






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase  :							int			  =    logging.get_logger(__name__)

lowercase  :							Union[str, Any]			  =    {
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Optional[Any]						=					'decision_transformer'
						A     :   str						=					['past_key_values']
						A     :   Tuple						=					{
						    'max_position_embeddings': 'n_positions',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=17		,				_SCREAMING_SNAKE_CASE=4		,				_SCREAMING_SNAKE_CASE=128		,				_SCREAMING_SNAKE_CASE=4096		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=1		,				_SCREAMING_SNAKE_CASE=1024		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=1		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE="relu"		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=1e-5		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=5_0256		,				_SCREAMING_SNAKE_CASE=5_0256		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=False		,				**_SCREAMING_SNAKE_CASE		,				) ->				Tuple:
								snake_case_				:							Optional[int]						 =       state_dim
								snake_case_				:							List[str]						 =       act_dim
								snake_case_				:							str						 =       hidden_size
								snake_case_				:							Tuple						 =       max_ep_len
								snake_case_				:							Union[str, Any]						 =       action_tanh
								snake_case_				:							List[Any]						 =       vocab_size
								snake_case_				:							Tuple						 =       n_positions
								snake_case_				:							List[Any]						 =       n_layer
								snake_case_				:							Union[str, Any]						 =       n_head
								snake_case_				:							List[Any]						 =       n_inner
								snake_case_				:							Dict						 =       activation_function
								snake_case_				:							Optional[int]						 =       resid_pdrop
								snake_case_				:							Optional[Any]						 =       embd_pdrop
								snake_case_				:							int						 =       attn_pdrop
								snake_case_				:							int						 =       layer_norm_epsilon
								snake_case_				:							Optional[Any]						 =       initializer_range
								snake_case_				:							Tuple						 =       scale_attn_weights
								snake_case_				:							str						 =       use_cache
								snake_case_				:							Union[str, Any]						 =       scale_attn_by_inverse_layer_idx
								snake_case_				:							Any						 =       reorder_and_upcast_attn

								snake_case_				:							Optional[int]						 =       bos_token_id
								snake_case_				:							str						 =       eos_token_id

								super().__init__(bos_token_id=_SCREAMING_SNAKE_CASE		,				eos_token_id=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)








from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor




class 		UpperCAmelCase_						:


						'''simple docstring'''




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






						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							Union[str, Any]						 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

								snake_case_				:							Optional[Any]						 =       None
								if self.use_labels:
										snake_case_				:							Any						 =       ids_tensor([self.batch_size]		,				self.num_labels				)

								snake_case_				:							Optional[int]						 =       self.get_config()
								return config, pixel_values, labels






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								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       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
								snake_case_				:							Union[str, Any]						 =       TFRegNetModel(config=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[str]						 =       model(_SCREAMING_SNAKE_CASE		,				training=_SCREAMING_SNAKE_CASE				)
								# 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 // 32, self.image_size // 32)		,				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Tuple:
								snake_case_				:							Optional[int]						 =       self.num_labels
								snake_case_				:							Tuple						 =       TFRegNetForImageClassification(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[str]						 =       model(_SCREAMING_SNAKE_CASE		,				labels=_SCREAMING_SNAKE_CASE		,				training=_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							Optional[Any]						 =       self.prepare_config_and_inputs()
								snake_case_ ,       snake_case_ ,       snake_case_				:							Union[str, Any]						 =       config_and_inputs
								snake_case_				:							Any						 =       {"pixel_values": pixel_values}
								return config, inputs_dict




@require_tf
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   Optional[int]						=					(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
						A     :   Dict						=					(
						    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						A     :   List[Any]						=					False
						A     :   List[str]						=					False
						A     :   Optional[Any]						=					False
						A     :   List[Any]						=					False
						A     :   List[Any]						=					False






						def       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							List[Any]						 =       TFRegNetModelTester(self				)
								snake_case_				:							Any						 =       ConfigTester(self		,				config_class=_SCREAMING_SNAKE_CASE		,				has_text_modality=_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								return






						@unittest.skip(reason="RegNet does not use inputs_embeds"				)
						def       _lowerCAmelCase       (	self				) ->				Dict:
								pass






						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices("GPU"				)				) == 0		,				reason="TF does not support backprop for grouped convolutions on CPU."		,				)
						@slow
						def       _lowerCAmelCase       (	self				) ->				Any:
								super().test_keras_fit()






						@unittest.skip(reason="RegNet does not support input and output embeddings"				)
						def       _lowerCAmelCase       (	self				) ->				str:
								pass






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_ ,       snake_case_				:							List[Any]						 =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										snake_case_				:							int						 =       model_class(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							str						 =       inspect.signature(model.call				)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										snake_case_				:							List[str]						 =       [*signature.parameters.keys()]

										snake_case_				:							Dict						 =       ["pixel_values"]
										self.assertListEqual(arg_names[:1]		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							List[Any]						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								def check_hidden_states_output(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							List[str]						 =       model_class(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Tuple						 =       model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)		,				training=_SCREAMING_SNAKE_CASE				)

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

										snake_case_				:							Any						 =       self.model_tester.num_stages
										self.assertEqual(len(_SCREAMING_SNAKE_CASE				)		,				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]		,				)

								snake_case_ ,       snake_case_				:							Dict						 =       self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_				:							Optional[int]						 =       ["basic", "bottleneck"]
								for model_class in self.all_model_classes:
										for layer_type in layers_type:
												snake_case_				:							Any						 =       layer_type
												snake_case_				:							int						 =       True
												check_hidden_states_output(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

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

												check_hidden_states_output(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								snake_case_ ,       snake_case_				:							List[Any]						 =       self.model_tester.prepare_config_and_inputs_for_common()

								def check_equivalence(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE={}				):
										snake_case_				:							List[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				return_dict=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)
										snake_case_				:							List[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				return_dict=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				).to_tuple()

										def recursive_check(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
												if isinstance(_SCREAMING_SNAKE_CASE		,				(List, Tuple)				):
														for tuple_iterable_value, dict_iterable_value in zip(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
																recursive_check(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
												elif tuple_object is None:
														return
												else:
														self.assertTrue(
														    all(tf.equal(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)				)		,				msg=(
														        "Tuple and dict output are not equal. Difference:"
														        f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object				)				)}'''
														    )		,				)

												recursive_check(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

								for model_class in self.all_model_classes:
										snake_case_				:							str						 =       model_class(_SCREAMING_SNAKE_CASE				)

										snake_case_				:							List[Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Optional[Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										check_equivalence(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

										snake_case_				:							Union[str, Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Union[str, Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)
										check_equivalence(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

										snake_case_				:							Optional[Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Union[str, Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										check_equivalence(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				{"output_hidden_states": True}				)

										snake_case_				:							List[Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Dict						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)
										check_equivalence(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				{"output_hidden_states": True}				)






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							List[str]						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)






						@slow
						def       _lowerCAmelCase       (	self				) ->				Any:
								for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										snake_case_				:							Dict						 =       TFRegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
										self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)




def      lowerCAmelCase__	(    ):
		snake_case_				:							int						 =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
		return image




@require_tf
@require_vision
class 		UpperCAmelCase_						(	unittest.TestCase					):


						'''simple docstring'''




						@cached_property
						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								return (
								    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
								    if is_vision_available()
								    else None
								)






						@slow
						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							str						 =       TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

								snake_case_				:							int						 =       self.default_image_processor
								snake_case_				:							List[Any]						 =       prepare_img()
								snake_case_				:							str						 =       image_processor(images=_SCREAMING_SNAKE_CASE		,				return_tensors="tf"				)

								# forward pass
								snake_case_				:							Any						 =       model(**_SCREAMING_SNAKE_CASE		,				training=_SCREAMING_SNAKE_CASE				)

								# verify the logits
								snake_case_				:							List[str]						 =       tf.TensorShape((1, 1000)				)
								self.assertEqual(outputs.logits.shape		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[int]						 =       tf.constant([-0.4180, -1.5051, -3.4836]				)

								tf.debugging.assert_near(outputs.logits[0, :3]		,				_SCREAMING_SNAKE_CASE		,				atol=1e-4				)
