





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


lowerCAmelCase_   =   logging.get_logger(__name__)

lowerCAmelCase_   =   {
    '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 				A    (UpperCAmelCase_				):
      _SCREAMING_SNAKE_CASE					=							"""opt"""
      _SCREAMING_SNAKE_CASE					=							["""past_key_values"""]



      def __init__(					self     ,  lowercase_=5_0272     ,  lowercase_=768     ,  lowercase_=12     ,  lowercase_=3072     ,  lowercase_=2048     ,  lowercase_=True     ,  lowercase_=False     ,  lowercase_=None     ,  lowercase_=0.1     ,  lowercase_=0.0     ,  lowercase_=12     ,  lowercase_="relu"     ,  lowercase_=0.0     ,  lowercase_=0.02     ,  lowercase_=True     ,  lowercase_=1     ,  lowercase_=2     ,  lowercase_=2     ,  lowercase_=True     ,  lowercase_=True     ,  **lowercase_     ,  )      ->   Dict:



             '''simple docstring'''

             super().__init__(
                 pad_token_id=__UpperCamelCase     ,  bos_token_id=__UpperCamelCase     ,  eos_token_id=__UpperCamelCase     ,  **__UpperCamelCase     ,  )
             _snake_case      :							int     				=						vocab_size
             _snake_case      :							Tuple     				=						max_position_embeddings
             _snake_case      :							Optional[Any]     				=						num_attention_heads
             _snake_case      :							Optional[int]     				=						word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
             _snake_case      :							str     				=						ffn_dim
             _snake_case      :							List[str]     				=						hidden_size
             _snake_case      :							int     				=						num_hidden_layers
             _snake_case      :							Dict     				=						dropout
             _snake_case      :							Any     				=						attention_dropout
             _snake_case      :							int     				=						activation_function
             _snake_case      :							int     				=						init_std
             _snake_case      :							Dict     				=						layerdrop
             _snake_case      :							List[Any]     				=						use_cache
             _snake_case      :							Optional[Any]     				=						do_layer_norm_before
             # We keep these variables at `True` for backward compatibility.
             _snake_case      :							str     				=						enable_bias
             _snake_case      :							Tuple     				=						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
             _snake_case      :							int     				=						_remove_final_layer_norm













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 ConvNextImageProcessor

class 				A    (unittest.TestCase				):



      def __init__(					self     ,  lowercase_     ,  lowercase_=7     ,  lowercase_=3     ,  lowercase_=18     ,  lowercase_=30     ,  lowercase_=400     ,  lowercase_=True     ,  lowercase_=None     ,  lowercase_=0.875     ,  lowercase_=True     ,  lowercase_=[0.5, 0.5, 0.5]     ,  lowercase_=[0.5, 0.5, 0.5]     ,  )      ->   List[str]:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						size if size is not None else {'''shortest_edge''': 20}
             _snake_case      :							List[Any]     				=						parent
             _snake_case      :							Optional[int]     				=						batch_size
             _snake_case      :							str     				=						num_channels
             _snake_case      :							Union[str, Any]     				=						image_size
             _snake_case      :							Optional[Any]     				=						min_resolution
             _snake_case      :							Union[str, Any]     				=						max_resolution
             _snake_case      :							Tuple     				=						do_resize
             _snake_case      :							List[Any]     				=						size
             _snake_case      :							Optional[int]     				=						crop_pct
             _snake_case      :							Any     				=						do_normalize
             _snake_case      :							Optional[int]     				=						image_mean
             _snake_case      :							Union[str, Any]     				=						image_std



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "crop_pct": self.crop_pct,
             }


@require_torch
@require_vision
class 				A    (__UpperCAmelCase       ,unittest.TestCase				):
      _SCREAMING_SNAKE_CASE					=							ConvNextImageProcessor if is_vision_available() else None



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						ConvNextImageProcessingTester(self					)



      @property
      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             return self.image_processor_tester.prepare_image_processor_dict()



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						self.image_processing_class(**self.image_processor_dict					)
             self.assertTrue(hasattr(lowercase_     ,  '''do_resize'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''size'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''crop_pct'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''do_normalize'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''image_mean'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''image_std'''					)					)



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							int     				=						self.image_processing_class.from_dict(self.image_processor_dict					)
             self.assertEqual(image_processor.size     ,  {'''shortest_edge''': 20}					)

             _snake_case      :							Tuple     				=						self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42					)
             self.assertEqual(image_processor.size     ,  {'''shortest_edge''': 42}					)



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             pass



      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random PIL images
             _snake_case      :							List[str]     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  Image.Image					)

             # Test not batched input
             _snake_case      :							List[str]     				=						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['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )

             # Test batched
             _snake_case      :							int     				=						image_processing(lowercase_     ,  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['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

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

             # Test not batched input
             _snake_case      :							List[str]     				=						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['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )

             # Test batched
             _snake_case      :							Union[str, Any]     				=						image_processing(lowercase_     ,  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['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )



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



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random PyTorch tensors
             _snake_case      :							Dict     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  torchify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  torch.Tensor					)

             # Test not batched input
             _snake_case      :							Union[str, Any]     				=						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['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )

             # Test batched
             _snake_case      :							List[Any]     				=						image_processing(lowercase_     ,  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['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )






