

import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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 torch import nn

   from transformers import RegNetForImageClassification, RegNetModel
   from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor





class        lowerCAmelCase__  :



       '''simple docstring'''







       def __init__(							self ,   lowercase__ ,   lowercase__=3 ,   lowercase__=3_2 ,   lowercase__=3 ,   lowercase__=1_0 ,   lowercase__=[1_0, 2_0, 3_0, 4_0] ,   lowercase__=[1, 1, 2, 1] ,   lowercase__=True ,   lowercase__=True ,   lowercase__="relu" ,   lowercase__=3 ,   lowercase__=None ,   ):







             '''simple docstring'''



             __A    =parent
             __A    =batch_size
             __A    =image_size
             __A    =num_channels
             __A    =embeddings_size
             __A    =hidden_sizes
             __A    =depths
             __A    =is_training
             __A    =use_labels
             __A    =hidden_act
             __A    =num_labels
             __A    =scope
             __A    =len(lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

             __A    =None
             if self.use_labels:
                   __A    =ids_tensor([self.batch_size] ,   self.num_labels     )

             __A    =self.get_config()

             return config, pixel_values, labels

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return RegNetConfig(
                 num_channels=self.num_channels ,   embeddings_size=self.embeddings_size ,   hidden_sizes=self.hidden_sizes ,   depths=self.depths ,   hidden_act=self.hidden_act ,   num_labels=self.num_labels ,   )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =RegNetModel(config=lowercase__     )
             model.to(lowercase__     )
             model.eval()
             __A    =model(lowercase__     )
             # expected last hidden states: B, C, H // 32, W // 32
             self.parent.assertEqual(
                 result.last_hidden_state.shape ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) ,   )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =self.num_labels
             __A    =RegNetForImageClassification(lowercase__     )
             model.to(lowercase__     )
             model.eval()
             __A    =model(lowercase__ ,   labels=lowercase__     )
             self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_labels)     )


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.prepare_config_and_inputs()
             __A    ,		__A    ,		__A    =config_and_inputs
             __A    ={'''pixel_values''': pixel_values}
             return config, inputs_dict





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



       '''simple docstring'''







       lowercase_						=    (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
       lowercase_						=    (
           {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
           if is_torch_available()
           else {}
       )

       lowercase_						=    False
       lowercase_						=    False
       lowercase_						=    False
       lowercase_						=    False

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =RegNetModelTester(self     )
             __A    =ConfigTester(self ,   config_class=lowercase__ ,   has_text_modality=lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             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     ):







             '''simple docstring'''



             return

       @unittest.skip(reason='''RegNet does not use inputs_embeds'''     )
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             pass

       @unittest.skip(reason='''RegNet does not support input and output embeddings'''     )
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             pass

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ,		__A    =self.model_tester.prepare_config_and_inputs_for_common()

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

                   __A    =['''pixel_values''']
                   self.assertListEqual(arg_names[:1] ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ,		__A    =self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   __A    =model_class(config=lowercase__     )
                   for name, module in model.named_modules():
                         if isinstance(lowercase__ ,   (nn.BatchNormad, nn.GroupNorm)     ):
                               self.assertTrue(
                                   torch.all(module.weight == 1     ) ,   msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' ,   )
                               self.assertTrue(
                                   torch.all(module.bias == 0     ) ,   msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' ,   )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             def check_hidden_states_output(lowercase__ ,   lowercase__ ,   lowercase__     ):
                   __A    =model_class(lowercase__     )
                   model.to(lowercase__     )
                   model.eval()

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

                   __A    =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                   __A    =self.model_tester.num_stages
                   self.assertEqual(len(lowercase__     ) ,   expected_num_stages + 1     )

                   # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
                   self.assertListEqual(
                       list(hidden_states[0].shape[-2:]     ) ,   [self.model_tester.image_size // 2, self.model_tester.image_size // 2] ,   )

             __A    ,		__A    =self.model_tester.prepare_config_and_inputs_for_common()
             __A    =['''basic''', '''bottleneck''']
             for model_class in self.all_model_classes:
                   for layer_type in layers_type:
                         __A    =layer_type
                         __A    =True
                         check_hidden_states_output(lowercase__ ,   lowercase__ ,   lowercase__     )

                         # check that output_hidden_states also work using config
                         del inputs_dict["output_hidden_states"]
                         __A    =True

                         check_hidden_states_output(lowercase__ ,   lowercase__ ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*lowercase__     )


       @slow
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __A    =RegNetModel.from_pretrained(lowercase__     )
                   self.assertIsNotNone(lowercase__     )





def      A__					(			)					->Optional[Any]:
      __A    =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
      return image





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



       '''simple docstring'''







       @cached_property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return (
                 AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
                 if is_vision_available()
                 else None
             )


       @slow
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(lowercase__     )

             __A    =self.default_image_processor
             __A    =prepare_img()
             __A    =image_processor(images=lowercase__ ,   return_tensors='''pt'''     ).to(lowercase__     )

             # forward pass
             with torch.no_grad():
                   __A    =model(**lowercase__     )

             # verify the logits
             __A    =torch.Size((1, 1_0_0_0)     )
             self.assertEqual(outputs.logits.shape ,   lowercase__     )

             __A    =torch.tensor([-0.4180, -1.5051, -3.4836]     ).to(lowercase__     )

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





from typing import Dict, List, Optional, Tuple, Union

import numpy as np

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


if is_vision_available():
   import PIL

if is_torch_available():
   import torch


_lowerCamelCase      :					Dict					    =      logging.get_logger(__name__)





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    ["""pixel_values"""]

       def __init__(							self ,   lowercase__ = True ,   lowercase__ = None ,   lowercase__ = PILImageResampling.BILINEAR ,   lowercase__ = True ,   lowercase__ = 1 / 2_5_5 ,   lowercase__ = True ,   lowercase__ = None ,   lowercase__ = True ,   **lowercase__ ,   ):







             '''simple docstring'''



             super().__init__(**lowercase__     )
             __A    =size if size is not None else {'''shortest_edge''': 2_2_4}
             __A    =get_size_dict(lowercase__ ,   default_to_square=lowercase__     )
             __A    =crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
             __A    =get_size_dict(lowercase__ ,   param_name='''crop_size'''     )

             __A    =do_resize
             __A    =size
             __A    =resample
             __A    =do_rescale
             __A    =rescale_factor
             __A    =do_center_crop
             __A    =crop_size
             __A    =do_flip_channel_order

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ = PIL.Image.BILINEAR ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =get_size_dict(lowercase__ ,   default_to_square=lowercase__     )
             if "shortest_edge" not in size:
                   raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''     )
             __A    =get_resize_output_image_size(lowercase__ ,   size=size['''shortest_edge'''] ,   default_to_square=lowercase__     )
             return resize(lowercase__ ,   size=lowercase__ ,   resample=lowercase__ ,   data_format=lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =get_size_dict(lowercase__     )
             if "height" not in size or "width" not in size:
                   raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''     )
             return center_crop(lowercase__ ,   size=(size['''height'''], size['''width''']) ,   data_format=lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             return rescale(lowercase__ ,   scale=lowercase__ ,   data_format=lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             return flip_channel_order(lowercase__ ,   data_format=lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = ChannelDimension.FIRST ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =do_resize if do_resize is not None else self.do_resize
             __A    =resample if resample is not None else self.resample
             __A    =do_rescale if do_rescale is not None else self.do_rescale
             __A    =rescale_factor if rescale_factor is not None else self.rescale_factor
             __A    =do_center_crop if do_center_crop is not None else self.do_center_crop
             __A    =(
                 do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
             )

             __A    =size if size is not None else self.size
             __A    =get_size_dict(lowercase__ ,   default_to_square=lowercase__     )
             __A    =crop_size if crop_size is not None else self.crop_size
             __A    =get_size_dict(lowercase__ ,   param_name='''crop_size'''     )

             __A    =make_list_of_images(lowercase__     )

             if not valid_images(lowercase__     ):
                   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:
                   raise ValueError('''Size must be specified if do_resize is True.'''     )

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

             if do_center_crop and crop_size is None:
                   raise ValueError('''Crop size must be specified if do_center_crop is True.'''     )

             # All transformations expect numpy arrays.
             __A    =[to_numpy_array(lowercase__     ) for image in images]

             if do_resize:
                   __A    =[self.resize(image=lowercase__ ,   size=lowercase__ ,   resample=lowercase__     ) for image in images]

             if do_center_crop:
                   __A    =[self.center_crop(image=lowercase__ ,   size=lowercase__     ) for image in images]

             if do_rescale:
                   __A    =[self.rescale(image=lowercase__ ,   scale=lowercase__     ) for image in images]

             # the pretrained checkpoints assume images are BGR, not RGB
             if do_flip_channel_order:
                   __A    =[self.flip_channel_order(image=lowercase__     ) for image in images]

             __A    =[to_channel_dimension_format(lowercase__ ,   lowercase__     ) for image in images]

             __A    ={'''pixel_values''': images}
             return BatchFeature(data=lowercase__ ,   tensor_type=lowercase__     )


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =outputs.logits

             # Resize logits and compute semantic segmentation maps
             if target_sizes is not None:
                   if len(lowercase__     ) != len(lowercase__     ):
                         raise ValueError(
                             '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''     )

                   if is_torch_tensor(lowercase__     ):
                         __A    =target_sizes.numpy()

                   __A    =[]

                   for idx in range(len(lowercase__     )     ):
                         __A    =torch.nn.functional.interpolate(
                             logits[idx].unsqueeze(dim=0     ) ,   size=target_sizes[idx] ,   mode='''bilinear''' ,   align_corners=lowercase__     )
                         __A    =resized_logits[0].argmax(dim=0     )
                         semantic_segmentation.append(lowercase__     )
             else:
                   __A    =logits.argmax(dim=1     )
                   __A    =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]     )]

             return semantic_segmentation


