

'''simple docstring'''

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, 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 MaskaFormerForUniversalSegmentation, MaskaFormerModel

    if is_vision_available():
        from transformers import MaskaFormerImageProcessor

if is_vision_available():
    from PIL import Image





class 							_snake_case					:
   def __init__( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Dict=2     ,SCREAMING_SNAKE_CASE__			:      str=True     ,SCREAMING_SNAKE_CASE__			:      List[str]=False     ,SCREAMING_SNAKE_CASE__			:      Tuple=10     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=3     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=32 * 8     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=32 * 8     ,SCREAMING_SNAKE_CASE__			:      List[str]=4     ,SCREAMING_SNAKE_CASE__			:      int=64     ,):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					parent
     SCREAMING_SNAKE_CASE:Any						      =					batch_size
     SCREAMING_SNAKE_CASE:int						      =					is_training
     SCREAMING_SNAKE_CASE:Any						      =					use_auxiliary_loss
     SCREAMING_SNAKE_CASE:List[str]						      =					num_queries
     SCREAMING_SNAKE_CASE:Tuple						      =					num_channels
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					min_size
     SCREAMING_SNAKE_CASE:Dict						      =					max_size
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					num_labels
     SCREAMING_SNAKE_CASE:List[str]						      =					hidden_dim
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					hidden_dim
   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:Dict						      =					floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]							).to(
         lowerCamelCase_							)

     SCREAMING_SNAKE_CASE:str						      =					torch.ones([self.batch_size, self.min_size, self.max_size]     ,device=lowerCamelCase_							)

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					(
         torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]     ,device=lowerCamelCase_							) > 0.5
     ).float()
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					(torch.rand((self.batch_size, self.num_labels)     ,device=lowerCamelCase_							) > 0.5).long()

     SCREAMING_SNAKE_CASE:List[str]						      =					self.get_config()
     return config, pixel_values, pixel_mask, mask_labels, class_labels
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:List[str]						      =					MaskaFormerConfig(
         hidden_size=self.hidden_dim     ,)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.num_queries
     SCREAMING_SNAKE_CASE:Dict						      =					self.num_labels

     SCREAMING_SNAKE_CASE:List[Any]						      =					[1, 1, 1, 1]
     SCREAMING_SNAKE_CASE:Dict						      =					self.num_channels

     SCREAMING_SNAKE_CASE:Dict						      =					64
     SCREAMING_SNAKE_CASE:str						      =					128
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.hidden_dim
     SCREAMING_SNAKE_CASE:List[Any]						      =					self.hidden_dim
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.hidden_dim
     return config
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:str						      =					self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
     return config, inputs_dict
   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      int							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					output.encoder_hidden_states
     SCREAMING_SNAKE_CASE:int						      =					output.pixel_decoder_hidden_states
     SCREAMING_SNAKE_CASE:Any						      =					output.transformer_decoder_hidden_states

     self.parent.assertTrue(len(lowerCamelCase_							)     ,len(config.backbone_config.depths							)							)
     self.parent.assertTrue(len(lowerCamelCase_							)     ,len(config.backbone_config.depths							)							)
     self.parent.assertTrue(len(lowerCamelCase_							)     ,config.decoder_layers							)
   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Dict     ,SCREAMING_SNAKE_CASE__			:      List[str]=False							):
     with torch.no_grad():
       SCREAMING_SNAKE_CASE:int						      =					MaskaFormerModel(config=lowerCamelCase_							)
       model.to(lowerCamelCase_							)
       model.eval()

       SCREAMING_SNAKE_CASE:Dict						      =					model(pixel_values=lowerCamelCase_     ,pixel_mask=lowerCamelCase_							)
       SCREAMING_SNAKE_CASE:List[Any]						      =					model(lowerCamelCase_     ,output_hidden_states=lowerCamelCase_							)

     self.parent.assertEqual(
         output.transformer_decoder_last_hidden_state.shape     ,(self.batch_size, self.num_queries, self.hidden_dim)     ,)
     # 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(lowerCamelCase_     ,lowerCamelCase_							)







   def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      List[Any]							):
     SCREAMING_SNAKE_CASE:List[str]						      =					MaskaFormerForUniversalSegmentation(config=lowerCamelCase_							)
     model.to(lowerCamelCase_							)
     model.eval()

     def comm_check_on_output(SCREAMING_SNAKE_CASE__			:      str							):
       # 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():
       SCREAMING_SNAKE_CASE:Any						      =					model(pixel_values=lowerCamelCase_     ,pixel_mask=lowerCamelCase_							)
       SCREAMING_SNAKE_CASE:List[Any]						      =					model(lowerCamelCase_							)

       comm_check_on_output(lowerCamelCase_							)

       SCREAMING_SNAKE_CASE:Optional[Any]						      =					model(
           pixel_values=lowerCamelCase_     ,pixel_mask=lowerCamelCase_     ,mask_labels=lowerCamelCase_     ,class_labels=lowerCamelCase_							)

     comm_check_on_output(lowerCamelCase_							)

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





@require_torch
class 							_snake_case					(       a__	,  a__	,  unittest.TestCase						):
   _A       :     Tuple       =					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
   _A       :     Any       =					{"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

   _A       :     Union[str, Any]       =					False
   _A       :     str       =					False
   _A       :     List[Any]       =					False
   _A       :     Dict       =					False
   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:Dict						      =					MaskaFormerModelTester(self							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					ConfigTester(self     ,config_class=lowerCamelCase_     ,has_text_modality=lowerCamelCase_							)
   def     __UpperCamelCase						( self			:      List[str]							):
     self.config_tester.run_common_tests()
   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Any						      =					self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(lowerCamelCase_     ,**lowerCamelCase_     ,output_hidden_states=lowerCamelCase_							)
   def     __UpperCamelCase						( self			:      str							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase_							)
   @unittest.skip(reason="Mask2Former does not use inputs_embeds"							)
   def     __UpperCamelCase						( self			:      Any							):
     pass
   @unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"							)
   def     __UpperCamelCase						( self			:      Tuple							):
     pass
   @unittest.skip(reason="Mask2Former is not a generative model"							)
   def     __UpperCamelCase						( self			:      List[Any]							):
     pass
   @unittest.skip(reason="Mask2Former does not use token embeddings"							)
   def     __UpperCamelCase						( self			:      str							):
     pass
   @require_torch_multi_gpu
   @unittest.skip(
       reason="Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`"							)
   def     __UpperCamelCase						( self			:      Optional[int]							):
     pass
   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
   def     __UpperCamelCase						( self			:      Optional[int]							):
     pass
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[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(lowerCamelCase_							)
       SCREAMING_SNAKE_CASE:Tuple						      =					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:Tuple						      =					["pixel_values"]
       self.assertListEqual(arg_names[:1]     ,lowerCamelCase_							)
   @slow
   def     __UpperCamelCase						( self			:      int							):
     for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
       SCREAMING_SNAKE_CASE:str						      =					MaskaFormerModel.from_pretrained(lowerCamelCase_							)
       self.assertIsNotNone(lowerCamelCase_							)
   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					(self.model_tester.min_size,) * 2
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					{
         "pixel_values": torch.randn((2, 3, *size)     ,device=lowerCamelCase_							),
         "mask_labels": torch.randn((2, 10, *size)     ,device=lowerCamelCase_							),
         "class_labels": torch.zeros(2     ,10     ,device=lowerCamelCase_							).long(),
     }
     SCREAMING_SNAKE_CASE:int						      =					self.model_tester.get_config()

     SCREAMING_SNAKE_CASE:Tuple						      =					MaskaFormerForUniversalSegmentation(lowerCamelCase_							).to(lowerCamelCase_							)
     SCREAMING_SNAKE_CASE:Tuple						      =					model(**lowerCamelCase_							)
     self.assertTrue(outputs.loss is not None							)
   def     __UpperCamelCase						( self			:      str							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(lowerCamelCase_     ,**lowerCamelCase_     ,output_hidden_states=lowerCamelCase_							)
   def     __UpperCamelCase						( self			:      Dict							):
     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:Optional[Any]						      =					model_class(lowerCamelCase_							).to(lowerCamelCase_							)
       SCREAMING_SNAKE_CASE:int						      =					model(**lowerCamelCase_     ,output_attentions=lowerCamelCase_							)
       self.assertTrue(outputs.attentions is not None							)
   def     __UpperCamelCase						( self			:      int							):
     if not self.model_tester.is_training:
       return

     SCREAMING_SNAKE_CASE:Dict						      =					self.all_model_classes[1]
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.model_tester.prepare_config_and_inputs()

     SCREAMING_SNAKE_CASE:str						      =					model_class(lowerCamelCase_							)
     model.to(lowerCamelCase_							)
     model.train()

     SCREAMING_SNAKE_CASE:int						      =					model(lowerCamelCase_     ,mask_labels=lowerCamelCase_     ,class_labels=lowerCamelCase_							).loss
     loss.backward()







   def     __UpperCamelCase						( self			:      int							):
     SCREAMING_SNAKE_CASE:Tuple						      =					self.all_model_classes[1]
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.model_tester.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE:List[str]						      =					True
     SCREAMING_SNAKE_CASE:List[Any]						      =					True

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					model_class(lowerCamelCase_							).to(lowerCamelCase_							)
     model.train()

     SCREAMING_SNAKE_CASE:List[str]						      =					model(lowerCamelCase_     ,mask_labels=lowerCamelCase_     ,class_labels=lowerCamelCase_							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					outputs.encoder_hidden_states[0]
     encoder_hidden_states.retain_grad()

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					outputs.pixel_decoder_hidden_states[0]
     pixel_decoder_hidden_states.retain_grad()

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					outputs.transformer_decoder_hidden_states[0]
     transformer_decoder_hidden_states.retain_grad()

     SCREAMING_SNAKE_CASE:int						      =					outputs.attentions[0]
     attentions.retain_grad()

     outputs.loss.backward(retain_graph=lowerCamelCase_							)

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



A_       						=							1e-4





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





@require_vision
@slow
class 							_snake_case					(       unittest.TestCase						):
   @cached_property
   def     __UpperCamelCase						( self			:      List[Any]							):
     return "facebook/mask2former-swin-small-coco-instance"
   @cached_property
   def     __UpperCamelCase						( self			:      List[str]							):
     return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints							) if is_vision_available() else None
   def     __UpperCamelCase						( self			:      Dict							):
     SCREAMING_SNAKE_CASE:Dict						      =					MaskaFormerModel.from_pretrained(self.model_checkpoints							).to(lowerCamelCase_							)
     SCREAMING_SNAKE_CASE:int						      =					self.default_image_processor
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					prepare_img()
     SCREAMING_SNAKE_CASE:int						      =					image_processor(lowerCamelCase_     ,return_tensors="pt"							).to(lowerCamelCase_							)
     SCREAMING_SNAKE_CASE: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(lowerCamelCase_     ,(1, 3, 384, 384)							)

     with torch.no_grad():
       SCREAMING_SNAKE_CASE:List[str]						      =					model(**lowerCamelCase_							)

     SCREAMING_SNAKE_CASE:List[str]						      =					torch.tensor(
         [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]							).to(lowerCamelCase_							)
     self.assertTrue(
         torch.allclose(
             outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,lowerCamelCase_     ,atol=lowerCamelCase_							)							)

     SCREAMING_SNAKE_CASE:Tuple						      =					torch.tensor(
         [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]							).to(lowerCamelCase_							)
     self.assertTrue(
         torch.allclose(
             outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]     ,lowerCamelCase_     ,atol=lowerCamelCase_							)							)

     SCREAMING_SNAKE_CASE:str						      =					torch.tensor(
         [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]							).to(lowerCamelCase_							)
     self.assertTrue(
         torch.allclose(
             outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,lowerCamelCase_     ,atol=lowerCamelCase_							)							)
   def     __UpperCamelCase						( self			:      str							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(lowerCamelCase_							).eval()
     SCREAMING_SNAKE_CASE:str						      =					self.default_image_processor
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					prepare_img()
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_processor(lowerCamelCase_     ,return_tensors="pt"							).to(lowerCamelCase_							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					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(lowerCamelCase_     ,(1, 3, 384, 384)							)

     with torch.no_grad():
       SCREAMING_SNAKE_CASE:Any						      =					model(**lowerCamelCase_							)
     # masks_queries_logits
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					outputs.masks_queries_logits
     self.assertEqual(
         masks_queries_logits.shape     ,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)							)
     SCREAMING_SNAKE_CASE:str						      =					[
         [-8.7_839, -9.0_056, -8.8_121],
         [-7.4_104, -7.0_313, -6.5_401],
         [-6.6_105, -6.3_427, -6.4_675],
     ]
     SCREAMING_SNAKE_CASE:str						      =					torch.tensor(lowerCamelCase_							).to(lowerCamelCase_							)
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,lowerCamelCase_     ,atol=lowerCamelCase_							)							)
     # class_queries_logits
     SCREAMING_SNAKE_CASE:Tuple						      =					outputs.class_queries_logits
     self.assertEqual(class_queries_logits.shape     ,(1, model.config.num_queries, model.config.num_labels + 1)							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.tensor(
         [
             [1.8_324, -8.0_835, -4.1_922],
             [0.8_450, -9.0_050, -3.6_053],
             [0.3_045, -7.7_293, -3.0_275],
         ]							).to(lowerCamelCase_							)
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,lowerCamelCase_     ,atol=lowerCamelCase_							)							)







   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(lowerCamelCase_							).eval()
     SCREAMING_SNAKE_CASE:int						      =					self.default_image_processor

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					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"     ,)

     SCREAMING_SNAKE_CASE:List[str]						      =					inputs["pixel_values"].to(lowerCamelCase_							)
     SCREAMING_SNAKE_CASE:str						      =					[el.to(lowerCamelCase_							) for el in inputs["mask_labels"]]
     SCREAMING_SNAKE_CASE:int						      =					[el.to(lowerCamelCase_							) for el in inputs["class_labels"]]

     with torch.no_grad():
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					model(**lowerCamelCase_							)

     self.assertTrue(outputs.loss is not None							)









'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     Union[str, Any]       =					['''pixel_values''']
   def __init__( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BILINEAR     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Union[int, float] = 1 / 255     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,**SCREAMING_SNAKE_CASE__			:      List[str]     ,):
     super().__init__(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					size if size is not None else {"shortest_edge": 384}
     SCREAMING_SNAKE_CASE:List[str]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					do_resize
     SCREAMING_SNAKE_CASE:List[Any]						      =					size
     # Default value set here for backwards compatibility where the value in config is None
     SCREAMING_SNAKE_CASE:Dict						      =					crop_pct if crop_pct is not None else 224 / 256
     SCREAMING_SNAKE_CASE:Any						      =					resample
     SCREAMING_SNAKE_CASE:Dict						      =					do_rescale
     SCREAMING_SNAKE_CASE:Optional[int]						      =					rescale_factor
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					do_normalize
     SCREAMING_SNAKE_CASE:Optional[int]						      =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     SCREAMING_SNAKE_CASE:Tuple						      =					image_std if image_std is not None else IMAGENET_STANDARD_STD
   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int]     ,SCREAMING_SNAKE_CASE__			:      float     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BICUBIC     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      int     ,):
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     if "shortest_edge" not in size:
       raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''							)
     SCREAMING_SNAKE_CASE:Any						      =					size["shortest_edge"]

     if shortest_edge < 384:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       SCREAMING_SNAKE_CASE:Any						      =					int(shortest_edge / crop_pct							)
       SCREAMING_SNAKE_CASE:Any						      =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=SCREAMING_SNAKE_CASE__     ,size=(shortest_edge, shortest_edge)     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           SCREAMING_SNAKE_CASE__     ,size=(shortest_edge, shortest_edge)     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[int, float]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     return rescale(SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     return normalize(SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      ImageInput     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, TensorType]] = None     ,SCREAMING_SNAKE_CASE__			:      ChannelDimension = ChannelDimension.FIRST     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE:List[str]						      =					crop_pct if crop_pct is not None else self.crop_pct
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE:List[str]						      =					do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE:str						      =					rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE:str						      =					do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE:Any						      =					image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE:Optional[int]						      =					size if size is not None else self.size
     SCREAMING_SNAKE_CASE:Tuple						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					make_list_of_images(SCREAMING_SNAKE_CASE__							)

     if not valid_images(SCREAMING_SNAKE_CASE__							):
       raise ValueError(
           "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
           "torch.Tensor, tf.Tensor or jax.ndarray."							)

     if do_resize and size is None or resample is None:
       raise ValueError("Size and resample must be specified if do_resize is True."							)

     if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
       raise ValueError("crop_pct must be specified if size < 384."							)

     if do_rescale and rescale_factor is None:
       raise ValueError("Rescale factor must be specified if do_rescale is True."							)

     if do_normalize and (image_mean is None or image_std is None):
       raise ValueError("Image mean and std must be specified if do_normalize is True."							)

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE:int						      =					[to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					[self.resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,crop_pct=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[self.rescale(image=SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_normalize:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					[self.normalize(image=SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:Any						      =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:int						      =					{"pixel_values": images}
     return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,tensor_type=SCREAMING_SNAKE_CASE__							)






