from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
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 TFViTForImageClassification, TFViTModel


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor





class UpperCAmelCase						:


				'''simple docstring'''






				def __init__(						self		,     lowercase		,     lowercase=1_3		,     lowercase=3_0		,     lowercase=2		,     lowercase=3		,     lowercase=True		,     lowercase=True		,     lowercase=3_2		,     lowercase=2		,     lowercase=4		,     lowercase=3_7		,     lowercase="gelu"		,     lowercase=0.1		,     lowercase=0.1		,     lowercase=1_0		,     lowercase=0.02		,     lowercase=3		,     lowercase=None		,     ):



						"""simple docstring"""





						A_		:    List[Any]         =				parent
						A_		:    List[str]         =				batch_size
						A_		:    Union[str, Any]         =				image_size
						A_		:    List[str]         =				patch_size
						A_		:    int         =				num_channels
						A_		:    Optional[int]         =				is_training
						A_		:    Optional[int]         =				use_labels
						A_		:    Tuple         =				hidden_size
						A_		:    Tuple         =				num_hidden_layers
						A_		:    Dict         =				num_attention_heads
						A_		:    List[Any]         =				intermediate_size
						A_		:    Any         =				hidden_act
						A_		:    List[Any]         =				hidden_dropout_prob
						A_		:    Tuple         =				attention_probs_dropout_prob
						A_		:    Optional[Any]         =				type_sequence_label_size
						A_		:    Any         =				initializer_range
						A_		:    List[Any]         =				scope

						# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						A_		:    Optional[Any]         =				(image_size // patch_size) ** 2
						A_		:    str         =				num_patches + 1
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    str         =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

						A_		:    Dict         =				None
						if self.use_labels:
								A_		:    int         =				ids_tensor([self.batch_size]		,     self.type_sequence_label_size )

						A_		:    List[Any]         =				self.get_config()

						return config, pixel_values, labels
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return ViTConfig(
						    image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     is_decoder=lowercase		,     initializer_range=self.initializer_range		,     )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    Any         =				TFViTModel(config=lowercase )
						A_		:    List[str]         =				model(lowercase		,     training=lowercase )
						self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size) )

						# Test with an image with different size than the one specified in config.
						A_		:    str         =				self.image_size // 2
						A_		:    Any         =				pixel_values[:, :, :image_size, :image_size]
						A_		:    Optional[int]         =				model(lowercase		,     interpolate_pos_encoding=lowercase		,     training=lowercase )
						A_		:    Optional[Any]         =				(image_size // self.patch_size) ** 2 + 1
						self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, seq_length, self.hidden_size) )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    Any         =				self.type_sequence_label_size
						A_		:    int         =				TFViTForImageClassification(lowercase )
						A_		:    List[str]         =				model(lowercase		,     labels=lowercase		,     training=lowercase )
						self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size) )

						# Test with an image with different size than the one specified in config.
						A_		:    str         =				self.image_size // 2
						A_		:    Union[str, Any]         =				pixel_values[:, :, :image_size, :image_size]
						A_		:    List[Any]         =				model(lowercase		,     interpolate_pos_encoding=lowercase		,     training=lowercase )
						self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size) )

						# test greyscale images
						A_		:    Dict         =				1
						A_		:    List[Any]         =				TFViTForImageClassification(lowercase )
						A_		:    int         =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
						A_		:    List[Any]         =				model(lowercase )
						self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size) )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				self.prepare_config_and_inputs()
						A_       ,      A_       ,      A_		:    Dict         =				config_and_inputs
						A_		:    Optional[Any]         =				{'pixel_values': pixel_values}
						return config, inputs_dict





@require_tf
class UpperCAmelCase						( __A ,	__A ,	unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
				lowerCamelCase_    = (
				    {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
				    if is_tf_available()
				    else {}
				)

				lowerCamelCase_    = False
				lowerCamelCase_    = False
				lowerCamelCase_    = False
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				TFViTModelTester(self )
						A_		:    List[str]         =				ConfigTester(self		,     config_class=lowercase		,     has_text_modality=lowercase		,     hidden_size=3_7 )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						self.config_tester.run_common_tests()
				@unittest.skip(reason='ViT does not use inputs_embeds' )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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



						"""simple docstring"""





						pass
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_       ,      A_		:    List[Any]         =				self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
								A_		:    int         =				model_class(lowercase )
								self.assertIsInstance(model.get_input_embeddings()		,     (tf.keras.layers.Layer) )
								A_		:    List[str]         =				model.get_output_embeddings()
								self.assertTrue(x is None or isinstance(lowercase		,     tf.keras.layers.Layer ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_       ,      A_		:    int         =				self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
								A_		:    Dict         =				model_class(lowercase )
								A_		:    Tuple         =				inspect.signature(model.call )
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								A_		:    List[Any]         =				[*signature.parameters.keys()]

								A_		:    Union[str, Any]         =				['pixel_values']
								self.assertListEqual(arg_names[:1]		,     lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowercase )







				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				TFViTModel.from_pretrained('google/vit-base-patch16-224' )
						self.assertIsNotNone(lowercase )




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Optional[Any]         =				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 ):



						"""simple docstring"""





						return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224' ) if is_vision_available() else None







				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224' )

						A_		:    str         =				self.default_image_processor
						A_		:    Dict         =				prepare_img()
						A_		:    List[str]         =				image_processor(images=lowercase		,     return_tensors='tf' )

						# forward pass
						A_		:    Tuple         =				model(**lowercase )

						# verify the logits
						A_		:    Optional[int]         =				tf.TensorShape((1, 1_0_0_0) )
						self.assertEqual(outputs.logits.shape		,     lowercase )

						A_		:    Any         =				tf.constant([-0.2744, 0.8215, -0.0836] )

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







import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


_UpperCAmelCase            =		{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
	_UpperCAmelCase            =		"""hopper-medium-v2"""
	_UpperCAmelCase            =		gym.make(env_name)

	_UpperCAmelCase            =		ValueGuidedRLPipeline.from_pretrained(
	    """bglick13/hopper-medium-v2-value-function-hor32""",
	    env=env,
	)

	env.seed(0)
	_UpperCAmelCase            =		env.reset()
	_UpperCAmelCase            =		0
	_UpperCAmelCase            =		0
	_UpperCAmelCase            =		1000
	_UpperCAmelCase            =		[obs.copy()]
	try:
		for t in tqdm.tqdm(range(T)):
			# call the policy
			_UpperCAmelCase            =		pipeline(obs, planning_horizon=32)

			# execute action in environment
			_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase            =		env.step(denorm_actions)
			_UpperCAmelCase            =		env.get_normalized_score(total_reward)

			# update return
			total_reward += reward
			total_score += score
			print(
			    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
			    F""" {total_score}"""
			)

			# save observations for rendering
			rollout.append(next_observation.copy())

			_UpperCAmelCase            =		next_observation
	except KeyboardInterrupt:
		pass

	print(F"""Total reward: {total_reward}""")






