

'''simple docstring'''

import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_MAPPING,
        LevitForImageClassification,
        LevitForImageClassificationWithTeacher,
        LevitModel,
    )
    from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import LevitImageProcessor





class 				_snake_case			(							_a    ):
   def      __UpperCamelCase    (			self      :   List[Any]				):
     SCREAMING_SNAKE_CASE:List[str]       =       self.config_class(**self.inputs_dict				)
     self.parent.assertTrue(hasattr(lowercase__  ,"hidden_sizes"				)				)
     self.parent.assertTrue(hasattr(lowercase__  ,"num_attention_heads"				)				)





class 				_snake_case			:
   def __init__(			self      :   Optional[int]  ,SCREAMING_SNAKE_CASE__      :   Dict  ,SCREAMING_SNAKE_CASE__      :   int=13  ,SCREAMING_SNAKE_CASE__      :   Dict=64  ,SCREAMING_SNAKE_CASE__      :   List[Any]=3  ,SCREAMING_SNAKE_CASE__      :   Optional[int]=3  ,SCREAMING_SNAKE_CASE__      :   List[str]=2  ,SCREAMING_SNAKE_CASE__      :   str=1  ,SCREAMING_SNAKE_CASE__      :   Tuple=16  ,SCREAMING_SNAKE_CASE__      :   str=[128, 256, 384]  ,SCREAMING_SNAKE_CASE__      :   Any=[4, 6, 8]  ,SCREAMING_SNAKE_CASE__      :   List[Any]=[2, 3, 4]  ,SCREAMING_SNAKE_CASE__      :   List[Any]=[16, 16, 16]  ,SCREAMING_SNAKE_CASE__      :   List[str]=0  ,SCREAMING_SNAKE_CASE__      :   Tuple=[2, 2, 2]  ,SCREAMING_SNAKE_CASE__      :   Union[str, Any]=[2, 2, 2]  ,SCREAMING_SNAKE_CASE__      :   List[Any]=0.02  ,SCREAMING_SNAKE_CASE__      :   List[str]=True  ,SCREAMING_SNAKE_CASE__      :   List[str]=True  ,SCREAMING_SNAKE_CASE__      :   str=2  ,):
     SCREAMING_SNAKE_CASE:List[Any]       =       parent
     SCREAMING_SNAKE_CASE:Any       =       batch_size
     SCREAMING_SNAKE_CASE:str       =       image_size
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       num_channels
     SCREAMING_SNAKE_CASE:str       =       kernel_size
     SCREAMING_SNAKE_CASE:List[Any]       =       stride
     SCREAMING_SNAKE_CASE:List[Any]       =       padding
     SCREAMING_SNAKE_CASE:List[str]       =       hidden_sizes
     SCREAMING_SNAKE_CASE:List[Any]       =       num_attention_heads
     SCREAMING_SNAKE_CASE:Tuple       =       depths
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       key_dim
     SCREAMING_SNAKE_CASE:List[Any]       =       drop_path_rate
     SCREAMING_SNAKE_CASE:Any       =       patch_size
     SCREAMING_SNAKE_CASE:int       =       attention_ratio
     SCREAMING_SNAKE_CASE:List[Any]       =       mlp_ratio
     SCREAMING_SNAKE_CASE:Any       =       initializer_range
     SCREAMING_SNAKE_CASE:Tuple       =       [
         ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
         ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
     ]
     SCREAMING_SNAKE_CASE:str       =       is_training
     SCREAMING_SNAKE_CASE:List[Any]       =       use_labels
     SCREAMING_SNAKE_CASE:str       =       num_labels
     SCREAMING_SNAKE_CASE:Optional[Any]       =       initializer_range
   def      __UpperCamelCase    (			self      :   Optional[int]				):
     SCREAMING_SNAKE_CASE:Dict       =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

     SCREAMING_SNAKE_CASE:List[str]       =       None
     if self.use_labels:
       SCREAMING_SNAKE_CASE:List[str]       =       ids_tensor([self.batch_size]  ,self.num_labels				)

     SCREAMING_SNAKE_CASE:List[Any]       =       self.get_config()
     return config, pixel_values, labels
   def      __UpperCamelCase    (			self      :   Optional[int]				):
     return LevitConfig(
         image_size=self.image_size  ,num_channels=self.num_channels  ,kernel_size=self.kernel_size  ,stride=self.stride  ,padding=self.padding  ,patch_size=self.patch_size  ,hidden_sizes=self.hidden_sizes  ,num_attention_heads=self.num_attention_heads  ,depths=self.depths  ,key_dim=self.key_dim  ,drop_path_rate=self.drop_path_rate  ,mlp_ratio=self.mlp_ratio  ,attention_ratio=self.attention_ratio  ,initializer_range=self.initializer_range  ,down_ops=self.down_ops  ,)
   def      __UpperCamelCase    (			self      :   Tuple  ,SCREAMING_SNAKE_CASE__      :   Tuple  ,SCREAMING_SNAKE_CASE__      :   List[Any]  ,SCREAMING_SNAKE_CASE__      :   Tuple				):
     SCREAMING_SNAKE_CASE:Any       =       LevitModel(config=lowercase__				)
     model.to(lowercase__				)
     model.eval()
     SCREAMING_SNAKE_CASE:Dict       =       model(lowercase__				)
     SCREAMING_SNAKE_CASE:Dict       =       (self.image_size, self.image_size)
     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:str       =       image_size[0], image_size[1]
     for _ in range(4				):
       SCREAMING_SNAKE_CASE:str       =       floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
       SCREAMING_SNAKE_CASE:Tuple       =       floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
     self.parent.assertEqual(
         result.last_hidden_state.shape  ,(self.batch_size, ceil(height / 4				) * ceil(width / 4				), self.hidden_sizes[-1])  ,)
   def      __UpperCamelCase    (			self      :   Optional[Any]  ,SCREAMING_SNAKE_CASE__      :   List[str]  ,SCREAMING_SNAKE_CASE__      :   List[str]  ,SCREAMING_SNAKE_CASE__      :   Optional[int]				):
     SCREAMING_SNAKE_CASE:Optional[Any]       =       self.num_labels
     SCREAMING_SNAKE_CASE:List[str]       =       LevitForImageClassification(lowercase__				)
     model.to(lowercase__				)
     model.eval()
     SCREAMING_SNAKE_CASE:Optional[Any]       =       model(lowercase__  ,labels=lowercase__				)
     self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)				)







   def      __UpperCamelCase    (			self      :   Tuple				):
     SCREAMING_SNAKE_CASE:int       =       self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:str       =       config_and_inputs
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       {"pixel_values": pixel_values}
     return config, inputs_dict





@require_torch
class 				_snake_case			(							_a						,	_a						,	unittest.TestCase    ):
   _A							:  Union[str, Any]      =       (
       (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
       if is_torch_available()
       else ()
   )
   _A							:  Union[str, Any]      =       (
       {
           '''feature-extraction''': LevitModel,
           '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
       }
       if is_torch_available()
       else {}
   )

   _A							:  Any      =       False
   _A							:  List[str]      =       False
   _A							:  List[Any]      =       False
   _A							:  Dict      =       False
   _A							:  Optional[Any]      =       False
   def      __UpperCamelCase    (			self      :   Optional[Any]				):
     SCREAMING_SNAKE_CASE:List[Any]       =       LevitModelTester(self				)
     SCREAMING_SNAKE_CASE:List[Any]       =       ConfigTester(self  ,config_class=lowercase__  ,has_text_modality=lowercase__  ,hidden_size=37				)
   def      __UpperCamelCase    (			self      :   List[str]				):
     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()
   def      __UpperCamelCase    (			self      :   str				):
     return
   @unittest.skip(reason="Levit does not use inputs_embeds"				)
   def      __UpperCamelCase    (			self      :   Tuple				):
     pass
   @unittest.skip(reason="Levit does not support input and output embeddings"				)
   def      __UpperCamelCase    (			self      :   Tuple				):
     pass
   @unittest.skip(reason="Levit does not output attentions"				)
   def      __UpperCamelCase    (			self      :   Optional[int]				):
     pass
   def      __UpperCamelCase    (			self      :   Tuple				):
     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:List[Any]       =       self.model_tester.prepare_config_and_inputs_for_common()

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

       SCREAMING_SNAKE_CASE:Optional[int]       =       ["pixel_values"]
       self.assertListEqual(arg_names[:1]  ,lowercase__				)
   def      __UpperCamelCase    (			self      :   int				):
     def check_hidden_states_output(SCREAMING_SNAKE_CASE__      :   Optional[Any]  ,SCREAMING_SNAKE_CASE__      :   str  ,SCREAMING_SNAKE_CASE__      :   Tuple				):
       SCREAMING_SNAKE_CASE:int       =       model_class(lowercase__				)
       model.to(lowercase__				)
       model.eval()

       with torch.no_grad():
         SCREAMING_SNAKE_CASE:int       =       model(**self._prepare_for_class(lowercase__  ,lowercase__				)				)

       SCREAMING_SNAKE_CASE:Any       =       outputs.hidden_states

       SCREAMING_SNAKE_CASE:List[Any]       =       len(self.model_tester.depths				) + 1
       self.assertEqual(len(lowercase__				)  ,lowercase__				)

       SCREAMING_SNAKE_CASE:Dict       =       (self.model_tester.image_size, self.model_tester.image_size)
       SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:str       =       image_size[0], image_size[1]
       for _ in range(4				):
         SCREAMING_SNAKE_CASE:Dict       =       floor(
             (
                 (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                 / self.model_tester.stride
             )
             + 1				)
         SCREAMING_SNAKE_CASE:int       =       floor(
             (
                 (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                 / self.model_tester.stride
             )
             + 1				)
       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-2:]				)  ,[
               height * width,
               self.model_tester.hidden_sizes[0],
           ]  ,)

     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:int       =       self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE:Any       =       True
       check_hidden_states_output(lowercase__  ,lowercase__  ,lowercase__				)

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

       check_hidden_states_output(lowercase__  ,lowercase__  ,lowercase__				)
   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."				)
   def      __UpperCamelCase    (			self      :   str				):
     pass
   def      __UpperCamelCase    (			self      :   int  ,SCREAMING_SNAKE_CASE__      :   List[Any]  ,SCREAMING_SNAKE_CASE__      :   Optional[Any]  ,SCREAMING_SNAKE_CASE__      :   Union[str, Any]=False				):
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       super()._prepare_for_class(lowercase__  ,lowercase__  ,return_labels=lowercase__				)

     if return_labels:
       if model_class.__name__ == "LevitForImageClassificationWithTeacher":
         del inputs_dict["labels"]

     return inputs_dict
   def      __UpperCamelCase    (			self      :   int				):
     SCREAMING_SNAKE_CASE:Dict       =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase__				)
   def      __UpperCamelCase    (			self      :   List[Any]				):
     SCREAMING_SNAKE_CASE:str       =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase__				)
   def      __UpperCamelCase    (			self      :   str				):
     if not self.model_tester.is_training:
       return

     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:Tuple       =       self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE:List[Any]       =       True

     for model_class in self.all_model_classes:
       # LevitForImageClassificationWithTeacher supports inference-only
       if (
           model_class in get_values(lowercase__				)
           or model_class.__name__ == "LevitForImageClassificationWithTeacher"
       ):
         continue
       SCREAMING_SNAKE_CASE:Dict       =       model_class(lowercase__				)
       model.to(lowercase__				)
       model.train()
       SCREAMING_SNAKE_CASE:int       =       self._prepare_for_class(lowercase__  ,lowercase__  ,return_labels=lowercase__				)
       SCREAMING_SNAKE_CASE:Optional[Any]       =       model(**lowercase__				).loss
       loss.backward()
   def      __UpperCamelCase    (			self      :   str				):
     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:List[Any]       =       self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
       return

     SCREAMING_SNAKE_CASE:Optional[int]       =       False
     SCREAMING_SNAKE_CASE:Optional[Any]       =       True

     for model_class in self.all_model_classes:
       if model_class in get_values(lowercase__				) or not model_class.supports_gradient_checkpointing:
         continue
       # LevitForImageClassificationWithTeacher supports inference-only
       if model_class.__name__ == "LevitForImageClassificationWithTeacher":
         continue
       SCREAMING_SNAKE_CASE:Tuple       =       model_class(lowercase__				)
       model.gradient_checkpointing_enable()
       model.to(lowercase__				)
       model.train()
       SCREAMING_SNAKE_CASE:Union[str, Any]       =       self._prepare_for_class(lowercase__  ,lowercase__  ,return_labels=lowercase__				)
       SCREAMING_SNAKE_CASE:List[str]       =       model(**lowercase__				).loss
       loss.backward()
   def      __UpperCamelCase    (			self      :   List[Any]				):
     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:Any       =       self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE:List[Any]       =       [
         {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
         {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
         {"title": "regression", "num_labels": 1, "dtype": torch.float},
     ]

     for model_class in self.all_model_classes:
       if (
           model_class
           not in [
               *get_values(lowercase__				),
           ]
           or model_class.__name__ == "LevitForImageClassificationWithTeacher"
       ):
         continue

       for problem_type in problem_types:
         with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}'''				):
           SCREAMING_SNAKE_CASE:List[Any]       =       problem_type["title"]
           SCREAMING_SNAKE_CASE:Dict       =       problem_type["num_labels"]

           SCREAMING_SNAKE_CASE:Optional[Any]       =       model_class(lowercase__				)
           model.to(lowercase__				)
           model.train()

           SCREAMING_SNAKE_CASE:Tuple       =       self._prepare_for_class(lowercase__  ,lowercase__  ,return_labels=lowercase__				)

           if problem_type["num_labels"] > 1:
             SCREAMING_SNAKE_CASE:str       =       inputs["labels"].unsqueeze(1				).repeat(1  ,problem_type["num_labels"]				)

           SCREAMING_SNAKE_CASE:Tuple       =       inputs["labels"].to(problem_type["dtype"]				)

           # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
           # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
           # they have the same size." which is a symptom something in wrong for the regression problem.
           # See https://github.com/huggingface/transformers/issues/11780
           with warnings.catch_warnings(record=lowercase__				) as warning_list:
             SCREAMING_SNAKE_CASE:List[str]       =       model(**lowercase__				).loss
           for w in warning_list:
             if "Using a target size that is different to the input size" in str(w.message				):
               raise ValueError(
                   F'''Something is going wrong in the regression problem: intercepted {w.message}'''				)

           loss.backward()







   @slow
   def      __UpperCamelCase    (			self      :   Union[str, Any]				):
     for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       SCREAMING_SNAKE_CASE:Any       =       LevitModel.from_pretrained(lowercase__				)
       self.assertIsNotNone(lowercase__				)





def  A_	(			):
  SCREAMING_SNAKE_CASE:List[str]       =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
  return image





@require_torch
@require_vision
class 				_snake_case			(							unittest.TestCase    ):
   @cached_property
   def      __UpperCamelCase    (			self      :   int				):
     return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)







   @slow
   def      __UpperCamelCase    (			self      :   int				):
     SCREAMING_SNAKE_CASE:Any       =       LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(
         lowercase__				)

     SCREAMING_SNAKE_CASE:int       =       self.default_image_processor
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       prepare_img()
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       image_processor(images=lowercase__  ,return_tensors="pt"				).to(lowercase__				)

     # forward pass
     with torch.no_grad():
       SCREAMING_SNAKE_CASE:List[Any]       =       model(**lowercase__				)

     # verify the logits
     SCREAMING_SNAKE_CASE:Any       =       torch.Size((1, 1_000)				)
     self.assertEqual(outputs.logits.shape  ,lowercase__				)

     SCREAMING_SNAKE_CASE:Dict       =       torch.tensor([1.0_448, -0.3_745, -1.8_317]				).to(lowercase__				)

     self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,lowercase__  ,atol=1e-4				)				)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase:	List[Any]       			=		logging.get_logger(__name__)

_lowercase:	int       			=		{
    '''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json''',
}



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							="mgp-str"





			def __init__(       self	:	int  ,  lowercase__	:	List[Any]=[32, 1_28]  ,  lowercase__	:	str=4  ,  lowercase__	:	List[str]=3  ,  lowercase__	:	Tuple=27  ,  lowercase__	:	Optional[Any]=38  ,  lowercase__	:	Optional[Any]=5_02_57  ,  lowercase__	:	Dict=3_05_22  ,  lowercase__	:	Dict=7_68  ,  lowercase__	:	Tuple=12  ,  lowercase__	:	int=12  ,  lowercase__	:	int=4.0  ,  lowercase__	:	Tuple=True  ,  lowercase__	:	List[Any]=False  ,  lowercase__	:	int=1e-5  ,  lowercase__	:	int=0.0  ,  lowercase__	:	Optional[Any]=0.0  ,  lowercase__	:	List[Any]=0.0  ,  lowercase__	:	Union[str, Any]=False  ,  lowercase__	:	str=0.0_2  ,  **lowercase__	:	Optional[int]  ,  ):
										super().__init__(**lowercase__						)

										_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							=							distilled
										_lowerCAmelCase							=							layer_norm_eps
										_lowerCAmelCase							=							drop_rate
										_lowerCAmelCase							=							qkv_bias
										_lowerCAmelCase							=							attn_drop_rate
										_lowerCAmelCase							=							drop_path_rate
										_lowerCAmelCase							=							output_aa_attentions
										_lowerCAmelCase							=							initializer_range






