
'''simple docstring'''





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


SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     =       {
    """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 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    List[str]				=							"gpt_neox"







 def __init__(					self		:  Tuple			,				snake_case__		:  int=5_04_32			,				snake_case__		:  Any=61_44			,				snake_case__		:  Optional[Any]=44			,				snake_case__		:  List[Any]=64			,				snake_case__		:  Any=2_45_76			,				snake_case__		:  Union[str, Any]="gelu"			,				snake_case__		:  Optional[Any]=0.25			,				snake_case__		:  Tuple=1_00_00			,				snake_case__		:  Optional[int]=0.0			,				snake_case__		:  List[str]=0.0			,				snake_case__		:  Optional[int]=0.1			,				snake_case__		:  int=20_48			,				snake_case__		:  Optional[Any]=0.02			,				snake_case__		:  Tuple=1e-5			,				snake_case__		:  int=True			,				snake_case__		:  Union[str, Any]=0			,				snake_case__		:  List[str]=2			,				snake_case__		:  Dict=False			,				snake_case__		:  int=True			,				snake_case__		:  List[str]=None			,				**snake_case__		:  Optional[Any]			,				):



   '''simple docstring'''




   super().__init__(bos_token_id=snake_case__			,				eos_token_id=snake_case__			,				**snake_case__ )
   UpperCAmelCase__      :					Optional[Any]				       =							vocab_size
   UpperCAmelCase__      :					Any				       =							max_position_embeddings
   UpperCAmelCase__      :					Tuple				       =							hidden_size
   UpperCAmelCase__      :					Dict				       =							num_hidden_layers
   UpperCAmelCase__      :					int				       =							num_attention_heads
   UpperCAmelCase__      :					Any				       =							intermediate_size
   UpperCAmelCase__      :					Optional[int]				       =							hidden_act
   UpperCAmelCase__      :					List[str]				       =							rotary_pct
   UpperCAmelCase__      :					Tuple				       =							rotary_emb_base
   UpperCAmelCase__      :					Tuple				       =							attention_dropout
   UpperCAmelCase__      :					List[str]				       =							hidden_dropout
   UpperCAmelCase__      :					Optional[int]				       =							classifier_dropout
   UpperCAmelCase__      :					Optional[int]				       =							initializer_range
   UpperCAmelCase__      :					str				       =							layer_norm_eps
   UpperCAmelCase__      :					Dict				       =							use_cache
   UpperCAmelCase__      :					Optional[Any]				       =							tie_word_embeddings
   UpperCAmelCase__      :					Union[str, Any]				       =							use_parallel_residual
   UpperCAmelCase__      :					int				       =							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 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   if self.rope_scaling is None:
     return

   if not isinstance(self.rope_scaling			,				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}""" )
   UpperCAmelCase__      :					List[str]				       =							self.rope_scaling.get("type"			,				snake_case__ )
   UpperCAmelCase__      :					str				       =							self.rope_scaling.get("factor"			,				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(snake_case__			,				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 json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
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 ImageGPTImageProcessor






class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def __init__(					self		:  Optional[int]			,				snake_case__		:  int			,				snake_case__		:  Optional[Any]=7			,				snake_case__		:  Tuple=3			,				snake_case__		:  Optional[Any]=18			,				snake_case__		:  Dict=30			,				snake_case__		:  Optional[int]=4_00			,				snake_case__		:  str=True			,				snake_case__		:  str=None			,				snake_case__		:  Optional[int]=True			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							size if size is not None else {"height": 18, "width": 18}
   UpperCAmelCase__      :					List[str]				       =							parent
   UpperCAmelCase__      :					Union[str, Any]				       =							batch_size
   UpperCAmelCase__      :					List[str]				       =							num_channels
   UpperCAmelCase__      :					List[Any]				       =							image_size
   UpperCAmelCase__      :					Tuple				       =							min_resolution
   UpperCAmelCase__      :					Optional[int]				       =							max_resolution
   UpperCAmelCase__      :					Any				       =							do_resize
   UpperCAmelCase__      :					Any				       =							size
   UpperCAmelCase__      :					int				       =							do_normalize







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return {
       # here we create 2 clusters for the sake of simplicity
       "clusters": np.asarray(
           [
               [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
               [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
           ] ),
       "do_resize": self.do_resize,
       "size": self.size,
       "do_normalize": self.do_normalize,
   }






@require_torch
@require_vision
class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Any				=							ImageGPTImageProcessor if is_vision_available() else None







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							ImageGPTImageProcessingTester(self )







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return self.image_processor_tester.prepare_image_processor_dict()







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							self.image_processing_class(**self.image_processor_dict )
   self.assertTrue(hasattr(snake_case__			,				"clusters" ) )
   self.assertTrue(hasattr(snake_case__			,				"do_resize" ) )
   self.assertTrue(hasattr(snake_case__			,				"size" ) )
   self.assertTrue(hasattr(snake_case__			,				"do_normalize" ) )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							self.image_processing_class.from_dict(self.image_processor_dict )
   self.assertEqual(image_processor.size			,				{"height": 18, "width": 18} )

   UpperCAmelCase__      :					Union[str, Any]				       =							self.image_processing_class.from_dict(self.image_processor_dict			,				size=42 )
   self.assertEqual(image_processor.size			,				{"height": 42, "width": 42} )







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.image_processing_class(**self.image_processor_dict )
   UpperCAmelCase__      :					List[str]				       =							json.loads(image_processor.to_json_string() )
   for key, value in self.image_processor_dict.items():
     if key == "clusters":
       self.assertTrue(np.array_equal(snake_case__			,				obj[key] ) )
     else:
       self.assertEqual(obj[key]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							self.image_processing_class(**self.image_processor_dict )

   with tempfile.TemporaryDirectory() as tmpdirname:
     UpperCAmelCase__      :					Union[str, Any]				       =							os.path.join(snake_case__			,				"image_processor.json" )
     image_processor_first.to_json_file(snake_case__ )
     UpperCAmelCase__      :					Any				       =							self.image_processing_class.from_json_file(snake_case__ ).to_dict()

   UpperCAmelCase__      :					Union[str, Any]				       =							image_processor_first.to_dict()
   for key, value in image_processor_first.items():
     if key == "clusters":
       self.assertTrue(np.array_equal(snake_case__			,				image_processor_second[key] ) )
     else:
       self.assertEqual(image_processor_first[key]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.image_processing_class(**self.image_processor_dict )

   with tempfile.TemporaryDirectory() as tmpdirname:
     image_processor_first.save_pretrained(snake_case__ )
     UpperCAmelCase__      :					List[str]				       =							self.image_processing_class.from_pretrained(snake_case__ ).to_dict()

   UpperCAmelCase__      :					Tuple				       =							image_processor_first.to_dict()
   for key, value in image_processor_first.items():
     if key == "clusters":
       self.assertTrue(np.array_equal(snake_case__			,				image_processor_second[key] ) )
     else:
       self.assertEqual(image_processor_first[key]			,				snake_case__ )







 @unittest.skip("ImageGPT requires clusters at initialization" )
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   pass







def snake_case_     (					):
  UpperCAmelCase__      :					List[str]				       =							load_dataset("hf-internal-testing/fixtures_image_utils"   ,       split="test"		)

  UpperCAmelCase__      :					Tuple				       =							Image.open(dataset[4]["file"]		)
  UpperCAmelCase__      :					List[Any]				       =							Image.open(dataset[5]["file"]		)

  UpperCAmelCase__      :					Optional[Any]				       =							[imagea, imagea]

  return images






@require_vision
@require_torch
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 @slow
 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small" )

   UpperCAmelCase__      :					Union[str, Any]				       =							prepare_images()

   # test non-batched
   UpperCAmelCase__      :					Optional[Any]				       =							image_processing(images[0]			,				return_tensors="pt" )

   self.assertIsInstance(encoding.input_ids			,				torch.LongTensor )
   self.assertEqual(encoding.input_ids.shape			,				(1, 10_24) )

   UpperCAmelCase__      :					Union[str, Any]				       =							[3_06, 1_91, 1_91]
   self.assertEqual(encoding.input_ids[0, :3].tolist()			,				snake_case__ )

   # test batched
   UpperCAmelCase__      :					List[Any]				       =							image_processing(snake_case__			,				return_tensors="pt" )

   self.assertIsInstance(encoding.input_ids			,				torch.LongTensor )
   self.assertEqual(encoding.input_ids.shape			,				(2, 10_24) )

   UpperCAmelCase__      :					List[str]				       =							[3_03, 13, 13]
   self.assertEqual(encoding.input_ids[1, -3:].tolist()			,				snake_case__ )



