






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import MobileViTImageProcessor

class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def __init__(		self				:					Any ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Dict=7 ,  __lowerCamelCase				:					List[str]=3 ,  __lowerCamelCase				:					Optional[Any]=18 ,  __lowerCamelCase				:					List[str]=30 ,  __lowerCamelCase				:					Union[str, Any]=400 ,  __lowerCamelCase				:					Dict=True ,  __lowerCamelCase				:					Optional[int]=None ,  __lowerCamelCase				:					List[str]=True ,  __lowerCamelCase				:					Dict=None ,  __lowerCamelCase				:					Optional[Any]=True ,  ):
		UpperCamelCase     :int          =	size if size is not None else {"""shortest_edge""": 20}
		UpperCamelCase     :Dict          =	crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
		UpperCamelCase     :Any          =	parent
		UpperCamelCase     :Optional[int]          =	batch_size
		UpperCamelCase     :Any          =	num_channels
		UpperCamelCase     :Any          =	image_size
		UpperCamelCase     :int          =	min_resolution
		UpperCamelCase     :List[Any]          =	max_resolution
		UpperCamelCase     :Optional[int]          =	do_resize
		UpperCamelCase     :List[str]          =	size
		UpperCamelCase     :List[Any]          =	do_center_crop
		UpperCamelCase     :List[Any]          =	crop_size
		UpperCamelCase     :List[Any]          =	do_flip_channel_order





	def 						_A (		self				:					Any	):
		return {
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "do_center_crop": self.do_center_crop,
		    "crop_size": self.crop_size,
		    "do_flip_channel_order": self.do_flip_channel_order,
		}

@require_torch
@require_vision
class     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     int     	=  MobileViTImageProcessor if is_vision_available() else None





	def 						_A (		self				:					Union[str, Any]	):
		UpperCamelCase     :List[Any]          =	MobileViTImageProcessingTester(self	)





	@property
	def 						_A (		self				:					List[str]	):
		return self.image_processor_tester.prepare_image_processor_dict()





	def 						_A (		self				:					int	):
		UpperCamelCase     :Any          =	self.image_processing_class(**self.image_processor_dict	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_resize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """size"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_center_crop"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """center_crop"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_flip_channel_order"""	)	)





	def 						_A (		self				:					int	):
		UpperCamelCase     :List[str]          =	self.image_processing_class.from_dict(self.image_processor_dict	)
		self.assertEqual(image_processor.size ,  {"""shortest_edge""": 20}	)
		self.assertEqual(image_processor.crop_size ,  {"""height""": 18, """width""": 18}	)

		UpperCamelCase     :List[Any]          =	self.image_processing_class.from_dict(self.image_processor_dict ,  size=42 ,  crop_size=84	)
		self.assertEqual(image_processor.size ,  {"""shortest_edge""": 42}	)
		self.assertEqual(image_processor.crop_size ,  {"""height""": 84, """width""": 84}	)





	def 						_A (		self				:					Dict	):
		pass





	def 						_A (		self				:					List[str]	):
		# Initialize image_processing
		UpperCamelCase     :Dict          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PIL images
		UpperCamelCase     :Tuple          =	prepare_image_inputs(self.image_processor_tester ,  equal_resolution=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  Image.Image	)

		# Test not batched input
		UpperCamelCase     :List[Any]          =	image_processing(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :Dict          =	image_processing(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    ) ,  )





	def 						_A (		self				:					List[str]	):
		# Initialize image_processing
		UpperCamelCase     :List[Any]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random numpy tensors
		UpperCamelCase     :Optional[Any]          =	prepare_image_inputs(self.image_processor_tester ,  equal_resolution=__lowerCamelCase ,  numpify=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  np.ndarray	)

		# Test not batched input
		UpperCamelCase     :Any          =	image_processing(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :str          =	image_processing(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    ) ,  )





	def 						_A (		self				:					Optional[int]	):
		# Initialize image_processing
		UpperCamelCase     :str          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PyTorch tensors
		UpperCamelCase     :Any          =	prepare_image_inputs(self.image_processor_tester ,  equal_resolution=__lowerCamelCase ,  torchify=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  torch.Tensor	)

		# Test not batched input
		UpperCamelCase     :Optional[int]          =	image_processing(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :str          =	image_processing(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    ) ,  )








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


UpperCAmelCase_ :						List[str]      =      logging.get_logger(__name__)

UpperCAmelCase_ :						int      =      {
    '''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
        '''https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'''
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Any     	=  """trajectory_transformer"""
	snake_case__							:     Optional[Any]     	=  ["""past_key_values"""]
	snake_case__							:     Tuple     	=  {
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					Any=100 ,  __lowerCamelCase				:					str=5 ,  __lowerCamelCase				:					str=1 ,  __lowerCamelCase				:					Optional[int]=1 ,  __lowerCamelCase				:					int=249 ,  __lowerCamelCase				:					str=6 ,  __lowerCamelCase				:					Dict=17 ,  __lowerCamelCase				:					Optional[Any]=25 ,  __lowerCamelCase				:					List[str]=4 ,  __lowerCamelCase				:					str=4 ,  __lowerCamelCase				:					Tuple=128 ,  __lowerCamelCase				:					Dict=0.1 ,  __lowerCamelCase				:					str=0.1 ,  __lowerCamelCase				:					Any=0.1 ,  __lowerCamelCase				:					int=0.0006 ,  __lowerCamelCase				:					List[str]=512 ,  __lowerCamelCase				:					str=0.02 ,  __lowerCamelCase				:					Any=1E-12 ,  __lowerCamelCase				:					int=1 ,  __lowerCamelCase				:					Optional[Any]=True ,  __lowerCamelCase				:					Tuple=1 ,  __lowerCamelCase				:					int=50_256 ,  __lowerCamelCase				:					Union[str, Any]=50_256 ,  **__lowerCamelCase				:					Dict ,  ):
		UpperCamelCase     :Dict          =	vocab_size
		UpperCamelCase     :int          =	action_weight
		UpperCamelCase     :Tuple          =	reward_weight
		UpperCamelCase     :str          =	value_weight
		UpperCamelCase     :Tuple          =	max_position_embeddings
		UpperCamelCase     :Tuple          =	block_size
		UpperCamelCase     :Optional[int]          =	action_dim
		UpperCamelCase     :int          =	observation_dim
		UpperCamelCase     :List[str]          =	transition_dim
		UpperCamelCase     :List[Any]          =	learning_rate
		UpperCamelCase     :Optional[Any]          =	n_layer
		UpperCamelCase     :Any          =	n_head
		UpperCamelCase     :List[str]          =	n_embd
		UpperCamelCase     :Any          =	embd_pdrop
		UpperCamelCase     :str          =	attn_pdrop
		UpperCamelCase     :Union[str, Any]          =	resid_pdrop
		UpperCamelCase     :Optional[Any]          =	initializer_range
		UpperCamelCase     :List[Any]          =	layer_norm_eps
		UpperCamelCase     :Optional[int]          =	kaiming_initializer_range
		UpperCamelCase     :Tuple          =	use_cache
		super().__init__(pad_token_id=__lowerCamelCase ,  bos_token_id=__lowerCamelCase ,  eos_token_id=__lowerCamelCase ,  **__lowerCamelCase	)
