

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				:


       '''simple docstring'''







       def __init__(	self					:  str						,	__snake_case					:  Tuple						,	__snake_case					:  Any=False						,	__snake_case					:  Optional[Any]=13						,	__snake_case					:  Dict=(32, 128)						,	__snake_case					:  Optional[Any]=4						,	__snake_case					:  str=3						,	__snake_case					:  Any=27						,	__snake_case					:  List[Any]=38						,	__snake_case					:  Optional[Any]=99						,	__snake_case					:  Dict=99						,	__snake_case					:  Tuple=32						,	__snake_case					:  Dict=5						,	__snake_case					:  int=4						,	__snake_case					:  Dict=4.0						,	__snake_case					:  str=257						,	__snake_case					:  Any=None						,	)   -> Tuple:







        '''simple docstring'''

        lowerCamelCase      						=    parent
        lowerCamelCase      						=    is_training
        lowerCamelCase      						=    batch_size
        lowerCamelCase      						=    image_size
        lowerCamelCase      						=    patch_size
        lowerCamelCase      						=    num_channels
        lowerCamelCase      						=    max_token_length
        lowerCamelCase      						=    num_character_labels
        lowerCamelCase      						=    num_bpe_labels
        lowerCamelCase      						=    num_wordpiece_labels
        lowerCamelCase      						=    hidden_size
        lowerCamelCase      						=    num_hidden_layers
        lowerCamelCase      						=    num_attention_heads
        lowerCamelCase      						=    mlp_ratio
        lowerCamelCase      						=    patch_embeds_hidden_size
        lowerCamelCase      						=    output_hidden_states

       def   lowerCamelCase__						(	self					:  Tuple     )   -> Optional[int]:







        '''simple docstring'''

        lowerCamelCase      						=    floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]     )
        lowerCamelCase      						=    self.get_config()
        return config, pixel_values

       def   lowerCamelCase__						(	self					:  List[str]     )   -> Optional[int]:







        '''simple docstring'''

        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					:  Optional[Any]						,	__snake_case					:  str						,	__snake_case					:  str     )   -> Optional[int]:







        '''simple docstring'''

        lowerCamelCase      						=    MgpstrForSceneTextRecognition(__snake_case     )
        model.to(__snake_case     )
        model.eval()
        with torch.no_grad():
         lowerCamelCase      						=    model(__snake_case     )
        self.parent.assertEqual(
            generated_ids[0][0].shape						,	(self.batch_size, self.max_token_length, self.num_character_labels)     )





       def   lowerCamelCase__						(	self					:  Tuple     )   -> Optional[Any]:







        '''simple docstring'''

        lowerCamelCase      						=    self.prepare_config_and_inputs()
        lowerCamelCase						,	lowerCamelCase      						=    config_and_inputs
        lowerCamelCase      						=    {'pixel_values': pixel_values}
        return config, inputs_dict


@require_torch
class     lowerCAmelCase				(    __UpperCamelCase   ,				__UpperCamelCase   ,				unittest.TestCase				):


       '''simple docstring'''







       snake_case    							=	(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
       snake_case    							=	{'feature-extraction': MgpstrForSceneTextRecognition} if is_torch_available() else {}
       snake_case    							=	False

       snake_case    							=	False
       snake_case    							=	False
       snake_case    							=	False
       snake_case    							=	False

       def   lowerCamelCase__						(	self					:  Dict     )   -> List[str]:







        '''simple docstring'''

        lowerCamelCase      						=    MgpstrModelTester(self     )
        lowerCamelCase      						=    ConfigTester(self						,	config_class=__snake_case						,	has_text_modality=__snake_case     )

       def   lowerCamelCase__						(	self					:  Optional[Any]     )   -> List[str]:







        '''simple docstring'''

        self.config_tester.run_common_tests()

       def   lowerCamelCase__						(	self					:  Optional[Any]     )   -> Optional[Any]:







        '''simple docstring'''

        lowerCamelCase      						=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__snake_case     )

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







        '''simple docstring'''

        pass

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







        '''simple docstring'''

        lowerCamelCase						,	lowerCamelCase      						=    self.model_tester.prepare_config_and_inputs_for_common()

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

       def   lowerCamelCase__						(	self					:  Tuple     )   -> Optional[int]:







        '''simple docstring'''

        lowerCamelCase						,	lowerCamelCase      						=    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCamelCase      						=    model_class(__snake_case     )
         lowerCamelCase      						=    inspect.signature(model.forward     )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCamelCase      						=    [*signature.parameters.keys()]

         lowerCamelCase      						=    ['pixel_values']
         self.assertListEqual(arg_names[:1]						,	__snake_case     )

       @unittest.skip(reason='MgpstrModel does not support feedforward chunking'     )
       def   lowerCamelCase__						(	self					:  Optional[Any]     )   -> List[Any]:







        '''simple docstring'''

        pass

       def   lowerCamelCase__						(	self					:  Dict     )   -> Union[str, Any]:







        '''simple docstring'''

        lowerCamelCase						,	lowerCamelCase      						=    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

         lowerCamelCase      						=    True
         lowerCamelCase      						=    model_class(__snake_case     )
         self.assertTrue(model.is_gradient_checkpointing     )

       def   lowerCamelCase__						(	self					:  str     )   -> Optional[int]:







        '''simple docstring'''

        def check_hidden_states_output(__snake_case					:  str						,	__snake_case					:  Optional[Any]						,	__snake_case					:  Dict     ):
         lowerCamelCase      						=    model_class(__snake_case     )
         model.to(__snake_case     )
         model.eval()

         with torch.no_grad():
          lowerCamelCase      						=    model(**self._prepare_for_class(__snake_case						,	__snake_case     )     )

         lowerCamelCase      						=    outputs.hidden_states

         lowerCamelCase      						=    getattr(
             self.model_tester						,	'expected_num_hidden_layers'						,	self.model_tester.num_hidden_layers + 1     )
         self.assertEqual(len(__snake_case     )						,	__snake_case     )

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

        lowerCamelCase						,	lowerCamelCase      						=    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCamelCase      						=    True
         check_hidden_states_output(__snake_case						,	__snake_case						,	__snake_case     )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCamelCase      						=    True

         check_hidden_states_output(__snake_case						,	__snake_case						,	__snake_case     )

       def   lowerCamelCase__						(	self					:  Optional[int]     )   -> Union[str, Any]:







        '''simple docstring'''

        lowerCamelCase						,	lowerCamelCase      						=    self.model_tester.prepare_config_and_inputs_for_common()

        lowerCamelCase      						=    _config_zero_init(__snake_case     )
        for model_class in self.all_model_classes:
         lowerCamelCase      						=    model_class(config=__snake_case     )
         for name, param in model.named_parameters():
          if isinstance(__snake_case						,	(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					:  int     )   -> List[Any]:







        '''simple docstring'''

        pass







def a_		(       )							->   Optional[Any]:







 """simple docstring"""

 lowerCamelCase      						=    'https://i.postimg.cc/ZKwLg2Gw/367-14.png'
 lowerCamelCase      						=    Image.open(requests.get(UpperCamelCase_   ,					stream=UpperCamelCase_  ).raw  ).convert('RGB'  )
 return im


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


       '''simple docstring'''







       @slow
       def   lowerCamelCase__						(	self					:  List[Any]     )   -> Dict:







        '''simple docstring'''

        lowerCamelCase      						=    'alibaba-damo/mgp-str-base'
        lowerCamelCase      						=    MgpstrForSceneTextRecognition.from_pretrained(__snake_case     ).to(__snake_case     )
        lowerCamelCase      						=    MgpstrProcessor.from_pretrained(__snake_case     )

        lowerCamelCase      						=    prepare_img()
        lowerCamelCase      						=    processor(images=__snake_case						,	return_tensors='pt'     ).pixel_values.to(__snake_case     )

        # forward pass
        with torch.no_grad():
         lowerCamelCase      						=    model(__snake_case     )

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

        lowerCamelCase      						=    processor.batch_decode(outputs.logits     )
        lowerCamelCase      						=    'ticket'

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

        lowerCamelCase      						=    torch.tensor(
            [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]						,	device=__snake_case						,	)

        self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]						,	__snake_case						,	atol=1e-4     )     )









def a_		(       UpperCamelCase_   :							int   ,					UpperCamelCase_   :							list  )							->   list:







 """simple docstring"""

 lowerCamelCase      						=    sorted(UpperCamelCase_   ,					key=lambda UpperCamelCase_  : value[2]   ,					reverse=UpperCamelCase_  )

 # Create a list of size equal to the maximum deadline
 # and initialize it with -1
 lowerCamelCase      						=    max(UpperCamelCase_   ,					key=lambda UpperCamelCase_  : value[1]  )[1]
 lowerCamelCase      						=    [-1] * max_deadline

 # Finding the maximum profit and the count of jobs
 lowerCamelCase      						=    0
 lowerCamelCase      						=    0
 for job in jobs:
  # Find a free time slot for this job
  # (Note that we start from the last possible slot)
  for i in range(job[1] - 1   ,					-1   ,					-1  ):
   if time_slots[i] == -1:
    lowerCamelCase      						=    job[0]
    count += 1
    max_profit += job[2]
    break
 return [count, max_profit]


if __name__ == "__main__":
     import doctest

     doctest.testmod()




