
"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TFDeiTForImageClassification,
       TFDeiTForImageClassificationWithTeacher,
       TFDeiTForMaskedImageModeling,
       TFDeiTModel,
   )
   from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DeiTImageProcessor

class 		a__						:
   def __init__(			self	,    _a	,    _a=13	,    _a=30	,    _a=2	,    _a=3	,    _a=True	,    _a=True	,    _a=32	,    _a=2	,    _a=4	,    _a=37	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=10	,    _a=0.0_2	,    _a=3	,    _a=None	,    _a=2	,    ):
          lowercase    :				List[str]       		=							parent
          lowercase    :				Optional[int]       		=							batch_size
          lowercase    :				Tuple       		=							image_size
          lowercase    :				str       		=							patch_size
          lowercase    :				Optional[int]       		=							num_channels
          lowercase    :				Any       		=							is_training
          lowercase    :				Tuple       		=							use_labels
          lowercase    :				List[Any]       		=							hidden_size
          lowercase    :				Optional[Any]       		=							num_hidden_layers
          lowercase    :				str       		=							num_attention_heads
          lowercase    :				int       		=							intermediate_size
          lowercase    :				int       		=							hidden_act
          lowercase    :				Dict       		=							hidden_dropout_prob
          lowercase    :				Optional[int]       		=							attention_probs_dropout_prob
          lowercase    :				str       		=							type_sequence_label_size
          lowercase    :				List[Any]       		=							initializer_range
          lowercase    :				Union[str, Any]       		=							scope
          lowercase    :				List[Any]       		=							encoder_stride

          # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
          lowercase    :				Any       		=							(image_size // patch_size) ** 2
          lowercase    :				List[str]       		=							num_patches + 2
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

          lowercase    :				List[str]       		=							None
          if self.use_labels:
                 lowercase    :				Dict       		=							ids_tensor([self.batch_size]	,    self.type_sequence_label_size				)

          lowercase    :				Tuple       		=							self.get_config()

          return config, pixel_values, labels
   def 			__magic_name__			(			self				):
          return DeiTConfig(
              image_size=self.image_size	,    patch_size=self.patch_size	,    num_channels=self.num_channels	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    is_decoder=_a	,    initializer_range=self.initializer_range	,    encoder_stride=self.encoder_stride	,    )
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				Any       		=							TFDeiTModel(config=_a				)
          lowercase    :				int       		=							model(_a				)
          self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				int       		=							TFDeiTForMaskedImageModeling(config=_a				)
          lowercase    :				Tuple       		=							model(_a				)
          self.parent.assertEqual(
              result.reconstruction.shape	,    (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

          # test greyscale images
          lowercase    :				int       		=							1
          lowercase    :				Optional[Any]       		=							TFDeiTForMaskedImageModeling(_a				)

          lowercase    :				Optional[int]       		=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          lowercase    :				Tuple       		=							model(_a				)
          self.parent.assertEqual(result.reconstruction.shape	,    (self.batch_size, 1, self.image_size, self.image_size)				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				Optional[int]       		=							self.type_sequence_label_size
          lowercase    :				int       		=							TFDeiTForImageClassification(_a				)
          lowercase    :				Union[str, Any]       		=							model(_a	,    labels=_a				)
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.type_sequence_label_size)				)

          # test greyscale images
          lowercase    :				Optional[int]       		=							1
          lowercase    :				List[str]       		=							TFDeiTForImageClassification(_a				)

          lowercase    :				str       		=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          lowercase    :				Optional[int]       		=							model(_a	,    labels=_a				)
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.type_sequence_label_size)				)

   def 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							self.prepare_config_and_inputs()
          lowercase		,    lowercase		,    lowercase    :				Dict       		=							config_and_inputs
          lowercase    :				List[Any]       		=							{"pixel_values": pixel_values}
          return config, inputs_dict


@require_tf
class 		a__						(      a_,			a_,			unittest.TestCase						):
   __lowerCAmelCase            =				(
       (
           TFDeiTModel,
           TFDeiTForImageClassification,
           TFDeiTForImageClassificationWithTeacher,
           TFDeiTForMaskedImageModeling,
       )
       if is_tf_available()
       else ()
   )
   __lowerCAmelCase            =				(
       {
           """feature-extraction""": TFDeiTModel,
           """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
       }
       if is_tf_available()
       else {}
   )

   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							TFDeiTModelTester(self				)
          lowercase    :				int       		=							ConfigTester(self	,    config_class=_a	,    has_text_modality=_a	,    hidden_size=37				)
   def 			__magic_name__			(			self				):
          self.config_tester.run_common_tests()
   @unittest.skip(reason="DeiT does not use inputs_embeds"				)
   def 			__magic_name__			(			self				):
          pass
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				str       		=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase    :				Optional[int]       		=							model_class(_a				)
                 self.assertIsInstance(model.get_input_embeddings()	,    (tf.keras.layers.Layer)				)
                 lowercase    :				Union[str, Any]       		=							model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(_a	,    tf.keras.layers.Dense				)				)
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				List[str]       		=							self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowercase    :				Union[str, Any]       		=							["pixel_values"]
                 self.assertListEqual(arg_names[:1]	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a=False				):
          lowercase    :				Tuple       		=							super()._prepare_for_class(_a	,    _a	,    return_labels=_a				)

          if return_labels:
                 if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call				).parameters:
                        del inputs_dict["labels"]

          return inputs_dict

   @slow
   def 			__magic_name__			(			self				):
          for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowercase    :				str       		=							TFDeiTModel.from_pretrained(_a				)
                 self.assertIsNotNone(_a				)


def   __magic_name__	(      ) ->				Tuple:
       lowercase    :				Dict       		=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
       return image

@require_tf
@require_vision
class 		a__						(      unittest.TestCase						):
   @cached_property
   def 			__magic_name__			(			self				):
          return (
              DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"				)
              if is_vision_available()
              else None
          )

   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"				)

          lowercase    :				List[Any]       		=							self.default_image_processor
          lowercase    :				Optional[int]       		=							prepare_img()
          lowercase    :				Optional[Any]       		=							image_processor(images=_a	,    return_tensors="tf"				)

          # forward pass
          lowercase    :				List[Any]       		=							model(**_a				)

          # verify the logits
          lowercase    :				Any       		=							tf.TensorShape((1, 1_000)				)
          self.assertEqual(outputs.logits.shape	,    _a				)

          lowercase    :				Dict       		=							tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]				)

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




"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class 		a__						(      a_						):
   def __lt__(			self	,    _a				):
          return self[-1] < other[-1]

   def __eq__(			self	,    _a				):
          return self[-1] == other[-1]


def   __magic_name__	(      __snake_case				:       list  ) ->				list:
       lowercase    :				list[Stack]       		=							[]
       # sort into stacks
       for element in collection:
              lowercase    :				Union[str, Any]       		=							Stack([element]  )
              lowercase    :				Dict       		=							bisect_left(__snake_case     , __snake_case  )
              if i != len(__snake_case  ):
                     stacks[i].append(__snake_case  )
              else:
                     stacks.append(__snake_case  )

    # use a heap-based merge to merge stack efficiently
       lowercase    :				Dict       		=							merge(*(reversed(__snake_case  ) for stack in stacks)  )
       return collection


if __name__ == "__main__":
   _A			:       Optional[int]   		=       input("""Enter numbers separated by a comma:\n""").strip()
   _A			:       Optional[Any]   		=       [int(item) for item in user_input.split(""",""")]
   print(patience_sort(unsorted))


