
"""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        _UpperCAmelCase							:




      def __init__(							self				,    lowercase_				,    lowercase_=1_2				,    lowercase_=7				,    lowercase_=True				,    lowercase_=True				,    lowercase_=True				,    lowercase_=9_9				,    lowercase_=3_2				,    lowercase_=3_2				,    lowercase_=5				,    lowercase_=4				,    lowercase_=3_7				,    lowercase_=0.1				,    lowercase_=0.1				,    lowercase_=5_1_2				,    lowercase_=0.0_2				,    lowercase_=0				,    lowercase_=None				,    )  ->   Optional[int]:
           UpperCAmelCase       =   parent
           UpperCAmelCase       =   batch_size
           UpperCAmelCase       =   seq_length
           UpperCAmelCase       =   is_training
           UpperCAmelCase       =   use_input_mask
           UpperCAmelCase       =   use_labels
           UpperCAmelCase       =   vocab_size
           UpperCAmelCase       =   hidden_size
           UpperCAmelCase       =   projection_dim
           UpperCAmelCase       =   num_hidden_layers
           UpperCAmelCase       =   num_attention_heads
           UpperCAmelCase       =   intermediate_size
           UpperCAmelCase       =   dropout
           UpperCAmelCase       =   attention_dropout
           UpperCAmelCase       =   max_position_embeddings
           UpperCAmelCase       =   initializer_range
           UpperCAmelCase       =   scope
           UpperCAmelCase       =   bos_token_id




      def       a_ (							self							)  ->   Optional[int]:
           UpperCAmelCase       =   ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size							)

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

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

           UpperCAmelCase       =   self.get_config()

           return config, input_ids, input_mask




      def       a_ (							self							)  ->   Union[str, 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       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Optional[Any]:
           UpperCAmelCase       =   BlipTextModel(config=lowercase_							)
           model.to(lowercase_							)
           model.eval()
           with torch.no_grad():
                UpperCAmelCase       =   model(lowercase_				,    attention_mask=lowercase_							)
                UpperCAmelCase       =   model(lowercase_							)
           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       a_ (							self							)  ->   Union[str, Any]:
           UpperCAmelCase       =   self.prepare_config_and_inputs()
           UpperCAmelCase     ,							UpperCAmelCase     ,							UpperCAmelCase       =   config_and_inputs
           UpperCAmelCase       =   {'input_ids': input_ids, 'attention_mask': input_mask}
           return config, inputs_dict

@require_torch
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     unittest.TestCase							):
      __SCREAMING_SNAKE_CASE				:							Optional[int]							 =		(BlipTextModel,) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE				:							str							 =		False
      __SCREAMING_SNAKE_CASE				:							List[Any]							 =		False
      __SCREAMING_SNAKE_CASE				:							Union[str, Any]							 =		False




      def       a_ (							self							)  ->   List[Any]:
           UpperCAmelCase       =   BlipTextModelTester(self							)
           UpperCAmelCase       =   ConfigTester(self				,    config_class=lowercase_				,    hidden_size=3_7							)




      def       a_ (							self							)  ->   Tuple:
           self.config_tester.run_common_tests()




      def       a_ (							self							)  ->   int:
           UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase_							)




      def       a_ (							self							)  ->   Optional[Any]:
           pass




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




      @unittest.skip(reason='Blip does not use inputs_embeds'							)
      def       a_ (							self							)  ->   Optional[Any]:
           pass




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




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




      @slow
      def       a_ (							self							)  ->   Union[str, Any]:
           for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase       =   BlipTextModel.from_pretrained(lowercase_							)
                self.assertIsNotNone(lowercase_							)




      def       a_ (							self							)  ->   Any:
           super().test_pt_tf_model_equivalence(allow_missing_keys=lowercase_							)





"""simple docstring"""

SCREAMING_SNAKE_CASE_         =  {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

SCREAMING_SNAKE_CASE_         =  [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

SCREAMING_SNAKE_CASE_         =  [0 for x in range(len(graph))]
SCREAMING_SNAKE_CASE_         =  []




def       lowercase__  (   lowerCAmelCase			:   Optional[int]	,			lowerCAmelCase			:   Any	)	-> List[str]:






    """simple docstring"""




    UpperCAmelCase       =   1
    while stack:
        UpperCAmelCase       =   stack.pop()
        print(lowerCAmelCase	,			clothes[current_clothing]	)
        order += 1




def       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Dict	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   1
    for v in graph[u]:
        if not visited[v]:
            depth_first_search(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)

    stack.append(lowerCAmelCase	)




def       lowercase__  (   lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   Dict	)	-> Optional[Any]:






    """simple docstring"""




    for v in range(len(lowerCAmelCase	)	):
        if not visited[v]:
            depth_first_search(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)



