'''simple docstring'''
import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
 from PIL import Image

 from transformers import MgpstrProcessor




class _lowerCAmelCase			:







      def __init__(  self  ,    lowercase  ,    lowercase=False  ,    lowercase=13  ,    lowercase=(32, 128)  ,    lowercase=4  ,    lowercase=3  ,    lowercase=27  ,    lowercase=38  ,    lowercase=99  ,    lowercase=99  ,    lowercase=32  ,    lowercase=5  ,    lowercase=4  ,    lowercase=4.0  ,    lowercase=257  ,    lowercase=None  ,    ):
        lowercase				:							Tuple									=   parent
        lowercase				:							int									=   is_training
        lowercase				:							Optional[int]									=   batch_size
        lowercase				:							Any									=   image_size
        lowercase				:							List[Any]									=   patch_size
        lowercase				:							Dict									=   num_channels
        lowercase				:							List[Any]									=   max_token_length
        lowercase				:							Union[str, Any]									=   num_character_labels
        lowercase				:							Optional[int]									=   num_bpe_labels
        lowercase				:							str									=   num_wordpiece_labels
        lowercase				:							Tuple									=   hidden_size
        lowercase				:							Optional[Any]									=   num_hidden_layers
        lowercase				:							str									=   num_attention_heads
        lowercase				:							List[str]									=   mlp_ratio
        lowercase				:							Optional[Any]									=   patch_embeds_hidden_size
        lowercase				:							str									=   output_hidden_states







      def      lowerCamelCase__      (  self     ):
        lowercase				:							int									=   floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]     )
        lowercase				:							List[str]									=   self.get_config()
        return config, pixel_values







      def      lowerCamelCase__      (  self     ):
        return MgpstrConfig(
            image_size=self.image_size  ,    patch_size=self.patch_size  ,    num_channels=self.num_channels  ,    max_token_length=self.max_token_length  ,    num_character_labels=self.num_character_labels  ,    num_bpe_labels=self.num_bpe_labels  ,    num_wordpiece_labels=self.num_wordpiece_labels  ,    hidden_size=self.hidden_size  ,    num_hidden_layers=self.num_hidden_layers  ,    num_attention_heads=self.num_attention_heads  ,    mlp_ratio=self.mlp_ratio  ,    output_hidden_states=self.output_hidden_states  ,    )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase     ):
        lowercase				:							Any									=   MgpstrForSceneTextRecognition(_lowerCamelCase     )
        model.to(_lowerCamelCase     )
        model.eval()
        with torch.no_grad():
          lowercase				:							List[Any]									=   model(_lowerCamelCase     )
        self.parent.assertEqual(
            generated_ids[0][0].shape  ,    (self.batch_size, self.max_token_length, self.num_character_labels)     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Any									=   self.prepare_config_and_inputs()
        lowercase				:							List[str]									=   config_and_inputs
        lowercase				:							Union[str, Any]									=   {'pixel_values': pixel_values}
        return config, inputs_dict





@require_torch
class _lowerCAmelCase			(	_lowerCamelCase							,   _lowerCamelCase							,   unittest.TestCase					):
      UpperCAmelCase_	:						Union[str, Any]	 =       (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
      UpperCAmelCase_	:						Union[str, Any]	 =       {"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
      UpperCAmelCase_	:						Tuple	 =       False

      UpperCAmelCase_	:						Optional[Any]	 =       False
      UpperCAmelCase_	:						str	 =       False
      UpperCAmelCase_	:						List[Any]	 =       False
      UpperCAmelCase_	:						Tuple	 =       False







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Union[str, Any]									=   MgpstrModelTester(self     )
        lowercase				:							int									=   ConfigTester(self  ,    config_class=_lowerCamelCase  ,    has_text_modality=_lowerCamelCase     )







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







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Dict									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_lowerCamelCase     )







      @unittest.skip(reason='''MgpstrModel does not use inputs_embeds'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[int]									=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowercase				:							Any									=   model_class(_lowerCamelCase     )
          self.assertIsInstance(model.get_input_embeddings()  ,    (nn.Module)     )
          lowercase				:							Tuple									=   model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(_lowerCamelCase  ,    nn.Linear     )     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							str									=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowercase				:							List[str]									=   model_class(_lowerCamelCase     )
          lowercase				:							int									=   inspect.signature(model.forward     )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowercase				:							List[Any]									=   [*signature.parameters.keys()]

          lowercase				:							Tuple									=   ['pixel_values']
          self.assertListEqual(arg_names[:1]  ,    _lowerCamelCase     )







      @unittest.skip(reason='''MgpstrModel does not support feedforward chunking'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[int]									=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          if not model_class.supports_gradient_checkpointing:
            continue

          lowercase				:							Any									=   True
          lowercase				:							str									=   model_class(_lowerCamelCase     )
          self.assertTrue(model.is_gradient_checkpointing     )







      def      lowerCamelCase__      (  self     ):
        def check_hidden_states_output(lowercase  ,    lowercase  ,    lowercase     ):
          lowercase				:							Optional[Any]									=   model_class(_lowerCamelCase     )
          model.to(_lowerCamelCase     )
          model.eval()

          with torch.no_grad():
            lowercase				:							Dict									=   model(**self._prepare_for_class(_lowerCamelCase  ,    _lowerCamelCase     )     )

          lowercase				:							Dict									=   outputs.hidden_states

          lowercase				:							Dict									=   getattr(
              self.model_tester  ,    '''expected_num_hidden_layers'''  ,    self.model_tester.num_hidden_layers + 1     )
          self.assertEqual(len(_lowerCamelCase     )  ,    _lowerCamelCase     )

          self.assertListEqual(
              list(hidden_states[0].shape[-2:]     )  ,    [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]  ,    )

        lowercase				:							str									=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowercase				:							Tuple									=   True
          check_hidden_states_output(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase     )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          lowercase				:							Dict									=   True

          check_hidden_states_output(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							List[str]									=   self.model_tester.prepare_config_and_inputs_for_common()

        lowercase				:							Any									=   _config_zero_init(_lowerCamelCase     )
        for model_class in self.all_model_classes:
          lowercase				:							List[str]									=   model_class(config=_lowerCamelCase     )
          for name, param in model.named_parameters():
            if isinstance(_lowerCamelCase  ,    (nn.Linear, nn.Convad, nn.LayerNorm)     ):
              if param.requires_grad:
                self.assertIn(
                    ((param.data.mean() * 1e9).round() / 1e9).item()  ,    [0.0, 1.0]  ,    msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""  ,    )







      @unittest.skip(reason='''Retain_grad is tested in individual model tests'''     )
      def      lowerCamelCase__      (  self     ):
        pass







def 				__UpperCamelCase (					)							->	int:
  lowercase				:							List[str]									=   'https://i.postimg.cc/ZKwLg2Gw/367-14.png'
  lowercase				:							str									=   Image.open(requests.get(_a ,   stream=_a			).raw			).convert('''RGB'''			)
  return im






@require_vision
@require_torch
class _lowerCAmelCase			(	unittest.TestCase					):







      @slow
      def      lowerCamelCase__      (  self     ):
        lowercase				:							List[str]									=   'alibaba-damo/mgp-str-base'
        lowercase				:							Optional[Any]									=   MgpstrForSceneTextRecognition.from_pretrained(_lowerCamelCase     ).to(_lowerCamelCase     )
        lowercase				:							Optional[int]									=   MgpstrProcessor.from_pretrained(_lowerCamelCase     )

        lowercase				:							Union[str, Any]									=   prepare_img()
        lowercase				:							Tuple									=   processor(images=_lowerCamelCase  ,    return_tensors='''pt'''     ).pixel_values.to(_lowerCamelCase     )

        # forward pass
        with torch.no_grad():
          lowercase				:							Optional[Any]									=   model(_lowerCamelCase     )

        # verify the logits
        self.assertEqual(outputs.logits[0].shape  ,    torch.Size((1, 27, 38)     )     )

        lowercase				:							List[str]									=   processor.batch_decode(outputs.logits     )
        lowercase				:							List[str]									=   'ticket'

        self.assertEqual(out_strs['''generated_text'''][0]  ,    _lowerCamelCase     )

        lowercase				:							int									=   torch.tensor(
            [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]  ,    device=_lowerCamelCase  ,    )

        self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]  ,    _lowerCamelCase  ,    atol=1e-4     )     )
'''simple docstring'''
from .stack import Stack







def 			_UpperCamelCase			(  _a						:						str      ):




    """simple docstring"""



    __UpperCamelCase						:						Stack[str]		     =	Stack()
    __UpperCamelCase						:						Any		     =	{'(': ')', '[': ']', '{': '}'}
    for bracket in parentheses:
        if bracket in bracket_pairs:
            stack.push(_a      )
        elif bracket in (")", "]", "}"):
            if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                return False
    return stack.is_empty()


if __name__ == "__main__":
  from doctest import testmod

  testmod()

  a=	['''((()))''', '''((())''', '''(()))''']
  print('''Balanced parentheses demonstration:\n''')
  for example in examples:
    a=	'''''' if balanced_parentheses(example) else '''not '''
    print(F"""{example} is {not_str}balanced""")



