import inspect
import unittest

from transformers import RegNetConfig, 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.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor





class      lowerCAmelCase						(					unittest.TestCase			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__=3				,      lowerCAmelCase__=32				,      lowerCAmelCase__=3				,      lowerCAmelCase__=10				,      lowerCAmelCase__=[10, 20, 30, 40]				,      lowerCAmelCase__=[1, 1, 2, 1]				,      lowerCAmelCase__=True				,      lowerCAmelCase__=True				,      lowerCAmelCase__="relu"				,      lowerCAmelCase__=3				,      lowerCAmelCase__=None				,      ):
            _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(lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

            _A=	self.get_config()

            return config, pixel_values



     def 			a__  (					self       ):
            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				,      image_size=self.image_size				,      )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	FlaxRegNetModel(config=lowerCAmelCase__       )
            _A=	model(lowerCAmelCase__       )

            # Output shape (b, c, h, w)
            self.parent.assertEqual(
                result.last_hidden_state.shape				,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,      )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	self.num_labels
            _A=	FlaxRegNetForImageClassification(config=lowerCAmelCase__       )
            _A=	model(lowerCAmelCase__       )
            self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_labels)       )





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





@require_flax
class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

     _SCREAMING_SNAKE_CASE     :     Tuple		=False
     _SCREAMING_SNAKE_CASE     :     Any		=False
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=False



     def 			a__  (					self       ):
            _A=	FlaxRegNetModelTester(self       )
            _A=	ConfigTester(self				,      config_class=lowerCAmelCase__				,      has_text_modality=lowerCAmelCase__       )



     def 			a__  (					self       ):
            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 			a__  (					self       ):
            return



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__       )



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



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



     def 			a__  (					self       ):
            _A,			_A=	self.model_tester.prepare_config_and_inputs_for_common()

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

                   _A=	['pixel_values']
                   self.assertListEqual(arg_names[:1]				,      lowerCAmelCase__       )



     def 			a__  (					self       ):
            def check_hidden_states_output(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
                   _A=	model_class(lowerCAmelCase__       )

                   _A=	model(**self._prepare_for_class(lowerCAmelCase__				,      lowerCAmelCase__       )       )

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

                   _A=	self.model_tester.num_stages
                   self.assertEqual(len(lowerCAmelCase__       )				,      expected_num_stages + 1       )

            _A,			_A=	self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _A=	True
                   check_hidden_states_output(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )

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

                   check_hidden_states_output(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )





     def 			a__  (					self       ):
            _A,			_A=	self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__       ):
                          _A=	self._prepare_for_class(lowerCAmelCase__				,      lowerCAmelCase__       )
                          _A=	model_class(lowerCAmelCase__       )

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

                          with self.subTest('JIT Enabled'       ):
                                 _A=	model_jitted(**lowerCAmelCase__       ).to_tuple()

                          with self.subTest('JIT Disabled'       ):
                                 with jax.disable_jit():
                                        _A=	model_jitted(**lowerCAmelCase__       ).to_tuple()

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


def 							UpperCamelCase				(    )		->						Dict:




       '''simple docstring'''






       _A=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
       return image





@require_flax
class      lowerCAmelCase						(					unittest.TestCase			):



     @cached_property
     def 			a__  (					self       ):
            return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'       ) if is_vision_available() else None





     @slow
     def 			a__  (					self       ):
            _A=	FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'       )

            _A=	self.default_image_processor
            _A=	prepare_img()
            _A=	image_processor(images=lowerCAmelCase__				,      return_tensors='np'       )

            _A=	model(**lowerCAmelCase__       )

            # verify the logits
            _A=	(1, 1000)
            self.assertEqual(outputs.logits.shape				,      lowerCAmelCase__       )

            _A=	jnp.array([-0.4180, -1.5051, -3.4836]       )

            self.assertTrue(jnp.allclose(outputs.logits[0, :3]				,      lowerCAmelCase__				,      atol=1E-4       )       )
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class      lowerCAmelCase						:
     _SCREAMING_SNAKE_CASE     :     torch.Tensor  # [batch_size x 3]
     _SCREAMING_SNAKE_CASE     :     torch.Tensor  # [batch_size x 3]
     _SCREAMING_SNAKE_CASE     :     torch.Tensor  # [batch_size x 3]
     _SCREAMING_SNAKE_CASE     :     torch.Tensor  # [batch_size x 3]
     _SCREAMING_SNAKE_CASE     :     int
     _SCREAMING_SNAKE_CASE     :     int
     _SCREAMING_SNAKE_CASE     :     float
     _SCREAMING_SNAKE_CASE     :     float
     _SCREAMING_SNAKE_CASE     :     Tuple[int]



     def 			a__  (					self       ):
            assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
            assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
            assert len(self.x.shape       ) == len(self.y.shape       ) == len(self.z.shape       ) == len(self.origin.shape       ) == 2



     def 			a__  (					self       ):
            return torch.from_numpy(np.array([self.width, self.height]				,      dtype=np.floataa       )       )



     def 			a__  (					self       ):
            return torch.from_numpy(np.array([self.x_fov, self.y_fov]				,      dtype=np.floataa       )       )



     def 			a__  (					self       ):
            _A=	torch.arange(self.height * self.width       )
            _A=	torch.stack(
                [
                    pixel_indices % self.width,
                    torch.div(lowerCAmelCase__				,      self.width				,      rounding_mode='trunc'       ),
                ]				,      axis=1				,      )
            return coords



     @property
     def 			a__  (					self       ):
            _A,			*_A=	self.shape
            _A=	int(np.prod(lowerCAmelCase__       )       )

            _A=	self.get_image_coords()
            _A=	torch.broadcast_to(coords.unsqueeze(0       )				,      [batch_size * inner_batch_size, *coords.shape]       )
            _A=	self.get_camera_rays(lowerCAmelCase__       )

            _A=	rays.view(lowerCAmelCase__				,      inner_batch_size * self.height * self.width				,      2				,      3       )

            return rays



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A,			*_A,			_A=	coords.shape
            assert n_coords == 2
            assert batch_size == self.origin.shape[0]

            _A=	coords.view(lowerCAmelCase__				,      -1				,      2       )

            _A=	self.resolution()
            _A=	self.fov()

            _A=	(flat.float() / (res - 1)) * 2 - 1
            _A=	fracs * torch.tan(fov / 2       )

            _A=	fracs.view(lowerCAmelCase__				,      -1				,      2       )
            _A=	(
                self.z.view(lowerCAmelCase__				,      1				,      3       )
                + self.x.view(lowerCAmelCase__				,      1				,      3       ) * fracs[:, :, :1]
                + self.y.view(lowerCAmelCase__				,      1				,      3       ) * fracs[:, :, 1:]
            )
            _A=	directions / directions.norm(dim=-1				,      keepdim=lowerCAmelCase__       )
            _A=	torch.stack(
                [
                    torch.broadcast_to(self.origin.view(lowerCAmelCase__				,      1				,      3       )				,      [batch_size, directions.shape[1], 3]       ),
                    directions,
                ]				,      dim=2				,      )
            return rays.view(lowerCAmelCase__				,      *lowerCAmelCase__				,      2				,      3       )





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            assert width * self.height == height * self.width, "The aspect ratio should not change."
            return DifferentiableProjectiveCamera(
                origin=self.origin				,      x=self.x				,      y=self.y				,      z=self.z				,      width=lowerCAmelCase__				,      height=lowerCAmelCase__				,      x_fov=self.x_fov				,      y_fov=self.y_fov				,      )


def 							UpperCamelCase				(    lowerCAmelCase_   )		->						DifferentiableProjectiveCamera:




       '''simple docstring'''






       _A=	[]
       _A=	[]
       _A=	[]
       _A=	[]
       for theta in np.linspace(0       ,					2 * np.pi       ,					num=20   ):
              _A=	np.array([np.sin(lowerCAmelCase_   ), np.cos(lowerCAmelCase_   ), -0.5]   )
              z /= np.sqrt(np.sum(z**2   )   )
              _A=	-z * 4
              _A=	np.array([np.cos(lowerCAmelCase_   ), -np.sin(lowerCAmelCase_   ), 0.0]   )
              _A=	np.cross(lowerCAmelCase_       ,					lowerCAmelCase_   )
              origins.append(lowerCAmelCase_   )
              xs.append(lowerCAmelCase_   )
              ys.append(lowerCAmelCase_   )
              zs.append(lowerCAmelCase_   )
       return DifferentiableProjectiveCamera(
           origin=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					x=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					y=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					z=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					width=lowerCAmelCase_       ,					height=lowerCAmelCase_       ,					x_fov=0.7       ,					y_fov=0.7       ,					shape=(1, len(lowerCAmelCase_   ))       ,					)