





from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)

class      lowerCAmelCase							(      __UpperCamelCase    ):
 def __init__(   self						:   Optional[Any]    ,  UpperCAmelCase						:   Distribution    ,  UpperCAmelCase						:   Dict=None    ,  UpperCAmelCase						:   Union[str, Any]=None    ,  UpperCAmelCase						:   str=0			)    ->			Dict:
     lowerCamelCase__					:   Dict      				=	1.0 if scale is None else scale
     lowerCamelCase__					:   Tuple      				=	0.0 if loc is None else loc

     super().__init__(UpperCAmelCase    ,  [AffineTransform(loc=self.loc    ,  scale=self.scale    ,  event_dim=UpperCAmelCase			)]			)
 @property
 def   A_			(   self						:   List[str]			)    ->			Tuple:
     return self.base_dist.mean * self.scale + self.loc
 @property
 def   A_			(   self						:   List[str]			)    ->			int:
     return self.base_dist.variance * self.scale**2







 @property
 def   A_			(   self						:   int			)    ->			Optional[Any]:
     return self.variance.sqrt()


class      lowerCAmelCase							(      nn.Module    ):
 def __init__(   self						:   List[str]    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Dict[str, int]    ,  UpperCAmelCase						:   Callable[..., Tuple[torch.Tensor]]    ,  **UpperCAmelCase						:   List[Any]			)    ->			None:
     super().__init__(**UpperCAmelCase			)
     lowerCamelCase__					:   List[Any]      				=	args_dim
     lowerCamelCase__					:   Optional[Any]      				=	nn.ModuleList([nn.Linear(UpperCAmelCase    ,  UpperCAmelCase			) for dim in args_dim.values()]			)
     lowerCamelCase__					:   Union[str, Any]      				=	domain_map







 def   A_			(   self						:   Optional[int]    ,  UpperCAmelCase						:   torch.Tensor			)    ->			Tuple[torch.Tensor]:
     lowerCamelCase__					:   Tuple      				=	[proj(UpperCAmelCase			) for proj in self.proj]

     return self.domain_map(*UpperCAmelCase			)


class      lowerCAmelCase							(      nn.Module    ):
 def __init__(   self						:   int    ,  UpperCAmelCase						:   Optional[int]			)    ->			Union[str, Any]:
     super().__init__()
     lowerCamelCase__					:   Optional[int]      				=	function







 def   A_			(   self						:   str    ,  UpperCAmelCase						:   Any    ,  *UpperCAmelCase						:   List[str]			)    ->			Tuple:
     return self.function(UpperCAmelCase    ,  *UpperCAmelCase			)


class      lowerCAmelCase							:
 UpperCAmelCase__									=	42
 UpperCAmelCase__									=	42
 UpperCAmelCase__									=	42
 def __init__(   self						:   Optional[Any]    ,  UpperCAmelCase						:   int = 1			)    ->			None:
     lowerCamelCase__					:   Optional[int]      				=	dim
     lowerCamelCase__					:   List[str]      				=	{k: dim * self.args_dim[k] for k in self.args_dim}
 def   A_			(   self						:   Optional[Any]    ,  UpperCAmelCase						:   List[Any]			)    ->			Dict:
     if self.dim == 1:
         return self.distribution_class(*UpperCAmelCase			)
     else:
         return Independent(self.distribution_class(*UpperCAmelCase			)    ,  1			)
 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Optional[torch.Tensor] = None    ,  UpperCAmelCase						:   Optional[torch.Tensor] = None    ,  )    ->			Distribution:
     lowerCamelCase__					:   int      				=	self._base_distribution(UpperCAmelCase			)
     if loc is None and scale is None:
         return distr
     else:
         return AffineTransformed(UpperCAmelCase    ,  loc=UpperCAmelCase    ,  scale=UpperCAmelCase    ,  event_dim=self.event_dim			)
 @property
 def   A_			(   self						:   List[Any]			)    ->			Tuple:
     return () if self.dim == 1 else (self.dim,)
 @property
 def   A_			(   self						:   List[Any]			)    ->			int:
     return len(self.event_shape			)
 @property
 def   A_			(   self						:   List[Any]			)    ->			float:
     return 0.0
 def   A_			(   self						:   Optional[int]    ,  UpperCAmelCase						:   int			)    ->			nn.Module:
     return ParameterProjection(
         in_features=UpperCAmelCase    ,  args_dim=self.args_dim    ,  domain_map=LambdaLayer(self.domain_map			)    ,  )
 def   A_			(   self						:   List[str]    ,  *UpperCAmelCase						:   torch.Tensor			)    ->			int:
     raise NotImplementedError()







 @staticmethod
 def   A_			(   UpperCAmelCase						:   torch.Tensor			)    ->			torch.Tensor:
     return (x + torch.sqrt(torch.square(UpperCAmelCase			) + 4.0			)) / 2.0


class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	{"df": 1, "loc": 1, "scale": 1}
 UpperCAmelCase__									=	StudentT
 @classmethod
 def   A_			(   cls						:   str    ,  UpperCAmelCase						:   torch.Tensor    ,  UpperCAmelCase						:   torch.Tensor    ,  UpperCAmelCase						:   torch.Tensor			)    ->			int:
     lowerCamelCase__					:   str      				=	cls.squareplus(UpperCAmelCase			).clamp_min(torch.finfo(scale.dtype			).eps			)
     lowerCamelCase__					:   List[str]      				=	2.0 + cls.squareplus(UpperCAmelCase			)
     return df.squeeze(-1			), loc.squeeze(-1			), scale.squeeze(-1			)


class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	{"loc": 1, "scale": 1}
 UpperCAmelCase__									=	Normal
 @classmethod
 def   A_			(   cls						:   List[Any]    ,  UpperCAmelCase						:   torch.Tensor    ,  UpperCAmelCase						:   torch.Tensor			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Optional[Any]      				=	cls.squareplus(UpperCAmelCase			).clamp_min(torch.finfo(scale.dtype			).eps			)
     return loc.squeeze(-1			), scale.squeeze(-1			)


class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	{"total_count": 1, "logits": 1}
 UpperCAmelCase__									=	NegativeBinomial
 @classmethod
 def   A_			(   cls						:   Tuple    ,  UpperCAmelCase						:   torch.Tensor    ,  UpperCAmelCase						:   torch.Tensor			)    ->			str:
     lowerCamelCase__					:   Any      				=	cls.squareplus(UpperCAmelCase			)
     return total_count.squeeze(-1			), logits.squeeze(-1			)
 def   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   List[Any]			)    ->			Distribution:
     lowerCamelCase__				,							lowerCamelCase__					:   List[str]      				=	distr_args
     if self.dim == 1:
         return self.distribution_class(total_count=UpperCAmelCase    ,  logits=UpperCAmelCase			)
     else:
         return Independent(self.distribution_class(total_count=UpperCAmelCase    ,  logits=UpperCAmelCase			)    ,  1			)







 def   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   Optional[torch.Tensor] = None    ,  UpperCAmelCase						:   Optional[torch.Tensor] = None			)    ->			Distribution:
     lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	distr_args

     if scale is not None:
         # See scaling property of Gamma.
         logits += scale.log()

     return self._base_distribution((total_count, logits)			)












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						:   List[Any]    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Optional[Any]=3    ,  UpperCAmelCase						:   str=32    ,  UpperCAmelCase						:   Union[str, Any]=3    ,  UpperCAmelCase						:   Dict=10    ,  UpperCAmelCase						:   List[str]=[10, 20, 30, 40]    ,  UpperCAmelCase						:   Any=[1, 1, 2, 1]    ,  UpperCAmelCase						:   List[str]=True    ,  UpperCAmelCase						:   List[str]=True    ,  UpperCAmelCase						:   Dict="relu"    ,  UpperCAmelCase						:   Tuple=3    ,  UpperCAmelCase						:   Dict=None    ,  )    ->			Optional[Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	parent
     lowerCamelCase__					:   Optional[Any]      				=	batch_size
     lowerCamelCase__					:   Dict      				=	image_size
     lowerCamelCase__					:   int      				=	num_channels
     lowerCamelCase__					:   int      				=	embeddings_size
     lowerCamelCase__					:   str      				=	hidden_sizes
     lowerCamelCase__					:   Any      				=	depths
     lowerCamelCase__					:   str      				=	is_training
     lowerCamelCase__					:   List[Any]      				=	use_labels
     lowerCamelCase__					:   Union[str, Any]      				=	hidden_act
     lowerCamelCase__					:   Dict      				=	num_labels
     lowerCamelCase__					:   Dict      				=	scope
     lowerCamelCase__					:   List[str]      				=	len(UpperCAmelCase			)
 def   A_			(   self						:   Optional[int]			)    ->			Optional[int]:
     lowerCamelCase__					:   int      				=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

     lowerCamelCase__					:   str      				=	self.get_config()

     return config, pixel_values
 def   A_			(   self						:   Optional[int]			)    ->			Dict:
     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						:   str    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Any			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[Any]      				=	FlaxRegNetModel(config=UpperCAmelCase			)
     lowerCamelCase__					:   Optional[Any]      				=	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 // 32, self.image_size // 32)    ,  )
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Tuple			)    ->			Tuple:
     lowerCamelCase__					:   List[str]      				=	self.num_labels
     lowerCamelCase__					:   str      				=	FlaxRegNetForImageClassification(config=UpperCAmelCase			)
     lowerCamelCase__					:   Union[str, Any]      				=	model(UpperCAmelCase			)
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels)			)







 def   A_			(   self						:   Dict			)    ->			str:
     lowerCamelCase__					:   Optional[int]      				=	self.prepare_config_and_inputs()
     lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	config_and_inputs
     lowerCamelCase__					:   Dict      				=	{'pixel_values': pixel_values}
     return config, inputs_dict


@require_flax
class      lowerCAmelCase							(      __UpperCamelCase,						unittest.TestCase    ):
 UpperCAmelCase__									=	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

 UpperCAmelCase__									=	False
 UpperCAmelCase__									=	False
 UpperCAmelCase__									=	False
 def   A_			(   self						:   List[Any]			)    ->			None:
     lowerCamelCase__					:   List[Any]      				=	FlaxRegNetModelTester(self			)
     lowerCamelCase__					:   Union[str, Any]      				=	ConfigTester(self    ,  config_class=UpperCAmelCase    ,  has_text_modality=UpperCAmelCase			)
 def   A_			(   self						:   int			)    ->			Tuple:
     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						:   Any			)    ->			str:
     return
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase			)
 def   A_			(   self						:   Dict			)    ->			Optional[Any]:
     lowerCamelCase__					:   Any      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase			)
 @unittest.skip(reason='RegNet does not use inputs_embeds'			)
 def   A_			(   self						:   Dict			)    ->			Dict:
     pass
 @unittest.skip(reason='RegNet does not support input and output embeddings'			)
 def   A_			(   self						:   Any			)    ->			Tuple:
     pass
 def   A_			(   self						:   Union[str, Any]			)    ->			Union[str, Any]:
     lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__					:   Optional[int]      				=	model_class(UpperCAmelCase			)
         lowerCamelCase__					:   Optional[Any]      				=	inspect.signature(model.__call__			)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCamelCase__					:   List[Any]      				=	[*signature.parameters.keys()]

         lowerCamelCase__					:   int      				=	['pixel_values']
         self.assertListEqual(arg_names[:1]    ,  UpperCAmelCase			)
 def   A_			(   self						:   int			)    ->			List[str]:
     def check_hidden_states_output(UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   Optional[Any]			):
         lowerCamelCase__					:   Any      				=	model_class(UpperCAmelCase			)

         lowerCamelCase__					:   Optional[Any]      				=	model(**self._prepare_for_class(UpperCAmelCase    ,  UpperCAmelCase			)			)

         lowerCamelCase__					:   Union[str, Any]      				=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         lowerCamelCase__					:   Optional[int]      				=	self.model_tester.num_stages
         self.assertEqual(len(UpperCAmelCase			)    ,  expected_num_stages + 1			)

     lowerCamelCase__				,							lowerCamelCase__					:   Optional[Any]      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__					:   List[str]      				=	True
         check_hidden_states_output(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCamelCase__					:   List[Any]      				=	True

         check_hidden_states_output(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)







 def   A_			(   self						:   Dict			)    ->			int:
     lowerCamelCase__				,							lowerCamelCase__					:   int      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__			):
             lowerCamelCase__					:   int      				=	self._prepare_for_class(UpperCAmelCase    ,  UpperCAmelCase			)
             lowerCamelCase__					:   Tuple      				=	model_class(UpperCAmelCase			)

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

             with self.subTest('JIT Enabled'			):
                 lowerCamelCase__					:   Dict      				=	model_jitted(**UpperCAmelCase			).to_tuple()

             with self.subTest('JIT Disabled'			):
                 with jax.disable_jit():
                     lowerCamelCase__					:   Optional[int]      				=	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  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Optional[int]      				=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
    return image


@require_flax
class      lowerCAmelCase							(      unittest.TestCase    ):
 @cached_property
 def   A_			(   self						:   Any			)    ->			List[Any]:
     return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'			) if is_vision_available() else None







 @slow
 def   A_			(   self						:   Dict			)    ->			Tuple:
     lowerCamelCase__					:   Union[str, Any]      				=	FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'			)

     lowerCamelCase__					:   Dict      				=	self.default_image_processor
     lowerCamelCase__					:   List[Any]      				=	prepare_img()
     lowerCamelCase__					:   List[str]      				=	image_processor(images=UpperCAmelCase    ,  return_tensors='np'			)

     lowerCamelCase__					:   List[Any]      				=	model(**UpperCAmelCase			)

     # verify the logits
     lowerCamelCase__					:   Union[str, Any]      				=	(1, 1000)
     self.assertEqual(outputs.logits.shape    ,  UpperCAmelCase			)

     lowerCamelCase__					:   Tuple      				=	jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]			)

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





