





'''simple docstring'''

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
      import torch

      from transformers import BlipTextModel
      from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class  a_					:

    '''simple docstring'''


    def __init__(		self			,		A			,		A=12			,		A=7			,		A=True			,		A=True			,		A=True			,		A=99			,		A=32			,		A=32			,		A=5			,		A=4			,		A=37			,		A=0.1			,		A=0.1			,		A=512			,		A=0.02			,		A=0			,		A=None			,		)		->       Tuple:
        _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_labels
        _SCREAMING_SNAKE_CASE     =      vocab_size
        _SCREAMING_SNAKE_CASE     =      hidden_size
        _SCREAMING_SNAKE_CASE     =      projection_dim
        _SCREAMING_SNAKE_CASE     =      num_hidden_layers
        _SCREAMING_SNAKE_CASE     =      num_attention_heads
        _SCREAMING_SNAKE_CASE     =      intermediate_size
        _SCREAMING_SNAKE_CASE     =      dropout
        _SCREAMING_SNAKE_CASE     =      attention_dropout
        _SCREAMING_SNAKE_CASE     =      max_position_embeddings
        _SCREAMING_SNAKE_CASE     =      initializer_range
        _SCREAMING_SNAKE_CASE     =      scope
        _SCREAMING_SNAKE_CASE     =      bos_token_id


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

        _SCREAMING_SNAKE_CASE     =      None
        if self.use_input_mask:
            _SCREAMING_SNAKE_CASE     =      random_attention_mask([self.batch_size, self.seq_length]	)

        if input_mask is not None:
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      input_mask.shape
            _SCREAMING_SNAKE_CASE     =      np.random.randint(1			,		seq_length - 1			,		size=(batch_size,)	)
            for batch_idx, start_index in enumerate(A	):
                _SCREAMING_SNAKE_CASE     =      1
                _SCREAMING_SNAKE_CASE     =      0

        _SCREAMING_SNAKE_CASE     =      self.get_config()

        return config, input_ids, input_mask


    def       snake_case_(		self	)		->       List[Any]:
        return BlipTextConfig(
            vocab_size=self.vocab_size			,		hidden_size=self.hidden_size			,		projection_dim=self.projection_dim			,		num_hidden_layers=self.num_hidden_layers			,		num_attention_heads=self.num_attention_heads			,		intermediate_size=self.intermediate_size			,		dropout=self.dropout			,		attention_dropout=self.attention_dropout			,		max_position_embeddings=self.max_position_embeddings			,		initializer_range=self.initializer_range			,		bos_token_id=self.bos_token_id			,		)


    def       snake_case_(		self			,		A			,		A			,		A	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      BlipTextModel(config=A	)
        model.to(A	)
        model.eval()
        with torch.no_grad():
            _SCREAMING_SNAKE_CASE     =      model(A			,		attention_mask=A	)
            _SCREAMING_SNAKE_CASE     =      model(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	)		->       int:
        _SCREAMING_SNAKE_CASE     =      self.prepare_config_and_inputs()
        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      config_and_inputs
        _SCREAMING_SNAKE_CASE     =      {"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict




@require_torch
class  a_					(    snake_case_				,   unittest.TestCase	):

    '''simple docstring'''
    UpperCamelCase			   =			(BlipTextModel,) if is_torch_available() else ()
    UpperCamelCase			   =			False
    UpperCamelCase			   =			False
    UpperCamelCase			   =			False


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


    def       snake_case_(		self	)		->       List[Any]:
        pass


    def       snake_case_(		self	)		->       str:
        pass


    @unittest.skip(reason="""Blip does not use inputs_embeds"""	)
    def       snake_case_(		self	)		->       int:
        pass


    @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""	)
    def       snake_case_(		self	)		->       List[Any]:
        pass


    @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""	)
    def       snake_case_(		self	)		->       Tuple:
        pass


    @slow
    def       snake_case_(		self	)		->       str:
        for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE     =      BlipTextModel.from_pretrained(A	)
            self.assertIsNotNone(A	)





    def       snake_case_(		self	)		->       Union[str, Any]:
        super().test_pt_tf_model_equivalence(allow_missing_keys=A	)










'''simple docstring'''

def lowerCamelCase						(					__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int   )						->int:
    return len_board * len_board_column * row + column





def lowerCamelCase						(					__lowerCamelCase			:   list[list[str]] ,	__lowerCamelCase			:   str ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   set[int] ,	)						->bool:

    if board[row][column] != word[word_index]:
        return False

    if word_index == len(__lowerCamelCase   ) - 1:
        return True

    _SCREAMING_SNAKE_CASE     =      [(0, 1), (0, -1), (-1, 0), (1, 0)]
    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      len(board[0]   )
    for direction in traverts_directions:
        _SCREAMING_SNAKE_CASE     =      row + direction[0]
        _SCREAMING_SNAKE_CASE     =      column + direction[1]
        if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
            continue

        _SCREAMING_SNAKE_CASE     =      get_point_key(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )
        if key in visited_points_set:
            continue

        visited_points_set.add(__lowerCamelCase   )
        if exits_word(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	word_index + 1 ,	__lowerCamelCase   ):
            return True

        visited_points_set.remove(__lowerCamelCase   )

    return False





def lowerCamelCase						(					__lowerCamelCase			:   list[list[str]] ,	__lowerCamelCase			:   str   )						->bool:
    _SCREAMING_SNAKE_CASE     =      (
        """The board should be a non empty matrix of single chars strings."""
    )

    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )
    if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
        raise ValueError(__lowerCamelCase   )

    for row in board:
        if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
            raise ValueError(__lowerCamelCase   )

        for item in row:
            if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) != 1:
                raise ValueError(__lowerCamelCase   )

    # Validate word
    if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
        raise ValueError(
            """The word parameter should be a string of length greater than 0."""   )

    _SCREAMING_SNAKE_CASE     =      len(board[0]   )
    for i in range(__lowerCamelCase   ):
        for j in range(__lowerCamelCase   ):
            if exits_word(
                __lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	0 ,	{get_point_key(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )}   ):
                return True

    return False


if __name__ == "__main__":
      import doctest

      doctest.testmod()



