
import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor

class 		lowercase_       (       unittest.TestCase				):

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




        """simple docstring"""


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

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


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

        UpperCamelCase_							       =       self.get_config()

        return config, pixel_values

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        return ResNetConfig(
            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     ,	image_size=self.image_size     ,	)

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       FlaxResNetModel(config=__UpperCamelCase     )
        UpperCamelCase_							       =       model(__UpperCamelCase     )

        # Output shape (b, c, h, w)
        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 lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.num_labels
        UpperCamelCase_							       =       FlaxResNetForImageClassification(config=__UpperCamelCase     )
        UpperCamelCase_							       =       model(__UpperCamelCase     )
        self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_labels)     )

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.prepare_config_and_inputs()
        UpperCamelCase_       ,				UpperCamelCase_							       =       config_and_inputs
        UpperCamelCase_							       =       {"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_flax
class 		lowercase_       (       __SCREAMING_SNAKE_CASE							,	unittest.TestCase				):
 A__						:				Tuple         =    (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

 A__						:				str         =    False
 A__						:				Dict         =    False
 A__						:				Tuple         =    False

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       FlaxResNetModelTester(self     )
        UpperCamelCase_							       =       ConfigTester(self     ,	config_class=__UpperCamelCase     ,	has_text_modality=__UpperCamelCase     )

 def lowerCamelCase_				(      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 lowerCamelCase_				(      self     ):




        """simple docstring"""


        return

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase     )

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase     )

 @unittest.skip(reason="""ResNet does not use inputs_embeds"""     )
 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        pass

 @unittest.skip(reason="""ResNet does not support input and output embeddings"""     )
 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        pass

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_       ,				UpperCamelCase_							       =       self.model_tester.prepare_config_and_inputs_for_common()

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

               UpperCamelCase_							       =       ["""pixel_values"""]
               self.assertListEqual(arg_names[:1]     ,	__UpperCamelCase     )

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        def check_hidden_states_output(__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ):
               UpperCamelCase_							       =       model_class(__UpperCamelCase     )

               UpperCamelCase_							       =       model(**self._prepare_for_class(__UpperCamelCase     ,	__UpperCamelCase     )     )

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

               UpperCamelCase_							       =       self.model_tester.num_stages
               self.assertEqual(len(__UpperCamelCase     )     ,	expected_num_stages + 1     )

 @unittest.skip(reason="""ResNet does not use feedforward chunking"""     )
 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        pass

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_       ,				UpperCamelCase_							       =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__     ):
                      UpperCamelCase_							       =       self._prepare_for_class(__UpperCamelCase     ,	__UpperCamelCase     )
                      UpperCamelCase_							       =       model_class(__UpperCamelCase     )

                      @jax.jit
                      def model_jitted(__UpperCamelCase     ,	**__UpperCamelCase     ):
                             return model(pixel_values=__UpperCamelCase     ,	**__UpperCamelCase     )

                      with self.subTest("""JIT Enabled"""     ):
                             UpperCamelCase_							       =       model_jitted(**__UpperCamelCase     ).to_tuple()

                      with self.subTest("""JIT Disabled"""     ):
                             with jax.disable_jit():
                                    UpperCamelCase_							       =       model_jitted(**__UpperCamelCase     ).to_tuple()

                      self.assertEqual(len(__UpperCamelCase     )     ,	len(__UpperCamelCase     )     )
                      for jitted_output, output in zip(__UpperCamelCase     ,	__UpperCamelCase     ):
                             self.assertEqual(jitted_output.shape     ,	output.shape     )




def 				lowerCamelCase__				(						)		->	List[Any]:
       UpperCamelCase_							       =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
       return image







@require_flax
class 		lowercase_       (       unittest.TestCase				):

 @cached_property
 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        return AutoImageProcessor.from_pretrained("""microsoft/resnet-50"""     ) if is_vision_available() else None

 @slow
 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       FlaxResNetForImageClassification.from_pretrained("""microsoft/resnet-50"""     )

        UpperCamelCase_							       =       self.default_image_processor
        UpperCamelCase_							       =       prepare_img()
        UpperCamelCase_							       =       image_processor(images=__UpperCamelCase     ,	return_tensors="""np"""     )

        UpperCamelCase_							       =       model(**__UpperCamelCase     )

        # verify the logits
        UpperCamelCase_							       =       (1, 1_0_0_0)
        self.assertEqual(outputs.logits.shape     ,	__UpperCamelCase     )

        UpperCamelCase_							       =       jnp.array([-11.1_069, -9.7_877, -8.3_777]     )

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





import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 		lowercase_       (       __SCREAMING_SNAKE_CASE				):
 A__						:				List[str]         =    CONFIG_NAME

 @register_to_config
 def __init__(      self     ,	__UpperCamelCase = True     ,	__UpperCamelCase = 8     ,	__UpperCamelCase = "lanczos"     ,	__UpperCamelCase = True     ,	__UpperCamelCase = False     ,	):




        """simple docstring"""


        super().__init__()

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_							       =       images[None, ...]
        UpperCamelCase_							       =       (images * 2_5_5).round().astype("""uint8"""     )
        if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               UpperCamelCase_							       =       [Image.fromarray(image.squeeze()     ,	mode="""L"""     ) for image in images]
        else:
               UpperCamelCase_							       =       [Image.fromarray(__UpperCamelCase     ) for image in images]

        return pil_images

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        if not isinstance(__UpperCamelCase     ,	__UpperCamelCase     ):
               UpperCamelCase_							       =       [images]
        UpperCamelCase_							       =       [np.array(__UpperCamelCase     ).astype(np.floataa     ) / 255.0 for image in images]
        UpperCamelCase_							       =       np.stack(__UpperCamelCase     ,	axis=0     )

        return images

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_							       =       images[..., None]

        UpperCamelCase_							       =       torch.from_numpy(images.transpose(0     ,	3     ,	1     ,	2     )     )
        return images

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       images.cpu().permute(0     ,	2     ,	3     ,	1     ).float().numpy()
        return images

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        return 2.0 * images - 1.0

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        return (images / 2 + 0.5).clamp(0     ,	1     )

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       image.convert("""RGB"""     )
        return image

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




        """simple docstring"""


        if height is None:
               UpperCamelCase_							       =       image.height
        if width is None:
               UpperCamelCase_							       =       image.width

        UpperCamelCase_       ,				UpperCamelCase_							       =       (
            x - x % self.config.vae_scale_factor for x in (width, height)
        )  # resize to integer multiple of vae_scale_factor
        UpperCamelCase_							       =       image.resize((width, height)     ,	resample=PIL_INTERPOLATION[self.config.resample]     )
        return image

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




        """simple docstring"""


        UpperCamelCase_							       =       (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(__UpperCamelCase     ,	__UpperCamelCase     ):
               UpperCamelCase_							       =       [image]
        elif not (isinstance(__UpperCamelCase     ,	__UpperCamelCase     ) and all(isinstance(__UpperCamelCase     ,	__UpperCamelCase     ) for i in image     )):
               raise ValueError(
                   f'''Input is in incorrect format: {[type(__UpperCamelCase     ) for i in image]}. Currently, we only support {", ".join(__UpperCamelCase     )}'''     )

        if isinstance(image[0]     ,	PIL.Image.Image     ):
               if self.config.do_convert_rgb:
                      UpperCamelCase_							       =       [self.convert_to_rgb(__UpperCamelCase     ) for i in image]
               if self.config.do_resize:
                      UpperCamelCase_							       =       [self.resize(__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ) for i in image]
               UpperCamelCase_							       =       self.pil_to_numpy(__UpperCamelCase     )  # to np
               UpperCamelCase_							       =       self.numpy_to_pt(__UpperCamelCase     )  # to pt

        elif isinstance(image[0]     ,	np.ndarray     ):
               UpperCamelCase_							       =       np.concatenate(__UpperCamelCase     ,	axis=0     ) if image[0].ndim == 4 else np.stack(__UpperCamelCase     ,	axis=0     )
               UpperCamelCase_							       =       self.numpy_to_pt(__UpperCamelCase     )
               UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_							       =       image.shape
               if self.config.do_resize and (
                   height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
               ):
                      raise ValueError(
                          f'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
                          f'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''     )

        elif isinstance(image[0]     ,	torch.Tensor     ):
               UpperCamelCase_							       =       torch.cat(__UpperCamelCase     ,	axis=0     ) if image[0].ndim == 4 else torch.stack(__UpperCamelCase     ,	axis=0     )
               UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_							       =       image.shape

               # don't need any preprocess if the image is latents
               if channel == 4:
                      return image

               if self.config.do_resize and (
                   height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
               ):
                      raise ValueError(
                          f'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
                          f'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''     )

        # expected range [0,1], normalize to [-1,1]
        UpperCamelCase_							       =       self.config.do_normalize
        if image.min() < 0:
               warnings.warn(
                   """Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] """
                   f'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'''     ,	__UpperCamelCase     ,	)
               UpperCamelCase_							       =       False

        if do_normalize:
               UpperCamelCase_							       =       self.normalize(__UpperCamelCase     )

        return image

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase = "pil"     ,	__UpperCamelCase = None     ,	):




        """simple docstring"""


        if not isinstance(__UpperCamelCase     ,	torch.Tensor     ):
               raise ValueError(
                   f'''Input for postprocessing is in incorrect format: {type(__UpperCamelCase     )}. We only support pytorch tensor'''     )
        if output_type not in ["latent", "pt", "np", "pil"]:
               UpperCamelCase_							       =       (
                   f'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
                   """`pil`, `np`, `pt`, `latent`"""
               )
               deprecate("""Unsupported output_type"""     ,	"""1.0.0"""     ,	__UpperCamelCase     ,	standard_warn=__UpperCamelCase     )
               UpperCamelCase_							       =       """np"""

        if output_type == "latent":
               return image

        if do_denormalize is None:
               UpperCamelCase_							       =       [self.config.do_normalize] * image.shape[0]

        UpperCamelCase_							       =       torch.stack(
            [self.denormalize(image[i]     ) if do_denormalize[i] else image[i] for i in range(image.shape[0]     )]     )

        if output_type == "pt":
               return image

        UpperCamelCase_							       =       self.pt_to_numpy(__UpperCamelCase     )

        if output_type == "np":
               return image

        if output_type == "pil":
               return self.numpy_to_pil(__UpperCamelCase     )



class 		lowercase_       (       __SCREAMING_SNAKE_CASE				):
 A__						:				List[Any]         =    CONFIG_NAME

 @register_to_config
 def __init__(      self     ,	__UpperCamelCase = True     ,	__UpperCamelCase = 8     ,	__UpperCamelCase = "lanczos"     ,	__UpperCamelCase = True     ,	):




        """simple docstring"""


        super().__init__()

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_							       =       images[None, ...]
        UpperCamelCase_							       =       (images * 2_5_5).round().astype("""uint8"""     )
        if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               UpperCamelCase_							       =       [Image.fromarray(image.squeeze()     ,	mode="""L"""     ) for image in images]
        else:
               UpperCamelCase_							       =       [Image.fromarray(image[:, :, :3]     ) for image in images]

        return pil_images

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        return image[:, :, 1] * 2**8 + image[:, :, 2]

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_							       =       images[None, ...]
        UpperCamelCase_							       =       images[:, :, :, 3:]
        if images.shape[-1] == 6:
               UpperCamelCase_							       =       (images_depth * 2_5_5).round().astype("""uint8"""     )
               UpperCamelCase_							       =       [
                   Image.fromarray(self.rgblike_to_depthmap(__UpperCamelCase     )     ,	mode="""I;16"""     ) for image_depth in images_depth
               ]
        elif images.shape[-1] == 4:
               UpperCamelCase_							       =       (images_depth * 65_535.0).astype(np.uintaa     )
               UpperCamelCase_							       =       [Image.fromarray(__UpperCamelCase     ,	mode="""I;16"""     ) for image_depth in images_depth]
        else:
               raise Exception("""Not supported"""     )

        return pil_images

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase = "pil"     ,	__UpperCamelCase = None     ,	):




        """simple docstring"""


        if not isinstance(__UpperCamelCase     ,	torch.Tensor     ):
               raise ValueError(
                   f'''Input for postprocessing is in incorrect format: {type(__UpperCamelCase     )}. We only support pytorch tensor'''     )
        if output_type not in ["latent", "pt", "np", "pil"]:
               UpperCamelCase_							       =       (
                   f'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
                   """`pil`, `np`, `pt`, `latent`"""
               )
               deprecate("""Unsupported output_type"""     ,	"""1.0.0"""     ,	__UpperCamelCase     ,	standard_warn=__UpperCamelCase     )
               UpperCamelCase_							       =       """np"""

        if do_denormalize is None:
               UpperCamelCase_							       =       [self.config.do_normalize] * image.shape[0]

        UpperCamelCase_							       =       torch.stack(
            [self.denormalize(image[i]     ) if do_denormalize[i] else image[i] for i in range(image.shape[0]     )]     )

        UpperCamelCase_							       =       self.pt_to_numpy(__UpperCamelCase     )

        if output_type == "np":
               if image.shape[-1] == 6:
                      UpperCamelCase_							       =       np.stack([self.rgblike_to_depthmap(im[:, :, 3:]     ) for im in image]     ,	axis=0     )
               else:
                      UpperCamelCase_							       =       image[:, :, :, 3:]
               return image[:, :, :, :3], image_depth

        if output_type == "pil":
               return self.numpy_to_pil(__UpperCamelCase     ), self.numpy_to_depth(__UpperCamelCase     )
        else:
               raise Exception(f'''This type {output_type} is not supported'''     )



