_SNAKE_CASE     =      type
            self.model_tester.create_and_check_model(*A	)


    def       snake_case_(		self	)		->       int:
        _SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*A	)


    def       snake_case_(		self	)		->       List[str]:
        _SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*A	)


    def       snake_case_(		self	)		->       Any:
        _SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*A	)


    def       snake_case_(		self	)		->       List[str]:
        _SCREAMING_SNAKE_CASE     =      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       snake_case_(		self	)		->       str:
        pass





def lowerCamelCase						(					)						->Tuple:
    # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
    # fmt: off
    _SCREAMING_SNAKE_CASE     =      torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]] ,	device=__lowerCamelCase   )  # noqa: E231
    _SCREAMING_SNAKE_CASE     =      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=__lowerCamelCase   )  # noqa: E231
    _SCREAMING_SNAKE_CASE     =      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=__lowerCamelCase   )  # noqa: E231
    _SCREAMING_SNAKE_CASE     =      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=__lowerCamelCase   )  # noqa: E231
    # these are sequence labels (i.e. at the token level)
    _SCREAMING_SNAKE_CASE     =      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=__lowerCamelCase   )  # noqa: E231
    # fmt: on

    return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class  a_					(    unittest.TestCase	):

    '''simple docstring'''


    @slow
    def       snake_case_(		self	)		->       List[Any]:
        _SCREAMING_SNAKE_CASE     =      LayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""	).to(A	)

        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      prepare_layoutlm_batch_inputs()

        # forward pass
        _SCREAMING_SNAKE_CASE     =      model(input_ids=A			,		bbox=A			,		attention_mask=A			,		token_type_ids=A	)

        # test the sequence output on [0, :3, :3]
        _SCREAMING_SNAKE_CASE     =      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]
        _SCREAMING_SNAKE_CASE     =      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       snake_case_(		self	)		->       Optional[Any]:
        # initialize model with randomly initialized sequence classification head
        _SCREAMING_SNAKE_CASE     =      LayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""			,		num_labels=2	).to(
            A	)

        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      prepare_layoutlm_batch_inputs()

        # forward pass
        _SCREAMING_SNAKE_CASE     =      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
        _SCREAMING_SNAKE_CASE     =      outputs.loss
        _SCREAMING_SNAKE_CASE     =      torch.Size([]	)
        self.assertEqual(loss.shape			,		A	)

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


    @slow
    def       snake_case_(		self	)		->       List[str]:
        # initialize model with randomly initialized token classification head
        _SCREAMING_SNAKE_CASE     =      LayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""			,		num_labels=13	).to(
            A	)

        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      prepare_layoutlm_batch_inputs()

        # forward pass
        _SCREAMING_SNAKE_CASE     =      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
        _SCREAMING_SNAKE_CASE     =      outputs.logits
        _SCREAMING_SNAKE_CASE     =      torch.Size((2, 25, 13)	)
        self.assertEqual(logits.shape			,		A	)





    @slow
    def       snake_case_(		self	)		->       str:
        # initialize model with randomly initialized token classification head
        _SCREAMING_SNAKE_CASE     =      LayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""	).to(A	)

        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      prepare_layoutlm_batch_inputs()

        # forward pass
        _SCREAMING_SNAKE_CASE     =      model(input_ids=A			,		bbox=A			,		attention_mask=A			,		token_type_ids=A	)

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










'''simple docstring'''

import os
import sys
import time

from . import transposition_cipher as trans_cipher





def lowerCamelCase						(					)						->None:
    _SCREAMING_SNAKE_CASE     =      """Prehistoric Men.txt"""
    _SCREAMING_SNAKE_CASE     =      """Output.txt"""
    _SCREAMING_SNAKE_CASE     =      int(input("""Enter key: """   )   )
    _SCREAMING_SNAKE_CASE     =      input("""Encrypt/Decrypt [e/d]: """   )

    if not os.path.exists(__lowerCamelCase   ):
        print(F'File {input_file} does not exist. Quitting...'   )
        sys.exit()
    if os.path.exists(__lowerCamelCase   ):
        print(F'Overwrite {output_file}? [y/n]'   )
        _SCREAMING_SNAKE_CASE     =      input("""> """   )
        if not response.lower().startswith("""y"""   ):
            sys.exit()

    _SCREAMING_SNAKE_CASE     =      time.time()
    if mode.lower().startswith("""e"""   ):
        with open(__lowerCamelCase   ) as f:
            _SCREAMING_SNAKE_CASE     =      f.read()
        _SCREAMING_SNAKE_CASE     =      trans_cipher.encrypt_message(__lowerCamelCase ,	__lowerCamelCase   )
    elif mode.lower().startswith("""d"""   ):
        with open(__lowerCamelCase   ) as f:
            _SCREAMING_SNAKE_CASE     =      f.read()
        _SCREAMING_SNAKE_CASE     =      trans_cipher.decrypt_message(__lowerCamelCase ,	__lowerCamelCase   )

    with open(__lowerCamelCase ,	"""w"""   ) as output_obj:
        output_obj.write(__lowerCamelCase   )

    _SCREAMING_SNAKE_CASE     =      round(time.time() - start_time ,	2   )
    print(("""Done (""", total_time, """seconds )""")   )


if __name__ == "__main__":
      main()



