import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class 					snake_case__       (				_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE	: Dict						    =      ["image_processor", "tokenizer"]
   _SCREAMING_SNAKE_CASE	: Tuple						    =      "AutoImageProcessor"
   _SCREAMING_SNAKE_CASE	: Dict						    =      "AutoTokenizer"



   def __init__(				self   :   int			,	A__   :   List[str]=None			,	A__   :   List[Any]=None			,	**A__   :   List[Any]   )							->     Union[str, Any]:




         '''simple docstring'''




         snake_case_     :			int       =       None
         if "feature_extractor" in kwargs:
               warnings.warn(
                   "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                   " instead."			,	A__			,	)
               snake_case_     :			Optional[int]       =       kwargs.pop("feature_extractor"   )

         snake_case_     :			Optional[Any]       =       image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
               raise ValueError("You need to specify an `image_processor`."   )
         if tokenizer is None:
               raise ValueError("You need to specify a `tokenizer`."   )

         super().__init__(A__			,	A__   )
         snake_case_     :			Any       =       self.image_processor
         snake_case_     :			Tuple       =       False



   def __call__(				self   :   Optional[Any]			,	*A__   :   int			,	**A__   :   Any   )							->     List[Any]:




         '''simple docstring'''




         if self._in_target_context_manager:
               return self.current_processor(*A__			,	**A__   )

         snake_case_     :			str       =       kwargs.pop("images"			,	A__   )
         snake_case_     :			Dict       =       kwargs.pop("text"			,	A__   )
         if len(A__   ) > 0:
               snake_case_     :			Optional[int]       =       args[0]
               snake_case_     :			str       =       args[1:]

         if images is None and text is None:
               raise ValueError("You need to specify either an `images` or `text` input to process."   )

         if images is not None:
               snake_case_     :			Optional[int]       =       self.image_processor(A__			,	*A__			,	**A__   )
         if text is not None:
               snake_case_     :			Union[str, Any]       =       self.tokenizer(A__			,	**A__   )

         if text is None:
               return inputs
         elif images is None:
               return encodings
         else:
               snake_case_     :			int       =       encodings["input_ids"]
               return inputs



   def 					UpperCAmelCase__       (				self   :   int			,	*A__   :   Tuple			,	**A__   :   Tuple   )							->     Optional[int]:




         '''simple docstring'''




         return self.tokenizer.batch_decode(*A__			,	**A__   )



   def 					UpperCAmelCase__       (				self   :   Tuple			,	*A__   :   Dict			,	**A__   :   Tuple   )							->     Union[str, Any]:




         '''simple docstring'''




         return self.tokenizer.decode(*A__			,	**A__   )



   @contextmanager
   def 					UpperCAmelCase__       (				self   :   Tuple   )							->     List[str]:




         '''simple docstring'''




         warnings.warn(
             "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
             "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
             "your images inputs, or in a separate call."   )
         snake_case_     :			Tuple       =       True
         snake_case_     :			Optional[Any]       =       self.tokenizer
         yield
         snake_case_     :			Union[str, Any]       =       self.image_processor
         snake_case_     :			List[str]       =       False



   @property
   def 					UpperCAmelCase__       (				self   :   Dict   )							->     Tuple:




         '''simple docstring'''




         warnings.warn(
             "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,	A__			,	)
         return self.image_processor_class



   @property
   def 					UpperCAmelCase__       (				self   :   Union[str, Any]   )							->     List[Any]:




         '''simple docstring'''




         warnings.warn(
             "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,	A__			,	)
         return self.image_processor




import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
     import torch

     from transformers import GPTaLMHeadModel



@require_torch
@require_sentencepiece
@require_tokenizers
class 					snake_case__       (				unittest.TestCase							):



   @slow
   @unittest.skip("Model is not available."   )
   def 					UpperCAmelCase__       (				self   :   Dict   )							->     Optional[Any]:




         '''simple docstring'''




         snake_case_     :			str       =       "nvidia/megatron-gpt2-345m/"
         if "MYDIR" in os.environ:
               snake_case_     :			int       =       os.path.join(os.environ["MYDIR"]			,	A__   )
         snake_case_     :			str       =       GPTaLMHeadModel.from_pretrained(A__   )
         model.to(A__   )
         model.half()

         snake_case_     :			Optional[int]       =       torch.tensor(
             [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]			,	device=A__			,	dtype=torch.long			,	)

         with torch.no_grad():
               snake_case_     :			Union[str, Any]       =       model(A__   ).logits

         snake_case_     :			List[str]       =       torch.Size((1, 9, 5_02_57)   )
         self.assertEqual(output.shape			,	A__   )

         snake_case_     :			Dict       =       torch.tensor(
             [
                 4.9414,
                 -0.2920,
                 -1.2148,
                 -4.0273,
                 -0.5161,
                 -5.2109,
                 -1.2412,
                 -1.8301,
                 -1.7734,
                 -4.7148,
                 -0.2317,
                 -1.0811,
                 -2.1777,
                 0.4141,
                 -3.7969,
                 -4.0586,
                 -2.5332,
                 -3.3809,
                 4.3867,
             ]			,	device=A__			,	dtype=torch.half			,	)

         for i in range(19   ):
               snake_case_  ,snake_case_     :			Dict       =       8 * i // 17, 27_92 * i  # along the diagonal
               snake_case_  ,snake_case_     :			Any       =       output[0, r, c], expected_diag[i]
               snake_case_     :			Dict       =       f"row={r} col={c} computed={computed} expected={expected}"
               self.assertAlmostEqual(A__			,	A__			,	delta=1E-4			,	msg=A__   )



