




"""simple docstring"""

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


a							      =     logging.get_logger(__name__)

a							      =     {
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	List[str]     =      '''rwkv'''
 UpperCAmelCase       :	Tuple     =      {'''max_position_embeddings''': '''context_length'''}

 def __init__(					self			:			Dict       ,				_UpperCAmelCase			:			Union[str, Any]=50_277       ,				_UpperCAmelCase			:			Tuple=1_024       ,				_UpperCAmelCase			:			Dict=4_096       ,				_UpperCAmelCase			:			Optional[int]=32       ,				_UpperCAmelCase			:			List[Any]=None       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			List[Any]=1E-5       ,				_UpperCAmelCase			:			Any=0       ,				_UpperCAmelCase			:			List[str]=0       ,				_UpperCAmelCase			:			Union[str, Any]=6       ,				_UpperCAmelCase			:			Union[str, Any]=False       ,				_UpperCAmelCase			:			Any=True       ,				**_UpperCAmelCase			:			Optional[int]       ,				):
       _A     =  vocab_size
       _A     =  context_length
       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  attention_hidden_size if attention_hidden_size is not None else hidden_size
       _A     =  intermediate_size if intermediate_size is not None else 4 * hidden_size
       _A     =  layer_norm_epsilon
       _A     =  rescale_every
       _A     =  use_cache

       _A     =  bos_token_id
       _A     =  eos_token_id

       super().__init__(
           tie_word_embeddings=_UpperCAmelCase       ,				bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase						)


'''simple docstring'''







import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor




class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						def __init__(    self :       Any							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Dict[str, int] = None							,							UpperCamelCase_ :       int = 32							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Union[int, float] = 1 / 2_55							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]							,							UpperCamelCase_ :       Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       int=7							,							UpperCamelCase_ :       Dict=30							,							UpperCamelCase_ :       Tuple=4_00							,							UpperCamelCase_ :       List[Any]=3							,							)     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :List[str]						    = parent
													SCREAMING_SNAKE_CASE__     :Tuple						    = do_resize
													SCREAMING_SNAKE_CASE__     :List[Any]						    = size if size is not None else {'shortest_edge': 2_88}
													SCREAMING_SNAKE_CASE__     :str						    = size_divisor
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = do_rescale
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = rescale_factor
													SCREAMING_SNAKE_CASE__     :str						    = do_normalize
													SCREAMING_SNAKE_CASE__     :int						    = do_center_crop
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = image_mean
													SCREAMING_SNAKE_CASE__     :str						    = image_std
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = do_pad
													SCREAMING_SNAKE_CASE__     :Tuple						    = batch_size
													SCREAMING_SNAKE_CASE__     :List[str]						    = num_channels
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = min_resolution
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = max_resolution




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[int]:
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "size_divisor": self.size_divisor,
													}




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       str=False      )     ->				Optional[int]:
													if not batched:
																				SCREAMING_SNAKE_CASE__     :Dict						    = self.size['shortest_edge']
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = image_inputs[0]
																				if isinstance(UpperCamelCase_							,							Image.Image      ):
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = image.size
																				else:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = image.shape[1], image.shape[2]
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = size / min(UpperCamelCase_							,							UpperCamelCase_      )
																				if h < w:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[Any]						    = size, scale * w
																				else:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = scale * h, size

																				SCREAMING_SNAKE_CASE__     :Any						    = int((13_33 / 8_00) * size      )
																				if max(UpperCamelCase_							,							UpperCamelCase_      ) > max_size:
																											SCREAMING_SNAKE_CASE__     :Tuple						    = max_size / max(UpperCamelCase_							,							UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :str						    = newh * scale
																											SCREAMING_SNAKE_CASE__     :Any						    = neww * scale

																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = int(newh + 0.5      ), int(neww + 0.5      )
																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[Any]						    = (
																				    newh // self.size_divisor * self.size_divisor,
																				    neww // self.size_divisor * self.size_divisor,
																				)

													else:
																				SCREAMING_SNAKE_CASE__     :Any						    = []
																				for image in image_inputs:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = self.get_expected_values([image]      )
																											expected_values.append((expected_height, expected_width)      )
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = max(UpperCamelCase_							,							key=lambda UpperCamelCase_      : item[0]      )[0]
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = max(UpperCamelCase_							,							key=lambda UpperCamelCase_      : item[1]      )[1]

													return expected_height, expected_width




@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							List[Any]     		= BridgeTowerImageProcessor if is_vision_available() else None




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				str:
													SCREAMING_SNAKE_CASE__     :List[str]						    = BridgeTowerImageProcessingTester(self      )




						@property
						def      __lowerCamelCase       (    self :       int      )     ->				Any:
													return self.image_processor_tester.prepare_image_processor_dict()




						def      __lowerCamelCase       (    self :       int      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processing_class(**self.image_processor_dict      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'image_mean'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'image_std'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'do_normalize'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'do_resize'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'size'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'size_divisor'      )      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				List[str]:
													pass




						def      __lowerCamelCase       (    self :       int      )     ->				Dict:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processing_class(**self.image_processor_dict      )
													# create random PIL images
													SCREAMING_SNAKE_CASE__     :Any						    = prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCamelCase_      )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase_							,							Image.Image      )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :str						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Dict						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :Any						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Tuple:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :Dict						    = self.image_processing_class(**self.image_processor_dict      )
													# create random numpy tensors
													SCREAMING_SNAKE_CASE__     :Tuple						    = prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCamelCase_							,							numpify=UpperCamelCase_      )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase_							,							np.ndarray      )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :List[str]						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :str						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Any						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)




						def      __lowerCamelCase       (    self :       List[str]      )     ->				List[Any]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.image_processing_class(**self.image_processor_dict      )
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCamelCase_							,							torchify=UpperCamelCase_      )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase_							,							torch.Tensor      )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :List[str]						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :List[Any]						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[Any]						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)






