import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

  if is_vision_available():
    from transformers import MaskFormerImageProcessor

if is_vision_available():
  from PIL import Image




class 			__magic_name__	:





   """simple docstring"""




   def __init__(						self       :	List[Any]		,	_lowercase       :	List[Any]		,	_lowercase       :	Optional[Any]=2		,	_lowercase       :	Tuple=True		,	_lowercase       :	Optional[int]=False		,	_lowercase       :	Dict=10		,	_lowercase       :	Dict=3		,	_lowercase       :	List[str]=32 * 4		,	_lowercase       :	Union[str, Any]=32 * 6		,	_lowercase       :	List[Any]=4		,	_lowercase       :	Union[str, Any]=32		,	):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			parent
    _UpperCamelCase:  List[str]    						=			batch_size
    _UpperCamelCase:  Union[str, Any]    						=			is_training
    _UpperCamelCase:  int    						=			use_auxiliary_loss
    _UpperCamelCase:  Optional[int]    						=			num_queries
    _UpperCamelCase:  int    						=			num_channels
    _UpperCamelCase:  Dict    						=			min_size
    _UpperCamelCase:  List[Any]    						=			max_size
    _UpperCamelCase:  Any    						=			num_labels
    _UpperCamelCase:  str    						=			mask_feature_size



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]				).to(
        _lowercase				)

    _UpperCamelCase:  Union[str, Any]    						=			torch.ones([self.batch_size, self.min_size, self.max_size]		,	device=_lowercase				)

    _UpperCamelCase:  Tuple    						=			(
        torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]		,	device=_lowercase				) > 0.5
    ).float()
    _UpperCamelCase:  Optional[Any]    						=			(torch.rand((self.batch_size, self.num_labels)		,	device=_lowercase				) > 0.5).long()

    _UpperCamelCase:  List[Any]    						=			self.get_config()
    return config, pixel_values, pixel_mask, mask_labels, class_labels



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    return MaskFormerConfig.from_backbone_and_decoder_configs(
        backbone_config=SwinConfig(
            depths=[1, 1, 1, 1]		,	)		,	decoder_config=DetrConfig(
            decoder_ffn_dim=128		,	num_queries=self.num_queries		,	decoder_attention_heads=2		,	d_model=self.mask_feature_size		,	)		,	mask_feature_size=self.mask_feature_size		,	fpn_feature_size=self.mask_feature_size		,	num_channels=self.num_channels		,	num_labels=self.num_labels		,	)



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			self.prepare_config_and_inputs()
    _UpperCamelCase:  Any    						=			{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
    return config, inputs_dict



   def 	lowerCAmelCase    (						self       :	Union[str, Any]		,	_lowercase       :	Optional[int]		,	_lowercase       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			output.encoder_hidden_states
    _UpperCamelCase:  Tuple    						=			output.pixel_decoder_hidden_states
    _UpperCamelCase:  Dict    						=			output.transformer_decoder_hidden_states

    self.parent.assertTrue(len(_lowercase				)		,	len(config.backbone_config.depths				)				)
    self.parent.assertTrue(len(_lowercase				)		,	len(config.backbone_config.depths				)				)
    self.parent.assertTrue(len(_lowercase				)		,	config.decoder_config.decoder_layers				)



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	Optional[Any]		,	_lowercase       :	Optional[int]		,	_lowercase       :	List[Any]		,	_lowercase       :	Optional[int]=False				):



    """simple docstring"""



    with torch.no_grad():
     _UpperCamelCase:  Optional[int]    						=			MaskFormerModel(config=_lowercase				)
     model.to(_lowercase				)
     model.eval()

     _UpperCamelCase:  Dict    						=			model(pixel_values=_lowercase		,	pixel_mask=_lowercase				)
     _UpperCamelCase:  Optional[Any]    						=			model(_lowercase		,	output_hidden_states=_lowercase				)
    # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
    # encoder and pixel decoder
    self.parent.assertEqual(
        output.transformer_decoder_last_hidden_state.shape		,	(self.batch_size, self.num_queries, self.mask_feature_size)		,	)
    # let's ensure the other two hidden state exists
    self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None				)
    self.parent.assertTrue(output.encoder_last_hidden_state is not None				)

    if output_hidden_states:
     self.check_output_hidden_state(_lowercase		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	str		,	_lowercase       :	Any		,	_lowercase       :	Optional[int]		,	_lowercase       :	Optional[Any]		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			MaskFormerForInstanceSegmentation(config=_lowercase				)
    model.to(_lowercase				)
    model.eval()

    def comm_check_on_output(_lowercase       :	Optional[Any]				):
     # let's still check that all the required stuff is there
     self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None				)
     self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None				)
     self.parent.assertTrue(result.encoder_last_hidden_state is not None				)
     # okay, now we need to check the logits shape
     # due to the encoder compression, masks have a //4 spatial size
     self.parent.assertEqual(
         result.masks_queries_logits.shape		,	(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)		,	)
     # + 1 for null class
     self.parent.assertEqual(
         result.class_queries_logits.shape		,	(self.batch_size, self.num_queries, self.num_labels + 1)				)

    with torch.no_grad():
     _UpperCamelCase:  Optional[Any]    						=			model(pixel_values=_lowercase		,	pixel_mask=_lowercase				)
     _UpperCamelCase:  Union[str, Any]    						=			model(_lowercase				)

     comm_check_on_output(_lowercase				)

     _UpperCamelCase:  Union[str, Any]    						=			model(
         pixel_values=_lowercase		,	pixel_mask=_lowercase		,	mask_labels=_lowercase		,	class_labels=_lowercase				)

    comm_check_on_output(_lowercase				)

    self.parent.assertTrue(result.loss is not None				)
    self.parent.assertEqual(result.loss.shape		,	torch.Size([1]				)				)




@require_torch
class 			__magic_name__	(		__a   ,						__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Union[str, Any]	      = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
   lowerCAmelCase   :     Any	      = (
       {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
       if is_torch_available()
       else {}
   )

   lowerCAmelCase   :     List[Any]	      = False
   lowerCAmelCase   :     int	      = False
   lowerCAmelCase   :     str	      = False
   lowerCAmelCase   :     str	      = False



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			MaskFormerModelTester(self				)
    _UpperCamelCase:  List[Any]    						=			ConfigTester(self		,	config_class=_lowercase		,	has_text_modality=_lowercase				)



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    self.config_tester.run_common_tests()



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  Any    						=			self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.create_and_check_maskformer_model(_lowercase		,	**_lowercase		,	output_hidden_states=_lowercase				)



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowercase				)



   @unittest.skip(reason='''MaskFormer does not use inputs_embeds'''				)
   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    pass



   @unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''				)
   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    pass



   @unittest.skip(reason='''MaskFormer is not a generative model'''				)
   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    pass



   @unittest.skip(reason='''MaskFormer does not use token embeddings'''				)
   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    pass



   @require_torch_multi_gpu
   @unittest.skip(
       reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''				)
   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    pass



   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			self.model_tester.prepare_config_and_inputs_for_common()

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

     _UpperCamelCase:  Union[str, Any]    						=			['''pixel_values''']
     self.assertListEqual(arg_names[:1]		,	_lowercase				)



   @slow
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    for model_name in ["facebook/maskformer-swin-small-coco"]:
     _UpperCamelCase:  List[Any]    						=			MaskFormerModel.from_pretrained(_lowercase				)
     self.assertIsNotNone(_lowercase				)



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			(self.model_tester.min_size,) * 2
    _UpperCamelCase:  List[str]    						=			{
        '''pixel_values''': torch.randn((2, 3, *size)		,	device=_lowercase				),
        '''mask_labels''': torch.randn((2, 10, *size)		,	device=_lowercase				),
        '''class_labels''': torch.zeros(2		,	10		,	device=_lowercase				).long(),
    }

    _UpperCamelCase:  Optional[Any]    						=			MaskFormerForInstanceSegmentation(MaskFormerConfig()				).to(_lowercase				)
    _UpperCamelCase:  int    						=			model(**_lowercase				)
    self.assertTrue(outputs.loss is not None				)



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  Union[str, Any]    						=			self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.create_and_check_maskformer_model(_lowercase		,	**_lowercase		,	output_hidden_states=_lowercase				)



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  Any    						=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     _UpperCamelCase:  List[str]    						=			model_class(_lowercase				).to(_lowercase				)
     _UpperCamelCase:  int    						=			model(**_lowercase		,	output_attentions=_lowercase				)
     self.assertTrue(outputs.attentions is not None				)



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    if not self.model_tester.is_training:
     return
    # only MaskFormerForInstanceSegmentation has the loss
    _UpperCamelCase:  Tuple    						=			self.all_model_classes[1]
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Union[str, Any]    						=			self.model_tester.prepare_config_and_inputs()

    _UpperCamelCase:  str    						=			model_class(_lowercase				)
    model.to(_lowercase				)
    model.train()

    _UpperCamelCase:  Union[str, Any]    						=			model(_lowercase		,	mask_labels=_lowercase		,	class_labels=_lowercase				).loss
    loss.backward()



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			self.all_model_classes[1]
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  str    						=			self.model_tester.prepare_config_and_inputs()
    _UpperCamelCase:  List[Any]    						=			True
    _UpperCamelCase:  Union[str, Any]    						=			True

    _UpperCamelCase:  List[str]    						=			model_class(_lowercase				)
    model.to(_lowercase				)
    model.train()

    _UpperCamelCase:  Optional[Any]    						=			model(_lowercase		,	mask_labels=_lowercase		,	class_labels=_lowercase				)

    _UpperCamelCase:  Dict    						=			outputs.encoder_hidden_states[0]
    encoder_hidden_states.retain_grad()

    _UpperCamelCase:  Optional[Any]    						=			outputs.pixel_decoder_hidden_states[0]
    pixel_decoder_hidden_states.retain_grad()
    # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
    _UpperCamelCase:  Optional[Any]    						=			outputs.transformer_decoder_hidden_states[0]
    transformer_decoder_hidden_states.retain_grad()

    _UpperCamelCase:  Tuple    						=			outputs.attentions[0]
    attentions.retain_grad()

    outputs.loss.backward(retain_graph=_lowercase				)

    self.assertIsNotNone(encoder_hidden_states.grad				)
    self.assertIsNotNone(pixel_decoder_hidden_states.grad				)
    self.assertIsNotNone(transformer_decoder_hidden_states.grad				)
    self.assertIsNotNone(attentions.grad				)




UpperCAmelCase_           =				1E-4




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







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
 return image





@require_vision
@slow
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   @cached_property
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    return (
        MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''				)
        if is_vision_available()
        else None
    )



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''				).to(_lowercase				)
    _UpperCamelCase:  Any    						=			self.default_image_processor
    _UpperCamelCase:  Optional[int]    						=			prepare_img()
    _UpperCamelCase:  Optional[Any]    						=			image_processor(_lowercase		,	return_tensors='''pt'''				).to(_lowercase				)
    _UpperCamelCase:  List[str]    						=			inputs['''pixel_values'''].shape
    # check size is divisible by 32
    self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
    # check size
    self.assertEqual(_lowercase		,	(1, 3, 800, 1_088)				)

    with torch.no_grad():
     _UpperCamelCase:  Tuple    						=			model(**_lowercase				)

    _UpperCamelCase:  int    						=			torch.tensor(
        [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]				).to(_lowercase				)
    self.assertTrue(
        torch.allclose(
            outputs.encoder_last_hidden_state[0, 0, :3, :3]		,	_lowercase		,	atol=_lowercase				)				)

    _UpperCamelCase:  int    						=			torch.tensor(
        [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]				).to(_lowercase				)
    self.assertTrue(
        torch.allclose(
            outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]		,	_lowercase		,	atol=_lowercase				)				)

    _UpperCamelCase:  Union[str, Any]    						=			torch.tensor(
        [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]				).to(_lowercase				)
    self.assertTrue(
        torch.allclose(
            outputs.transformer_decoder_last_hidden_state[0, :3, :3]		,	_lowercase		,	atol=_lowercase				)				)



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			(
        MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''				)
        .to(_lowercase				)
        .eval()
    )
    _UpperCamelCase:  List[Any]    						=			self.default_image_processor
    _UpperCamelCase:  Dict    						=			prepare_img()
    _UpperCamelCase:  Optional[int]    						=			image_processor(_lowercase		,	return_tensors='''pt'''				).to(_lowercase				)
    _UpperCamelCase:  List[Any]    						=			inputs['''pixel_values'''].shape
    # check size is divisible by 32
    self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
    # check size
    self.assertEqual(_lowercase		,	(1, 3, 800, 1_088)				)

    with torch.no_grad():
     _UpperCamelCase:  Union[str, Any]    						=			model(**_lowercase				)
    # masks_queries_logits
    _UpperCamelCase:  Dict    						=			outputs.masks_queries_logits
    self.assertEqual(
        masks_queries_logits.shape		,	(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		,	)
    _UpperCamelCase:  Tuple    						=			[
        [-1.3737124, -1.7724937, -1.9364233],
        [-1.5977281, -1.9867939, -2.1523695],
        [-1.5795398, -1.9269832, -2.093942],
    ]
    _UpperCamelCase:  Tuple    						=			torch.tensor(_lowercase				).to(_lowercase				)
    self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,	_lowercase		,	atol=_lowercase				)				)
    # class_queries_logits
    _UpperCamelCase:  str    						=			outputs.class_queries_logits
    self.assertEqual(
        class_queries_logits.shape		,	(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)				)
    _UpperCamelCase:  Tuple    						=			torch.tensor(
        [
            [1.65_12E00, -5.25_72E00, -3.35_19E00],
            [3.61_69E-02, -5.90_25E00, -2.93_13E00],
            [1.07_66E-04, -7.76_30E00, -5.12_63E00],
        ]				).to(_lowercase				)
    self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,	_lowercase		,	atol=_lowercase				)				)



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			(
        MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''				)
        .to(_lowercase				)
        .eval()
    )
    _UpperCamelCase:  List[str]    						=			self.default_image_processor
    _UpperCamelCase:  str    						=			prepare_img()
    _UpperCamelCase:  Tuple    						=			image_processor(_lowercase		,	return_tensors='''pt'''				).to(_lowercase				)
    _UpperCamelCase:  Optional[Any]    						=			inputs['''pixel_values'''].shape
    # check size is divisible by 32
    self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
    # check size
    self.assertEqual(_lowercase		,	(1, 3, 800, 1_088)				)

    with torch.no_grad():
     _UpperCamelCase:  Dict    						=			model(**_lowercase				)
    # masks_queries_logits
    _UpperCamelCase:  Any    						=			outputs.masks_queries_logits
    self.assertEqual(
        masks_queries_logits.shape		,	(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		,	)
    _UpperCamelCase:  Any    						=			[[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
    _UpperCamelCase:  Tuple    						=			torch.tensor(_lowercase				).to(_lowercase				)
    self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,	_lowercase		,	atol=_lowercase				)				)
    # class_queries_logits
    _UpperCamelCase:  Optional[Any]    						=			outputs.class_queries_logits
    self.assertEqual(
        class_queries_logits.shape		,	(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)				)
    _UpperCamelCase:  List[Any]    						=			torch.tensor(
        [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]				).to(_lowercase				)
    self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,	_lowercase		,	atol=_lowercase				)				)



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			(
        MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''				)
        .to(_lowercase				)
        .eval()
    )
    _UpperCamelCase:  Optional[int]    						=			self.default_image_processor

    _UpperCamelCase:  Tuple    						=			image_processor(
        [np.zeros((3, 800, 1_333)				), np.zeros((3, 800, 1_333)				)]		,	segmentation_maps=[np.zeros((384, 384)				).astype(np.floataa				), np.zeros((384, 384)				).astype(np.floataa				)]		,	return_tensors='''pt'''		,	)

    _UpperCamelCase:  Any    						=			inputs['''pixel_values'''].to(_lowercase				)
    _UpperCamelCase:  List[Any]    						=			[el.to(_lowercase				) for el in inputs['''mask_labels''']]
    _UpperCamelCase:  int    						=			[el.to(_lowercase				) for el in inputs['''class_labels''']]

    with torch.no_grad():
     _UpperCamelCase:  Optional[Any]    						=			model(**_lowercase				)

    self.assertTrue(outputs.loss is not None				)
from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def    lowerCAmelCase_  (		lowercase:				str = ""		)     ->					dict[str, float]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
 _UpperCamelCase:  Union[str, Any]    						=			BeautifulSoup(requests.get(lowercase		).text	,       '''html.parser'''		)
 _UpperCamelCase:  List[Any]    						=			soup.find_all('''td'''	,       attrs='''titleColumn'''		)
 _UpperCamelCase:  str    						=			soup.find_all('''td'''	,       class_='''ratingColumn imdbRating'''		)
 return {
     title.a.text: float(rating.strong.text		)
     for title, rating in zip(lowercase	,       lowercase		)
 }




def    lowerCAmelCase_  (		lowercase:				str = "IMDb_Top_250_Movies.csv"		)     ->					None:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			get_imdb_top_aaa_movies()
 with open(lowercase	,       '''w'''	,       newline=''''''		) as out_file:
  _UpperCamelCase:  Optional[Any]    						=			csv.writer(lowercase		)
  writer.writerow(['''Movie title''', '''IMDb rating''']		)
  for title, rating in movies.items():
   writer.writerow([title, rating]		)


if __name__ == "__main__":
  write_movies()