





'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A=7 ,		A=3 ,		A=18 ,		A=30 ,		A=400 ,		A=True ,		A=None ,		A=True ,		A=False ,		A=True ,		A=True ,		A=[0.5, 0.5, 0.5] ,		A=[0.5, 0.5, 0.5] ,		)		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			parent
        _SCREAMING_SNAKE_CASE									=			batch_size
        _SCREAMING_SNAKE_CASE									=			num_channels
        _SCREAMING_SNAKE_CASE									=			image_size
        _SCREAMING_SNAKE_CASE									=			min_resolution
        _SCREAMING_SNAKE_CASE									=			max_resolution
        _SCREAMING_SNAKE_CASE									=			do_resize
        _SCREAMING_SNAKE_CASE									=			size if size is not None else {"""height""": 18, """width""": 20}
        _SCREAMING_SNAKE_CASE									=			do_thumbnail
        _SCREAMING_SNAKE_CASE									=			do_align_axis
        _SCREAMING_SNAKE_CASE									=			do_pad
        _SCREAMING_SNAKE_CASE									=			do_normalize
        _SCREAMING_SNAKE_CASE									=			image_mean
        _SCREAMING_SNAKE_CASE									=			image_std





    def 			snake_case_(		self       )		->   Dict:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_thumbnail": self.do_thumbnail,
            "do_align_long_axis": self.do_align_axis,
            "do_pad": self.do_pad,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
        }




@require_torch
@require_vision
class 					a_     (				snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = DonutImageProcessor if is_vision_available() else None


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			DonutImageProcessingTester(self       )


    @property
    def 			snake_case_(		self       )		->   Optional[int]:
        return self.image_processor_tester.prepare_image_processor_dict()


    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			self.image_processing_class(**self.image_processor_dict       )
        self.assertTrue(hasattr(A ,		"""do_resize"""       )       )
        self.assertTrue(hasattr(A ,		"""size"""       )       )
        self.assertTrue(hasattr(A ,		"""do_thumbnail"""       )       )
        self.assertTrue(hasattr(A ,		"""do_align_long_axis"""       )       )
        self.assertTrue(hasattr(A ,		"""do_pad"""       )       )
        self.assertTrue(hasattr(A ,		"""do_normalize"""       )       )
        self.assertTrue(hasattr(A ,		"""image_mean"""       )       )
        self.assertTrue(hasattr(A ,		"""image_std"""       )       )


    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			self.image_processing_class.from_dict(self.image_processor_dict       )
        self.assertEqual(image_processor.size ,		{"""height""": 18, """width""": 20}       )

        _SCREAMING_SNAKE_CASE									=			self.image_processing_class.from_dict(self.image_processor_dict ,		size=42       )
        self.assertEqual(image_processor.size ,		{"""height""": 42, """width""": 42}       )

        # Previous config had dimensions in (width, height) order
        _SCREAMING_SNAKE_CASE									=			self.image_processing_class.from_dict(self.image_processor_dict ,		size=(42, 84)       )
        self.assertEqual(image_processor.size ,		{"""height""": 84, """width""": 42}       )


    def 			snake_case_(		self       )		->   Optional[int]:
        pass


    @is_flaky()
    def 			snake_case_(		self       )		->   Optional[int]:
        # Initialize image_processing
        _SCREAMING_SNAKE_CASE									=			self.image_processing_class(**self.image_processor_dict       )
        # create random PIL images
        _SCREAMING_SNAKE_CASE									=			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=A       )
        for image in image_inputs:
            self.assertIsInstance(A ,		Image.Image       )

        # Test not batched input
        _SCREAMING_SNAKE_CASE									=			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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            ) ,		)

        # Test batched
        _SCREAMING_SNAKE_CASE									=			image_processing(A ,		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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            ) ,		)


    @is_flaky()
    def 			snake_case_(		self       )		->   List[Any]:
        # Initialize image_processing
        _SCREAMING_SNAKE_CASE									=			self.image_processing_class(**self.image_processor_dict       )
        # create random numpy tensors
        _SCREAMING_SNAKE_CASE									=			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=A ,		numpify=A       )
        for image in image_inputs:
            self.assertIsInstance(A ,		np.ndarray       )

        # Test not batched input
        _SCREAMING_SNAKE_CASE									=			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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            ) ,		)

        # Test batched
        _SCREAMING_SNAKE_CASE									=			image_processing(A ,		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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            ) ,		)





    @is_flaky()
    def 			snake_case_(		self       )		->   Any:
        # Initialize image_processing
        _SCREAMING_SNAKE_CASE									=			self.image_processing_class(**self.image_processor_dict       )
        # create random PyTorch tensors
        _SCREAMING_SNAKE_CASE									=			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=A ,		torchify=A       )
        for image in image_inputs:
            self.assertIsInstance(A ,		torch.Tensor       )

        # Test not batched input
        _SCREAMING_SNAKE_CASE									=			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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            ) ,		)

        # Test batched
        _SCREAMING_SNAKE_CASE									=			image_processing(A ,		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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            ) ,		)











"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''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 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''rwkv'''
	__magic_name__	      =				{'''max_position_embeddings''': '''context_length'''}





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

				UpperCAmelCase_     : List[str]			     =					bos_token_id
				UpperCAmelCase_     : Union[str, Any]			     =					eos_token_id

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



