




'''simple docstring'''



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


UpperCAmelCase_							:	Optional[int]	     =		logging.get_logger(__name__)

UpperCAmelCase_							:	List[Any]	     =		{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   Tuple										=      '''gpt_neox'''


       def __init__( self     ,	__SCREAMING_SNAKE_CASE=5_0432     ,	__SCREAMING_SNAKE_CASE=6144     ,	__SCREAMING_SNAKE_CASE=44     ,	__SCREAMING_SNAKE_CASE=64     ,	__SCREAMING_SNAKE_CASE=2_4576     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=0.25     ,	__SCREAMING_SNAKE_CASE=1_0000     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=2048     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=1E-5     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=0     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=None     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	Tuple:



            '''simple docstring'''




            super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE     ,	eos_token_id=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	vocab_size
            __snake_case            =	max_position_embeddings
            __snake_case            =	hidden_size
            __snake_case            =	num_hidden_layers
            __snake_case            =	num_attention_heads
            __snake_case            =	intermediate_size
            __snake_case            =	hidden_act
            __snake_case            =	rotary_pct
            __snake_case            =	rotary_emb_base
            __snake_case            =	attention_dropout
            __snake_case            =	hidden_dropout
            __snake_case            =	classifier_dropout
            __snake_case            =	initializer_range
            __snake_case            =	layer_norm_eps
            __snake_case            =	use_cache
            __snake_case            =	tie_word_embeddings
            __snake_case            =	use_parallel_residual
            __snake_case            =	rope_scaling
            self._rope_scaling_validation()

            if self.hidden_size % self.num_attention_heads != 0:
                 raise ValueError(
                     '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''	)




       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            if self.rope_scaling is None:
                 return

            if not isinstance(self.rope_scaling     ,	__SCREAMING_SNAKE_CASE	) or len(self.rope_scaling	) != 2:
                 raise ValueError(
                     '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
                     F'''got {self.rope_scaling}'''	)
            __snake_case            =	self.rope_scaling.get('''type'''     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.rope_scaling.get('''factor'''     ,	__SCREAMING_SNAKE_CASE	)
            if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                 raise ValueError(
                     F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''	)
            if rope_scaling_factor is None or not isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) or rope_scaling_factor <= 1.0:
                 raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''	)












'''simple docstring'''



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      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=18     ,	__SCREAMING_SNAKE_CASE=30     ,	__SCREAMING_SNAKE_CASE=400     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=True     ,	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	size if size is not None else {'''shortest_edge''': 20}
            __snake_case            =	crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	num_channels
            __snake_case            =	image_size
            __snake_case            =	min_resolution
            __snake_case            =	max_resolution
            __snake_case            =	do_resize
            __snake_case            =	size
            __snake_case            =	do_center_crop
            __snake_case            =	crop_size
            __snake_case            =	do_flip_channel_order




       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            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      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Union[str, Any]										=      MobileViTImageProcessor if is_vision_available() else None


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	MobileViTImageProcessingTester(self	)


       @property
       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''do_resize'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''size'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''do_center_crop'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''center_crop'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''do_flip_channel_order'''	)	)


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	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}	)

            __snake_case            =	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    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            pass


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__SCREAMING_SNAKE_CASE	)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	Image.Image	)

            # Test not batched input
            __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.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)

            # Test batched
            __snake_case            =	image_processing(__SCREAMING_SNAKE_CASE     ,	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    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random numpy tensors
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__SCREAMING_SNAKE_CASE     ,	numpify=__SCREAMING_SNAKE_CASE	)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	np.ndarray	)

            # Test not batched input
            __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.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)

            # Test batched
            __snake_case            =	image_processing(__SCREAMING_SNAKE_CASE     ,	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    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random PyTorch tensors
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__SCREAMING_SNAKE_CASE     ,	torchify=__SCREAMING_SNAKE_CASE	)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	torch.Tensor	)

            # Test not batched input
            __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.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)

            # Test batched
            __snake_case            =	image_processing(__SCREAMING_SNAKE_CASE     ,	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'''],
                )     ,	)






