

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       SCREAMING_SNAKE_CASE__					:





    def __init__(      self						,   a						,   a=13						,   a=7						,   a=True						,   a=True						,   a=True						,   a=True						,   a=99						,   a=24						,   a=2						,   a=6						,   a=37						,   a="gelu"						,   a=0.1						,   a=0.1						,   a=512						,   a=16						,   a=2						,   a=0.02						,   a=3						,   a=None						,   a=1000						,   ):
         lowercase__    :   Any      =       parent
         lowercase__    :   Optional[Any]      =       batch_size
         lowercase__    :   int      =       seq_length
         lowercase__    :   Optional[int]      =       is_training
         lowercase__    :   List[str]      =       use_input_mask
         lowercase__    :   Optional[int]      =       use_token_type_ids
         lowercase__    :   Union[str, Any]      =       use_labels
         lowercase__    :   str      =       vocab_size
         lowercase__    :   Tuple      =       hidden_size
         lowercase__    :   int      =       num_hidden_layers
         lowercase__    :   int      =       num_attention_heads
         lowercase__    :   Optional[int]      =       intermediate_size
         lowercase__    :   Optional[Any]      =       hidden_act
         lowercase__    :   Union[str, Any]      =       hidden_dropout_prob
         lowercase__    :   Optional[Any]      =       attention_probs_dropout_prob
         lowercase__    :   Tuple      =       max_position_embeddings
         lowercase__    :   Union[str, Any]      =       type_vocab_size
         lowercase__    :   int      =       type_sequence_label_size
         lowercase__    :   Optional[Any]      =       initializer_range
         lowercase__    :   Optional[Any]      =       num_labels
         lowercase__    :   Optional[Any]      =       scope
         lowercase__    :   Any      =       range_bbox





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

         lowercase__    :   Tuple      =       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]:
                        lowercase__    :   Dict      =       bbox[i, j, 3]
                        lowercase__    :   Optional[int]      =       bbox[i, j, 1]
                        lowercase__    :   List[Any]      =       t
                   if bbox[i, j, 2] < bbox[i, j, 0]:
                        lowercase__    :   Optional[int]      =       bbox[i, j, 2]
                        lowercase__    :   List[str]      =       bbox[i, j, 0]
                        lowercase__    :   Union[str, Any]      =       t

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

         lowercase__    :   Optional[Any]      =       None
         if self.use_token_type_ids:
              lowercase__    :   List[str]      =       ids_tensor([self.batch_size, self.seq_length]						,   self.type_vocab_size)

         lowercase__    :   Tuple      =       None
         lowercase__    :   Any      =       None
         if self.use_labels:
              lowercase__    :   int      =       ids_tensor([self.batch_size]						,   self.type_sequence_label_size)
              lowercase__    :   Any      =       ids_tensor([self.batch_size, self.seq_length]						,   self.num_labels)

         lowercase__    :   List[Any]      =       self.get_config()

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





    def       snake_case_				(      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       snake_case_				(      self						,   a						,   a						,   a						,   a						,   a						,   a						,   a						,   ):
         lowercase__    :   List[Any]      =       LiltModel(config=a)
         model.to(a)
         model.eval()
         lowercase__    :   str      =       model(a						,   bbox=a						,   attention_mask=a						,   token_type_ids=a)
         lowercase__    :   Any      =       model(a						,   bbox=a						,   token_type_ids=a)
         lowercase__    :   Any      =       model(a						,   bbox=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						,   ):
         lowercase__    :   Tuple      =       self.num_labels
         lowercase__    :   Dict      =       LiltForTokenClassification(config=a)
         model.to(a)
         model.eval()
         lowercase__    :   Optional[int]      =       model(
             a						,   bbox=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						,   ):
         lowercase__    :   Any      =       LiltForQuestionAnswering(config=a)
         model.to(a)
         model.eval()
         lowercase__    :   List[str]      =       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):
         lowercase__    :   str      =       self.prepare_config_and_inputs()
         (
             (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , 
         )    :   Tuple      =       config_and_inputs
         lowercase__    :   List[str]      =       {
             'input_ids': input_ids,
             'bbox': bbox,
             'token_type_ids': token_type_ids,
             'attention_mask': input_mask,
         }
         return config, inputs_dict



@require_torch
class       SCREAMING_SNAKE_CASE__					(__snake_case		,       __snake_case		,       __snake_case		,       unittest.TestCase     ):
    __lowerCamelCase						:       Any														=     (
        (
            LiltModel,
            LiltForSequenceClassification,
            LiltForTokenClassification,
            LiltForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    __lowerCamelCase						:       Dict														=     (
        {
            """feature-extraction""": LiltModel,
            """question-answering""": LiltForQuestionAnswering,
            """text-classification""": LiltForSequenceClassification,
            """token-classification""": LiltForTokenClassification,
            """zero-shot""": LiltForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    __lowerCamelCase						:       List[str]														=     False
    __lowerCamelCase						:       str														=     False





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





    def       snake_case_				(      self):
         lowercase__    :   Any      =       LiltModelTester(self)
         lowercase__    :   List[Any]      =       ConfigTester(self						,   config_class=a						,   hidden_size=37)





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





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





    def       snake_case_				(      self):
         lowercase__    :   Union[str, Any]      =       self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              lowercase__    :   Optional[Any]      =       type
              self.model_tester.create_and_check_model(*a)





    def       snake_case_				(      self):
         lowercase__    :   List[Any]      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a)





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





    @slow
    def       snake_case_				(      self):
         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowercase__    :   Union[str, Any]      =       LiltModel.from_pretrained(a)
              self.assertIsNotNone(a)



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





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

         lowercase__    :   Optional[Any]      =       torch.tensor([[1, 2]]						,   device=a)
         lowercase__    :   Tuple      =       torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]						,   device=a)

         # forward pass
         with torch.no_grad():
              lowercase__    :   List[Any]      =       model(input_ids=a						,   bbox=a)

         lowercase__    :   Optional[Any]      =       torch.Size([1, 2, 768])
         lowercase__    :   List[Any]      =       torch.tensor(
             [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]]						,   device=a						,   )

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




from __future__ import annotations

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    dict	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     lowercase__      , lowercase__    :   List[str]      =       set(SCREAMING_SNAKE_CASE_			), [start]

     while stack:
          lowercase__    :   Union[str, Any]      =       stack.pop()
          explored.add(SCREAMING_SNAKE_CASE_			)
          # Differences from BFS:
          # 1) pop last element instead of first one
          # 2) add adjacent elements to stack without exploring them
          for adj in reversed(graph[v]			):
               if adj not in explored:
                    stack.append(SCREAMING_SNAKE_CASE_			)
     return explored


snake_case_			  = {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(depth_first_search(G, '''A'''))

