



"""simple docstring"""

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  _UpperCAmelCase (    unittest.TestCase):

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

 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  List[Any]       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

     snake_case_				:  Any       =			self.get_config()

     return config, pixel_values

 def 		_snake_case      (  self	: str       ):
     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 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: Union[str, Any]						,  lowercase_	: List[Any]       ):
     snake_case_				:  int       =			FlaxRegNetModel(config=lowercase_       )
     snake_case_				:  Union[str, Any]       =			model(lowercase_       )

     # 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 		_snake_case      (  self	: Any						,  lowercase_	: List[Any]						,  lowercase_	: List[str]       ):
     snake_case_				:  str       =			self.num_labels
     snake_case_				:  Optional[int]       =			FlaxRegNetForImageClassification(config=lowercase_       )
     snake_case_				:  Dict       =			model(lowercase_       )
     self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)       )






 def 		_snake_case      (  self	: Any       ):
     snake_case_				:  str       =			self.prepare_config_and_inputs()
     snake_case_,			snake_case_				:  str       =			config_and_inputs
     snake_case_				:  List[str]       =			{'''pixel_values''': pixel_values}
     return config, inputs_dict





@require_flax
class  _UpperCAmelCase (    lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    List[str]	      =	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

 _lowerCAmelCase     :    Union[str, Any]	      =	False
 _lowerCAmelCase     :    Dict	      =	False
 _lowerCAmelCase     :    Union[str, Any]	      =	False

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Optional[int]       =			FlaxRegNetModelTester(self       )
     snake_case_				:  List[Any]       =			ConfigTester(self						,  config_class=lowercase_						,  has_text_modality=lowercase_       )

 def 		_snake_case      (  self	: Dict       ):
     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 		_snake_case      (  self	: Dict       ):
     return

 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  Optional[Any]       =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_       )

 def 		_snake_case      (  self	: Any       ):
     snake_case_				:  int       =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_       )

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

 @unittest.skip(reason='''RegNet does not support input and output embeddings'''       )
 def 		_snake_case      (  self	: Optional[int]       ):
     pass

 def 		_snake_case      (  self	: int       ):
     snake_case_,			snake_case_				:  Tuple       =			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         snake_case_				:  Optional[Any]       =			model_class(lowercase_       )
         snake_case_				:  Dict       =			inspect.signature(model.__call__       )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         snake_case_				:  Union[str, Any]       =			[*signature.parameters.keys()]

         snake_case_				:  Union[str, Any]       =			['''pixel_values''']
         self.assertListEqual(arg_names[:1]						,  lowercase_       )

 def 		_snake_case      (  self	: Any       ):
     def check_hidden_states_output(lowercase_	: Dict						,  lowercase_	: List[str]						,  lowercase_	: Any       ):
         snake_case_				:  str       =			model_class(lowercase_       )

         snake_case_				:  Optional[Any]       =			model(**self._prepare_for_class(lowercase_						,  lowercase_       )       )

         snake_case_				:  int       =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

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

     snake_case_,			snake_case_				:  Dict       =			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         snake_case_				:  Optional[Any]       =			True
         check_hidden_states_output(lowercase_						,  lowercase_						,  lowercase_       )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         snake_case_				:  Optional[int]       =			True

         check_hidden_states_output(lowercase_						,  lowercase_						,  lowercase_       )






 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_,			snake_case_				:  Optional[int]       =			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__       ):
             snake_case_				:  Tuple       =			self._prepare_for_class(lowercase_						,  lowercase_       )
             snake_case_				:  str       =			model_class(lowercase_       )

             @jax.jit
             def model_jitted(lowercase_	: Any						,  **lowercase_	: str       ):
                 return model(pixel_values=lowercase_						,  **lowercase_       )

             with self.subTest('''JIT Enabled'''       ):
                 snake_case_				:  Optional[Any]       =			model_jitted(**lowercase_       ).to_tuple()

             with self.subTest('''JIT Disabled'''       ):
                 with jax.disable_jit():
                     snake_case_				:  Any       =			model_jitted(**lowercase_       ).to_tuple()

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





def __lowercase    (							):
    snake_case_				:  Dict       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
    return image






@require_flax
class  _UpperCAmelCase (    unittest.TestCase):

 @cached_property
 def 		_snake_case      (  self	: Optional[Any]       ):
     return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''       ) if is_vision_available() else None






 @slow
 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  Any       =			FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''       )

     snake_case_				:  Optional[Any]       =			self.default_image_processor
     snake_case_				:  List[str]       =			prepare_img()
     snake_case_				:  int       =			image_processor(images=lowercase_						,  return_tensors='''np'''       )

     snake_case_				:  List[str]       =			model(**lowercase_       )

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

     snake_case_				:  Tuple       =			jnp.array([-0.41_80, -1.50_51, -3.48_36]       )

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










"""simple docstring"""

import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowercase__					:						List[Any]												=			logging.get_logger(__name__)




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Tuple	      =	"""linear"""
 _lowerCAmelCase     :    Optional[int]	      =	"""cosine"""
 _lowerCAmelCase     :    Optional[int]	      =	"""cosine_with_restarts"""
 _lowerCAmelCase     :    Union[str, Any]	      =	"""polynomial"""
 _lowerCAmelCase     :    Optional[Any]	      =	"""constant"""
 _lowerCAmelCase     :    Optional[Any]	      =	"""constant_with_warmup"""
 _lowerCAmelCase     :    Union[str, Any]	      =	"""piecewise_constant"""





def __lowercase    (							_a	,    _a = -1					):
    return LambdaLR(_a	,    lambda _a					: 1	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1.0	,    _a					)					)
        return 1.0

    return LambdaLR(_a	,    _a	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a = -1					):
    snake_case_				:  Dict       =			{}
    snake_case_				:  Optional[Any]       =			step_rules.split(''','''					)
    for rule_str in rule_list[:-1]:
        snake_case_,			snake_case_				:  Union[str, Any]       =			rule_str.split(''':'''					)
        snake_case_				:  Dict       =			int(_a					)
        snake_case_				:  int       =			float(_a					)
        snake_case_				:  Any       =			value
    snake_case_				:  Any       =			float(rule_list[-1]					)

    def create_rules_function(_a	,    _a					):
        def rule_func(_a					) -> float:
            snake_case_				:  Union[str, Any]       =			sorted(rules_dict.keys()					)
            for i, sorted_step in enumerate(_a					):
                if steps < sorted_step:
                    return rules_dict[sorted_steps[i]]
            return last_lr_multiple

        return rule_func

    snake_case_				:  List[str]       =			create_rules_function(_a	,    _a					)

    return LambdaLR(_a	,    _a	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a=-1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        return max(
            0.0	,    float(num_training_steps - current_step					) / float(max(1	,    num_training_steps - num_warmup_steps					)					)					)

    return LambdaLR(_a	,    _a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a = 0.5	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        snake_case_				:  str       =			float(current_step - num_warmup_steps					) / float(max(1	,    num_training_steps - num_warmup_steps					)					)
        return max(0.0	,    0.5 * (1.0 + math.cos(math.pi * float(_a					) * 2.0 * progress					))					)

    return LambdaLR(_a	,    _a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a = 1	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        snake_case_				:  int       =			float(current_step - num_warmup_steps					) / float(max(1	,    num_training_steps - num_warmup_steps					)					)
        if progress >= 1.0:
            return 0.0
        return max(0.0	,    0.5 * (1.0 + math.cos(math.pi * ((float(_a					) * progress) % 1.0)					))					)

    return LambdaLR(_a	,    _a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a=1E-7	,    _a=1.0	,    _a=-1					):
    snake_case_				:  List[str]       =			optimizer.defaults['''lr''']
    if not (lr_init > lr_end):
        raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"					)

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        elif current_step > num_training_steps:
            return lr_end / lr_init  # as LambdaLR multiplies by lr_init
        else:
            snake_case_				:  Tuple       =			lr_init - lr_end
            snake_case_				:  List[str]       =			num_training_steps - num_warmup_steps
            snake_case_				:  str       =			1 - (current_step - num_warmup_steps) / decay_steps
            snake_case_				:  Optional[int]       =			lr_range * pct_remaining**power + lr_end
            return decay / lr_init  # as LambdaLR multiplies by lr_init

    return LambdaLR(_a	,    _a	,    _a					)


lowercase__					:						Dict												=			{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def __lowercase    (							_a	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = 1	,    _a = 1.0	,    _a = -1	,    ):
    snake_case_				:  Union[str, Any]       =			SchedulerType(_a					)
    snake_case_				:  Any       =			TYPE_TO_SCHEDULER_FUNCTION[name]
    if name == SchedulerType.CONSTANT:
        return schedule_func(_a	,    last_epoch=_a					)

    if name == SchedulerType.PIECEWISE_CONSTANT:
        return schedule_func(_a	,    step_rules=_a	,    last_epoch=_a					)

    # All other schedulers require `num_warmup_steps`
    if num_warmup_steps is None:
        raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument."					)

    if name == SchedulerType.CONSTANT_WITH_WARMUP:
        return schedule_func(_a	,    num_warmup_steps=_a	,    last_epoch=_a					)

    # All other schedulers require `num_training_steps`
    if num_training_steps is None:
        raise ValueError(f"{name} requires `num_training_steps`, please provide that argument."					)

    if name == SchedulerType.COSINE_WITH_RESTARTS:
        return schedule_func(
            _a	,    num_warmup_steps=_a	,    num_training_steps=_a	,    num_cycles=_a	,    last_epoch=_a	,    )

    if name == SchedulerType.POLYNOMIAL:
        return schedule_func(
            _a	,    num_warmup_steps=_a	,    num_training_steps=_a	,    power=_a	,    last_epoch=_a	,    )

    return schedule_func(
        _a	,    num_warmup_steps=_a	,    num_training_steps=_a	,    last_epoch=_a					)





