
import warnings

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


lowerCamelCase__						=				logging.get_logger(__name__)

lowerCamelCase__						=				{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}







class 							lowerCAmelCase__   ( A__						):
						UpperCamelCase_				:			Optional[int]						=		'xlnet'
						UpperCamelCase_				:			Union[str, Any]						=		['mems']
						UpperCamelCase_				:			Union[str, Any]						=		{
						    'n_token': 'vocab_size',  # Backward compatibility
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}

						def __init__(      self							,				a=3_20_00							,				a=10_24							,				a=24							,				a=16							,				a=40_96							,				a="gelu"							,				a=True							,				a="bi"							,				a=0.02							,				a=1e-12							,				a=0.1							,				a=5_12							,				a=None							,				a=True							,				a=False							,				a=False							,				a=-1							,				a=False							,				a="last"							,				a=True							,				a="tanh"							,				a=0.1							,				a=5							,				a=5							,				a=5							,				a=1							,				a=2							,				**a							,				)       ->   Optional[int]:


								'''simple docstring'''

								_UpperCamelCase         =						vocab_size
								_UpperCamelCase         =						d_model
								_UpperCamelCase         =						n_layer
								_UpperCamelCase         =						n_head
								if d_model % n_head != 0:
										raise ValueError(F'\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'					)
								if "d_head" in kwargs:
										if kwargs["d_head"] != d_model // n_head:
												raise ValueError(
												    F'`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})'					)
								_UpperCamelCase         =						d_model // n_head
								_UpperCamelCase         =						ff_activation
								_UpperCamelCase         =						d_inner
								_UpperCamelCase         =						untie_r
								_UpperCamelCase         =						attn_type

								_UpperCamelCase         =						initializer_range
								_UpperCamelCase         =						layer_norm_eps

								_UpperCamelCase         =						dropout
								_UpperCamelCase         =						mem_len
								_UpperCamelCase         =						reuse_len
								_UpperCamelCase         =						bi_data
								_UpperCamelCase         =						clamp_len
								_UpperCamelCase         =						same_length

								_UpperCamelCase         =						summary_type
								_UpperCamelCase         =						summary_use_proj
								_UpperCamelCase         =						summary_activation
								_UpperCamelCase         =						summary_last_dropout
								_UpperCamelCase         =						start_n_top
								_UpperCamelCase         =						end_n_top

								_UpperCamelCase         =						bos_token_id
								_UpperCamelCase         =						pad_token_id
								_UpperCamelCase         =						eos_token_id

								if "use_cache" in kwargs:
										warnings.warn(
										    """The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"""
										    """ instead."""							,				__UpperCAmelCase							,				)
										_UpperCamelCase         =						kwargs["""use_cache"""]

								_UpperCamelCase         =						use_mems_eval
								_UpperCamelCase         =						use_mems_train
								super().__init__(pad_token_id=__UpperCAmelCase							,				bos_token_id=__UpperCAmelCase							,				eos_token_id=__UpperCAmelCase							,				**__UpperCAmelCase					)

						@property
						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								logger.info(F'The model {self.model_type} is one of the few models that has no sequence length limit.'					)
								return -1







						@max_position_embeddings.setter
						def 			A_					(      self							,				a					)       ->   List[str]:


								'''simple docstring'''

								raise NotImplementedError(
								    F'The model {self.model_type} is one of the few models that has no sequence length limit.'					)






"""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					:    Tuple     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Any=7     ,					__UpperCAmelCase					:    Union[str, Any]=3     ,					__UpperCAmelCase					:    Any=1_8     ,					__UpperCAmelCase					:    Tuple=3_0     ,					__UpperCAmelCase					:    List[Any]=4_0_0     ,					__UpperCAmelCase					:    Dict=True     ,					__UpperCAmelCase					:    int=None     ,					__UpperCAmelCase					:    List[Any]=True     ,					__UpperCAmelCase					:    Any=None     ,					__UpperCAmelCase					:    Any=True     ,					) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   size if size is not None else {"""shortest_edge""": 2_0}
						SCREAMING_SNAKE_CASE__										=   crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
						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
						SCREAMING_SNAKE_CASE__										=   do_center_crop
						SCREAMING_SNAKE_CASE__										=   crop_size
						SCREAMING_SNAKE_CASE__										=   do_flip_channel_order





			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     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  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		Tuple       		=				MobileViTImageProcessor if is_vision_available() else None

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   MobileViTImageProcessingTester(self							)

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     int:
						return self.image_processor_tester.prepare_image_processor_dict()

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class(**self.image_processor_dict							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""do_resize"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""size"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""do_center_crop"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""center_crop"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""do_flip_channel_order"""							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class.from_dict(self.image_processor_dict							)
						self.assertEqual(image_processor.size     ,					{"""shortest_edge""": 2_0}							)
						self.assertEqual(image_processor.crop_size     ,					{"""height""": 1_8, """width""": 1_8}							)

						SCREAMING_SNAKE_CASE__										=   self.image_processing_class.from_dict(self.image_processor_dict     ,					size=4_2     ,					crop_size=8_4							)
						self.assertEqual(image_processor.size     ,					{"""shortest_edge""": 4_2}							)
						self.assertEqual(image_processor.crop_size     ,					{"""height""": 8_4, """width""": 8_4}							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Dict:
						pass

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     List[str]:
						# 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=__UpperCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase     ,					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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

						# Test batched
						SCREAMING_SNAKE_CASE__										=   image_processing(__UpperCAmelCase     ,					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        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Optional[int]:
						# 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=__UpperCAmelCase     ,					numpify=__UpperCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase     ,					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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

						# Test batched
						SCREAMING_SNAKE_CASE__										=   image_processing(__UpperCAmelCase     ,					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        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     str:
						# 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=__UpperCAmelCase     ,					torchify=__UpperCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase     ,					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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

						# Test batched
						SCREAMING_SNAKE_CASE__										=   image_processing(__UpperCAmelCase     ,					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"""],
						    )     ,					)


