"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import 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 numpy
       import tensorflow as tf

       from transformers import (
           TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
           TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
           TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
           BertConfig,
           DPRConfig,
           TFDPRContextEncoder,
           TFDPRQuestionEncoder,
           TFDPRReader,
       )





class 					_A		:

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=13						,			__lowerCAmelCase=7						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=99						,			__lowerCAmelCase=32						,			__lowerCAmelCase=2						,			__lowerCAmelCase=4						,			__lowerCAmelCase=37						,			__lowerCAmelCase="gelu"						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=512						,			__lowerCAmelCase=16						,			__lowerCAmelCase=2						,			__lowerCAmelCase=0.0_2						,			__lowerCAmelCase=3						,			__lowerCAmelCase=4						,			__lowerCAmelCase=None						,			__lowerCAmelCase=0						,			):







            """simple docstring"""



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

      def 							A__			(		self		):







            """simple docstring"""



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

            lowercase								=      None
            if self.use_input_mask:
                  # follow test_modeling_tf_ctrl.py
                  lowercase								=      random_attention_mask([self.batch_size, self.seq_length]		)

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

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

            lowercase								=      BertConfig(
                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						,			is_decoder=__lowerCAmelCase						,			initializer_range=self.initializer_range						,			)
            lowercase								=      DPRConfig(projection_dim=self.projection_dim						,			**config.to_dict()		)

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

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      TFDPRContextEncoder(config=__lowerCAmelCase		)
            lowercase								=      model(__lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			token_type_ids=__lowerCAmelCase		)
            lowercase								=      model(__lowerCAmelCase						,			token_type_ids=__lowerCAmelCase		)
            lowercase								=      model(__lowerCAmelCase		)
            self.parent.assertEqual(result.pooler_output.shape						,			(self.batch_size, self.projection_dim or self.hidden_size)		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      TFDPRQuestionEncoder(config=__lowerCAmelCase		)
            lowercase								=      model(__lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			token_type_ids=__lowerCAmelCase		)
            lowercase								=      model(__lowerCAmelCase						,			token_type_ids=__lowerCAmelCase		)
            lowercase								=      model(__lowerCAmelCase		)
            self.parent.assertEqual(result.pooler_output.shape						,			(self.batch_size, self.projection_dim or self.hidden_size)		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      TFDPRReader(config=__lowerCAmelCase		)
            lowercase								=      model(__lowerCAmelCase						,			attention_mask=__lowerCAmelCase		)

            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)		)
            self.parent.assertEqual(result.relevance_logits.shape						,			(self.batch_size,)		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.prepare_config_and_inputs()
            (
                (
                lowercase
            )				,				(
                lowercase
            )				,				(
                lowercase
            )				,				(
                lowercase
            )				,				(
                lowercase
            )				,				(
                lowercase
            )				,				(
                lowercase
            )				,				
            )								=      config_and_inputs
            lowercase								=      {"""input_ids""": input_ids}
            return config, inputs_dict






@require_tf
class 					_A		(			lowerCAmelCase    ,       lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  Optional[Any]				    =      (
          (
              TFDPRContextEncoder,
              TFDPRQuestionEncoder,
              TFDPRReader,
          )
          if is_tf_available()
          else ()
      )
      snake_case__     :  List[Any]				    =      {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}

      snake_case__     :  Tuple				    =      False
      snake_case__     :  Any				    =      False
      snake_case__     :  Any				    =      False
      snake_case__     :  Optional[Any]				    =      False
      snake_case__     :  Dict				    =      False

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TFDPRModelTester(self		)
            lowercase								=      ConfigTester(self						,			config_class=__lowerCAmelCase						,			hidden_size=37		)

      def 							A__			(		self		):







            """simple docstring"""



            self.config_tester.run_common_tests()

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_dpr_context_encoder(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_dpr_question_encoder(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_dpr_reader(*__lowerCAmelCase		)







      @slow
      def 							A__			(		self		):







            """simple docstring"""



            for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase								=      TFDPRContextEncoder.from_pretrained(__lowerCAmelCase		)
                  self.assertIsNotNone(__lowerCAmelCase		)

            for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase								=      TFDPRContextEncoder.from_pretrained(__lowerCAmelCase		)
                  self.assertIsNotNone(__lowerCAmelCase		)

            for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase								=      TFDPRQuestionEncoder.from_pretrained(__lowerCAmelCase		)
                  self.assertIsNotNone(__lowerCAmelCase		)

            for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase								=      TFDPRReader.from_pretrained(__lowerCAmelCase		)
                  self.assertIsNotNone(__lowerCAmelCase		)






@require_tf
class 					_A		(			unittest.TestCase      ):

      @slow
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base"""		)

            lowercase								=      tf.constant(
                [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]]		)  # [CLS] hello, is my dog cute? [SEP]
            lowercase								=      model(__lowerCAmelCase		)[0]  # embedding shape = (1, 768)
            # compare the actual values for a slice.
            lowercase								=      tf.constant(
                [
                    [
                        0.0_3_2_3_6_2_5_3,
                        0.1_2_7_5_3_3_3_5,
                        0.1_6_8_1_8_5_0_9,
                        0.0_0_2_7_9_7_8_6,
                        0.3_8_9_6_9_3_3,
                        0.2_4_2_6_4_9_4_5,
                        0.2_1_7_8_9_7_1,
                        -0.0_2_3_3_5_2_2_7,
                        -0.0_8_4_8_1_9_5_9,
                        -0.1_4_3_2_4_1_1_7,
                    ]
                ]		)
            self.assertTrue(numpy.allclose(output[:, :10].numpy()						,			expected_slice.numpy()						,			atol=1E-4		)		)




"""simple docstring"""






from __future__ import annotations


def      UpperCAmelCase__			(							lowerCAmelCase__	:dict   ,						lowerCAmelCase__	:str						)					->       set[str]:
      '''simple docstring'''


      lowercase				,				lowercase								=      set(lowerCAmelCase__						), [start]

      while stack:
            lowercase								=      stack.pop()
            explored.add(lowerCAmelCase__						)
            # 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(lowerCAmelCase__						)
      return explored


__lowerCAmelCase       :			str   	={
    """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"""))



