



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


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import SwinaSRImageProcessor
		from transformers.image_transforms import get_image_size



class     __snake_case						(					unittest.TestCase						):




							def __init__(	self					:	List[Any]    ,    A_					:	str    ,    A_					:	Optional[int]=7    ,    A_					:	int=3    ,    A_					:	Union[str, Any]=1_8    ,    A_					:	str=3_0    ,    A_					:	List[Any]=4_0_0    ,    A_					:	Optional[Any]=True    ,    A_					:	List[Any]=1 / 2_5_5    ,    A_					:	int=True    ,    A_					:	Dict=8    ,    ):
									lowerCAmelCase_      :					int     =			parent
									lowerCAmelCase_      :					Union[str, Any]     =			batch_size
									lowerCAmelCase_      :					Optional[int]     =			num_channels
									lowerCAmelCase_      :					Tuple     =			image_size
									lowerCAmelCase_      :					int     =			min_resolution
									lowerCAmelCase_      :					List[str]     =			max_resolution
									lowerCAmelCase_      :					Union[str, Any]     =			do_rescale
									lowerCAmelCase_      :					Dict     =			rescale_factor
									lowerCAmelCase_      :					int     =			do_pad
									lowerCAmelCase_      :					int     =			pad_size




							def        UpperCAmelCase__      (	self					:	Any):
									return {
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									    "pad_size": self.pad_size,
									}







							def        UpperCAmelCase__      (	self					:	Dict    ,    A_					:	Dict=False    ,    A_					:	int=False    ,    A_					:	int=False):
									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

									if equal_resolution:
											lowerCAmelCase_      :					Optional[int]     =			[]
											for i in range(self.batch_size):
													image_inputs.append(
													    np.random.randint(
													        2_5_5    ,    size=(self.num_channels, self.max_resolution, self.max_resolution)    ,    dtype=np.uinta))
									else:
											lowerCAmelCase_      :					Optional[Any]     =			[]
											for i in range(self.batch_size):
													lowerCAmelCase_      :					Optional[Any]     =			np.random.choice(np.arange(self.min_resolution    ,    self.max_resolution)    ,    2)
													image_inputs.append(np.random.randint(2_5_5    ,    size=(self.num_channels, width, height)    ,    dtype=np.uinta))

									if not numpify and not torchify:
											# PIL expects the channel dimension as last dimension
											lowerCAmelCase_      :					Dict     =			[Image.fromarray(np.moveaxis(A_    ,    0    ,    -1)) for x in image_inputs]

									if torchify:
											lowerCAmelCase_      :					Union[str, Any]     =			[torch.from_numpy(A_) for x in image_inputs]

									return image_inputs





@require_torch
@require_vision
class     __snake_case						(					UpperCamelCase_						,unittest.TestCase						):
							_a      				=			SwinaSRImageProcessor if is_vision_available() else None




							def        UpperCAmelCase__      (	self					:	int):
									lowerCAmelCase_      :					Optional[int]     =			SwinaSRImageProcessingTester(self)




							@property
							def        UpperCAmelCase__      (	self					:	List[Any]):
									return self.image_processor_tester.prepare_image_processor_dict()




							def        UpperCAmelCase__      (	self					:	Optional[Any]):
									lowerCAmelCase_      :					Tuple     =			self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(A_    ,    '''do_rescale'''))
									self.assertTrue(hasattr(A_    ,    '''rescale_factor'''))
									self.assertTrue(hasattr(A_    ,    '''do_pad'''))
									self.assertTrue(hasattr(A_    ,    '''pad_size'''))




							def        UpperCAmelCase__      (	self					:	List[str]):
									pass




							def        UpperCAmelCase__      (	self					:	Optional[int]    ,    A_					:	List[Any]):
									lowerCAmelCase_      :					Any     =			get_image_size(A_)
									lowerCAmelCase_      :					Any     =			self.image_processor_tester.pad_size

									lowerCAmelCase_      :					str     =			(old_height // size + 1) * size - old_height
									lowerCAmelCase_      :					Union[str, Any]     =			(old_width // size + 1) * size - old_width
									return old_height + pad_height, old_width + pad_width




							def        UpperCAmelCase__      (	self					:	Dict):
									# Initialize image_processor
									lowerCAmelCase_      :					Any     =			self.image_processing_class(**self.image_processor_dict)
									# create random PIL images
									lowerCAmelCase_      :					Any     =			self.image_processor_tester.prepare_inputs(equal_resolution=A_)
									for image in image_inputs:
											self.assertIsInstance(A_    ,    Image.Image)

									# Test not batched input
									lowerCAmelCase_      :					List[str]     =			image_processor(image_inputs[0]    ,    return_tensors='''pt''').pixel_values
									lowerCAmelCase_      :					Optional[Any]     =			self.calculate_expected_size(np.array(image_inputs[0]))
									self.assertEqual(
									    encoded_images.shape    ,    (
									        1,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )    ,    )




							def        UpperCAmelCase__      (	self					:	Dict):
									# Initialize image_processor
									lowerCAmelCase_      :					Dict     =			self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									lowerCAmelCase_      :					Union[str, Any]     =			self.image_processor_tester.prepare_inputs(equal_resolution=A_    ,    numpify=A_)
									for image in image_inputs:
											self.assertIsInstance(A_    ,    np.ndarray)

									# Test not batched input
									lowerCAmelCase_      :					Union[str, Any]     =			image_processor(image_inputs[0]    ,    return_tensors='''pt''').pixel_values
									lowerCAmelCase_      :					Any     =			self.calculate_expected_size(image_inputs[0])
									self.assertEqual(
									    encoded_images.shape    ,    (
									        1,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )    ,    )







							def        UpperCAmelCase__      (	self					:	List[Any]):
									# Initialize image_processor
									lowerCAmelCase_      :					int     =			self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									lowerCAmelCase_      :					Union[str, Any]     =			self.image_processor_tester.prepare_inputs(equal_resolution=A_    ,    torchify=A_)
									for image in image_inputs:
											self.assertIsInstance(A_    ,    torch.Tensor)

									# Test not batched input
									lowerCAmelCase_      :					List[Any]     =			image_processor(image_inputs[0]    ,    return_tensors='''pt''').pixel_values
									lowerCAmelCase_      :					Optional[Any]     =			self.calculate_expected_size(image_inputs[0])
									self.assertEqual(
									    encoded_images.shape    ,    (
									        1,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )    ,    )










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


A__  :  Tuple       = logging.get_logger(__name__)

A__  :  Tuple       = {
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}



class     __snake_case						(					UpperCamelCase_						):
							_a      				=			'''opt'''
							_a      				=			['''past_key_values''']




							def __init__(	self					:	Optional[int]    ,    A_					:	Any=5_0_2_7_2    ,    A_					:	Tuple=7_6_8    ,    A_					:	Optional[Any]=1_2    ,    A_					:	str=3_0_7_2    ,    A_					:	Dict=2_0_4_8    ,    A_					:	List[Any]=True    ,    A_					:	Optional[int]=False    ,    A_					:	Optional[int]=None    ,    A_					:	Dict=0.1    ,    A_					:	str=0.0    ,    A_					:	Union[str, Any]=1_2    ,    A_					:	List[str]="relu"    ,    A_					:	Dict=0.0    ,    A_					:	Union[str, Any]=0.02    ,    A_					:	Optional[Any]=True    ,    A_					:	Optional[Any]=1    ,    A_					:	List[Any]=2    ,    A_					:	Dict=2    ,    A_					:	Optional[int]=True    ,    A_					:	Optional[int]=True    ,    **A_					:	List[Any]    ,    ):
									super().__init__(
									    pad_token_id=A_    ,    bos_token_id=A_    ,    eos_token_id=A_    ,    **A_    ,    )
									lowerCAmelCase_      :					List[Any]     =			vocab_size
									lowerCAmelCase_      :					Union[str, Any]     =			max_position_embeddings
									lowerCAmelCase_      :					Optional[int]     =			num_attention_heads
									lowerCAmelCase_      :					int     =			word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
									lowerCAmelCase_      :					List[Any]     =			ffn_dim
									lowerCAmelCase_      :					Any     =			hidden_size
									lowerCAmelCase_      :					Union[str, Any]     =			num_hidden_layers
									lowerCAmelCase_      :					str     =			dropout
									lowerCAmelCase_      :					List[str]     =			attention_dropout
									lowerCAmelCase_      :					Any     =			activation_function
									lowerCAmelCase_      :					Union[str, Any]     =			init_std
									lowerCAmelCase_      :					int     =			layerdrop
									lowerCAmelCase_      :					Optional[int]     =			use_cache
									lowerCAmelCase_      :					int     =			do_layer_norm_before
									# We keep these variables at `True` for backward compatibility.
									lowerCAmelCase_      :					Optional[Any]     =			enable_bias
									lowerCAmelCase_      :					Optional[int]     =			layer_norm_elementwise_affine

									# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
									# with checkpoints that have been fine-tuned before transformers v4.20.1
									# see https://github.com/facebookresearch/metaseq/pull/164
									lowerCAmelCase_      :					Optional[Any]     =			_remove_final_layer_norm





