
'''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    UpperCAmelCase_				(	snake_case__					):





 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						self.config_class(**self.inputs_dict)
     self.parent.assertTrue(hasattr(__UpperCamelCase ,       "hidden_sizes"))
     self.parent.assertTrue(hasattr(__UpperCamelCase ,       "num_attention_heads"))






class    UpperCAmelCase_				:





 def __init__(    self ,       lowercase_ ,       lowercase_=13 ,       lowercase_=64 ,       lowercase_=3 ,       lowercase_=3 ,       lowercase_=2 ,       lowercase_=1 ,       lowercase_=16 ,       lowercase_=[1_28, 2_56, 3_84] ,       lowercase_=[4, 6, 8] ,       lowercase_=[2, 3, 4] ,       lowercase_=[16, 16, 16] ,       lowercase_=0 ,       lowercase_=[2, 2, 2] ,       lowercase_=[2, 2, 2] ,       lowercase_=0.02 ,       lowercase_=True ,       lowercase_=True ,       lowercase_=2 ,       ):
     snake_case_ :    Optional[Any]     			=						parent
     snake_case_ :    int     			=						batch_size
     snake_case_ :    Any     			=						image_size
     snake_case_ :    Union[str, Any]     			=						num_channels
     snake_case_ :    Optional[Any]     			=						kernel_size
     snake_case_ :    List[str]     			=						stride
     snake_case_ :    Optional[Any]     			=						padding
     snake_case_ :    Dict     			=						hidden_sizes
     snake_case_ :    List[Any]     			=						num_attention_heads
     snake_case_ :    List[Any]     			=						depths
     snake_case_ :    str     			=						key_dim
     snake_case_ :    Any     			=						drop_path_rate
     snake_case_ :    Dict     			=						patch_size
     snake_case_ :    Union[str, Any]     			=						attention_ratio
     snake_case_ :    Union[str, Any]     			=						mlp_ratio
     snake_case_ :    Optional[Any]     			=						initializer_range
     snake_case_ :    List[str]     			=						[
         ["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],
     ]
     snake_case_ :    List[Any]     			=						is_training
     snake_case_ :    int     			=						use_labels
     snake_case_ :    Optional[int]     			=						num_labels
     snake_case_ :    str     			=						initializer_range





 def        snake_case__		(    self):
     snake_case_ :    Tuple     			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

     snake_case_ :    str     			=						None
     if self.use_labels:
         snake_case_ :    Optional[int]     			=						ids_tensor([self.batch_size] ,       self.num_labels)

     snake_case_ :    List[str]     			=						self.get_config()
     return config, pixel_values, labels





 def        snake_case__		(    self):
     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        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_):
     snake_case_ :    Optional[int]     			=						LevitModel(config=__UpperCamelCase)
     model.to(__UpperCamelCase)
     model.eval()
     snake_case_ :    Union[str, Any]     			=						model(__UpperCamelCase)
     snake_case_ :    List[str]     			=						(self.image_size, self.image_size)
     snake_case_			, snake_case_ :    Any     			=						image_size[0], image_size[1]
     for _ in range(4):
         snake_case_ :    Tuple     			=						floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
         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        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_):
     snake_case_ :    Dict     			=						self.num_labels
     snake_case_ :    str     			=						LevitForImageClassification(__UpperCamelCase)
     model.to(__UpperCamelCase)
     model.eval()
     snake_case_ :    str     			=						model(__UpperCamelCase ,       labels=__UpperCamelCase)
     self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels))





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						self.prepare_config_and_inputs()
     snake_case_			, snake_case_			, snake_case_ :    Union[str, Any]     			=						config_and_inputs
     snake_case_ :    Optional[Any]     			=						{"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						(
     (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
     if is_torch_available()
     else ()
 )
 UpperCAmelCase_		    =						(
     {
         """feature-extraction""": LevitModel,
         """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						False





 def        snake_case__		(    self):
     snake_case_ :    str     			=						LevitModelTester(self)
     snake_case_ :    Tuple     			=						ConfigTester(self ,       config_class=__UpperCamelCase ,       has_text_modality=__UpperCamelCase ,       hidden_size=37)





 def        snake_case__		(    self):
     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        snake_case__		(    self):
     return





 @unittest.skip(reason="Levit does not use inputs_embeds")
 def        snake_case__		(    self):
     pass





 @unittest.skip(reason="Levit does not support input and output embeddings")
 def        snake_case__		(    self):
     pass





 @unittest.skip(reason="Levit does not output attentions")
 def        snake_case__		(    self):
     pass





 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Any     			=						self.model_tester.prepare_config_and_inputs_for_common()

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

         snake_case_ :    Union[str, Any]     			=						["pixel_values"]
         self.assertListEqual(arg_names[:1] ,       __UpperCamelCase)





 def        snake_case__		(    self):
     def check_hidden_states_output(lowercase_ ,       lowercase_ ,       lowercase_):
         snake_case_ :    int     			=						model_class(__UpperCamelCase)
         model.to(__UpperCamelCase)
         model.eval()

         with torch.no_grad():
             snake_case_ :    Tuple     			=						model(**self._prepare_for_class(__UpperCamelCase ,       __UpperCamelCase))

         snake_case_ :    Optional[int]     			=						outputs.hidden_states

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

         snake_case_ :    Optional[Any]     			=						(self.model_tester.image_size, self.model_tester.image_size)
         snake_case_			, snake_case_ :    Optional[Any]     			=						image_size[0], image_size[1]
         for _ in range(4):
             snake_case_ :    Optional[Any]     			=						floor(
                 (
                     (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                     / self.model_tester.stride
                 )
                 + 1)
             snake_case_ :    Optional[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],
             ] ,       )

     snake_case_			, snake_case_ :    Optional[Any]     			=						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         snake_case_ :    int     			=						True
         check_hidden_states_output(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase)

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

         check_hidden_states_output(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase)





 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
 def        snake_case__		(    self):
     pass





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_=False):
     snake_case_ :    Tuple     			=						super()._prepare_for_class(__UpperCamelCase ,       __UpperCamelCase ,       return_labels=__UpperCamelCase)

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

     return inputs_dict





 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__UpperCamelCase)





 def        snake_case__		(    self):
     snake_case_ :    str     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase)





 def        snake_case__		(    self):
     if not self.model_tester.is_training:
         return

     snake_case_			, snake_case_ :    List[str]     			=						self.model_tester.prepare_config_and_inputs_for_common()
     snake_case_ :    Dict     			=						True

     for model_class in self.all_model_classes:
         # LevitForImageClassificationWithTeacher supports inference-only
         if (
             model_class in get_values(__UpperCamelCase)
             or model_class.__name__ == "LevitForImageClassificationWithTeacher"
         ):
             continue
         snake_case_ :    Optional[Any]     			=						model_class(__UpperCamelCase)
         model.to(__UpperCamelCase)
         model.train()
         snake_case_ :    Union[str, Any]     			=						self._prepare_for_class(__UpperCamelCase ,       __UpperCamelCase ,       return_labels=__UpperCamelCase)
         snake_case_ :    str     			=						model(**__UpperCamelCase).loss
         loss.backward()





 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    str     			=						self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
         return

     snake_case_ :    Union[str, Any]     			=						False
     snake_case_ :    Any     			=						True

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





 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Dict     			=						self.model_tester.prepare_config_and_inputs_for_common()

     snake_case_ :    Union[str, 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(__UpperCamelCase),
             ]
             or model_class.__name__ == "LevitForImageClassificationWithTeacher"
         ):
             continue

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

                 snake_case_ :    List[Any]     			=						model_class(__UpperCamelCase)
                 model.to(__UpperCamelCase)
                 model.train()

                 snake_case_ :    Dict     			=						self._prepare_for_class(__UpperCamelCase ,       __UpperCamelCase ,       return_labels=__UpperCamelCase)

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

                 snake_case_ :    int     			=						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=__UpperCamelCase) as warning_list:
                     snake_case_ :    Any     			=						model(**__UpperCamelCase).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        snake_case__		(    self):
     for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_ :    Optional[int]     			=						LevitModel.from_pretrained(__UpperCamelCase)
         self.assertIsNotNone(__UpperCamelCase)


def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image






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





 @cached_property
 def        snake_case__		(    self):
     return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])





 @slow
 def        snake_case__		(    self):
     snake_case_ :    Tuple     			=						LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
         __UpperCamelCase)

     snake_case_ :    List[str]     			=						self.default_image_processor
     snake_case_ :    Optional[int]     			=						prepare_img()
     snake_case_ :    Optional[Any]     			=						image_processor(images=__UpperCamelCase ,       return_tensors="pt").to(__UpperCamelCase)

     # forward pass
     with torch.no_grad():
         snake_case_ :    Optional[Any]     			=						model(**__UpperCamelCase)

     # verify the logits
     snake_case_ :    Optional[int]     			=						torch.Size((1, 10_00))
     self.assertEqual(outputs.logits.shape ,       __UpperCamelCase)

     snake_case_ :    str     			=						torch.tensor([1.0_448, -0.3_745, -1.8_317]).to(__UpperCamelCase)

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






'''simple docstring'''
import doctest
from collections import deque

import numpy as np






class    UpperCAmelCase_				:





 def __init__(    self):
     snake_case_ :    List[Any]     			=						[2, 1, 2, -1]
     snake_case_ :    int     			=						[1, 2, 3, 4]





 def        snake_case__		(    self):
     snake_case_ :    str     			=						len(self.first_signal)
     snake_case_ :    Any     			=						len(self.second_signal)

     snake_case_ :    List[Any]     			=						max(lowercase_ ,       lowercase_)

     # create a zero matrix of max_length x max_length
     snake_case_ :    Dict     			=						[[0] * max_length for i in range(lowercase_)]

     # fills the smaller signal with zeros to make both signals of same length
     if length_first_signal < length_second_signal:
         self.first_signal += [0] * (max_length - length_first_signal)
     elif length_first_signal > length_second_signal:
         self.second_signal += [0] * (max_length - length_second_signal)
     for i in range(lowercase_):
         snake_case_ :    List[str]     			=						deque(self.second_signal)
         rotated_signal.rotate(lowercase_)
         for j, item in enumerate(lowercase_):
             matrix[i][j] += item

        # multiply the matrix with the first signal
     snake_case_ :    Any     			=						np.matmul(np.transpose(lowercase_) ,       np.transpose(self.first_signal))

     # rounding-off to two decimal places
     return [round(lowercase_ ,       2) for i in final_signal]


if __name__ == "__main__":
  doctest.testmod()




