



"""simple docstring"""
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import (
						    AutoProcessor,
						    BertTokenizerFast,
						    BlipImageProcessor,
						    GPTaTokenizer,
						    InstructBlipProcessor,
						    PreTrainedTokenizerFast,
						)
@require_vision
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Any						)   ->	Tuple:
											snake_case_ :List[str]						=			tempfile.mkdtemp()

											snake_case_ :Dict						=			BlipImageProcessor()
											snake_case_ :str						=			GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""						)
											snake_case_ :Optional[Any]						=			BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""						)

											snake_case_ :List[Any]						=			InstructBlipProcessor(snake_case						,     snake_case						,     snake_case						)

											processor.save_pretrained(self.tmpdirname						)






					def    lowerCAmelCase_				(       self:							Optional[int]						,     **snake_case:							Any						)   ->	Any:
											return AutoProcessor.from_pretrained(self.tmpdirname						,     **snake_case						).tokenizer






					def    lowerCAmelCase_				(       self:							int						,     **snake_case:							Optional[Any]						)   ->	Tuple:
											return AutoProcessor.from_pretrained(self.tmpdirname						,     **snake_case						).image_processor






					def    lowerCAmelCase_				(       self:							Tuple						,     **snake_case:							Union[str, Any]						)   ->	Optional[int]:
											return AutoProcessor.from_pretrained(self.tmpdirname						,     **snake_case						).qformer_tokenizer






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Optional[Any]:
											shutil.rmtree(self.tmpdirname						)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Union[str, Any]:
											snake_case_ :str						=			[np.random.randint(255						,     size=(3, 30, 400)						,     dtype=np.uinta						)]

											snake_case_ :Any						=			[Image.fromarray(np.moveaxis(snake_case						,     0						,     -1						)						) for x in image_inputs]

											return image_inputs






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	int:
											snake_case_ :Optional[int]						=			InstructBlipProcessor(
											    tokenizer=self.get_tokenizer()						,     image_processor=self.get_image_processor()						,     qformer_tokenizer=self.get_qformer_tokenizer()						,     )
											processor.save_pretrained(self.tmpdirname						)

											snake_case_ :Optional[Any]						=			self.get_tokenizer(bos_token="""(BOS)"""						,     eos_token="""(EOS)"""						)
											snake_case_ :Optional[Any]						=			self.get_image_processor(do_normalize=snake_case						,     padding_value=1.0						)

											snake_case_ :str						=			InstructBlipProcessor.from_pretrained(
											    self.tmpdirname						,     bos_token="""(BOS)"""						,     eos_token="""(EOS)"""						,     do_normalize=snake_case						,     padding_value=1.0						)

											self.assertEqual(processor.tokenizer.get_vocab()						,     tokenizer_add_kwargs.get_vocab()						)
											self.assertIsInstance(processor.tokenizer						,     snake_case						)

											self.assertEqual(processor.image_processor.to_json_string()						,     image_processor_add_kwargs.to_json_string()						)
											self.assertIsInstance(processor.image_processor						,     snake_case						)
											self.assertIsInstance(processor.qformer_tokenizer						,     snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											snake_case_ :Optional[Any]						=			self.get_image_processor()
											snake_case_ :Dict						=			self.get_tokenizer()
											snake_case_ :int						=			self.get_qformer_tokenizer()

											snake_case_ :str						=			InstructBlipProcessor(
											    tokenizer=snake_case						,     image_processor=snake_case						,     qformer_tokenizer=snake_case						)

											snake_case_ :List[Any]						=			self.prepare_image_inputs()

											snake_case_ :Union[str, Any]						=			image_processor(snake_case						,     return_tensors="""np"""						)
											snake_case_ :List[str]						=			processor(images=snake_case						,     return_tensors="""np"""						)

											for key in input_feat_extract.keys():
																	self.assertAlmostEqual(input_feat_extract[key].sum()						,     input_processor[key].sum()						,     delta=1E-2						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Optional[int]:
											snake_case_ :Optional[int]						=			self.get_image_processor()
											snake_case_ :int						=			self.get_tokenizer()
											snake_case_ :Optional[Any]						=			self.get_qformer_tokenizer()

											snake_case_ :int						=			InstructBlipProcessor(
											    tokenizer=snake_case						,     image_processor=snake_case						,     qformer_tokenizer=snake_case						)

											snake_case_ :Tuple						=			"""lower newer"""

											snake_case_ :str						=			processor(text=snake_case						)

											snake_case_ :List[str]						=			tokenizer(snake_case						,     return_token_type_ids=snake_case						)
											snake_case_ :Any						=			qformer_tokenizer(snake_case						,     return_token_type_ids=snake_case						)

											for key in encoded_tokens.keys():
																	self.assertListEqual(encoded_tokens[key]						,     encoded_processor[key]						)

											for key in encoded_tokens_qformer.keys():
																	self.assertListEqual(encoded_tokens_qformer[key]						,     encoded_processor["""qformer_""" + key]						)






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	List[str]:
											snake_case_ :Optional[Any]						=			self.get_image_processor()
											snake_case_ :Union[str, Any]						=			self.get_tokenizer()
											snake_case_ :List[Any]						=			self.get_qformer_tokenizer()

											snake_case_ :List[Any]						=			InstructBlipProcessor(
											    tokenizer=snake_case						,     image_processor=snake_case						,     qformer_tokenizer=snake_case						)

											snake_case_ :Union[str, Any]						=			"""lower newer"""
											snake_case_ :str						=			self.prepare_image_inputs()

											snake_case_ :Optional[Any]						=			processor(text=snake_case						,     images=snake_case						)

											self.assertListEqual(
											    list(inputs.keys()						)						,     ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]						,     )

											# test if it raises when no input is passed
											with pytest.raises(snake_case						):
																	processor()






					def    lowerCAmelCase_				(       self:							int						)   ->	Tuple:
											snake_case_ :List[Any]						=			self.get_image_processor()
											snake_case_ :List[str]						=			self.get_tokenizer()
											snake_case_ :Optional[Any]						=			self.get_qformer_tokenizer()

											snake_case_ :List[Any]						=			InstructBlipProcessor(
											    tokenizer=snake_case						,     image_processor=snake_case						,     qformer_tokenizer=snake_case						)

											snake_case_ :Optional[Any]						=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											snake_case_ :Tuple						=			processor.batch_decode(snake_case						)
											snake_case_ :str						=			tokenizer.batch_decode(snake_case						)

											self.assertListEqual(snake_case						,     snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Tuple:
											snake_case_ :List[Any]						=			self.get_image_processor()
											snake_case_ :List[str]						=			self.get_tokenizer()
											snake_case_ :str						=			self.get_qformer_tokenizer()

											snake_case_ :List[Any]						=			InstructBlipProcessor(
											    tokenizer=snake_case						,     image_processor=snake_case						,     qformer_tokenizer=snake_case						)

											snake_case_ :Dict						=			"""lower newer"""
											snake_case_ :Tuple						=			self.prepare_image_inputs()

											snake_case_ :int						=			processor(text=snake_case						,     images=snake_case						)

											self.assertListEqual(
											    list(inputs.keys()						)						,     ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]						,     )




'''simple docstring'''






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


__A					:  List[Any]											=      logging.get_logger(__name__)

__A					:  Optional[Any]											=      {
    "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    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					'gpt_neox'

    def __init__(   self    :  Optional[int]    ,						lowerCamelCase    :  Tuple=5_04_32    ,						lowerCamelCase    :  Optional[int]=61_44    ,						lowerCamelCase    :  Tuple=44    ,						lowerCamelCase    :  Any=64    ,						lowerCamelCase    :  List[Any]=2_45_76    ,						lowerCamelCase    :  List[Any]="gelu"    ,						lowerCamelCase    :  Optional[Any]=0.25    ,						lowerCamelCase    :  Any=1_00_00    ,						lowerCamelCase    :  Any=0.0    ,						lowerCamelCase    :  str=0.0    ,						lowerCamelCase    :  Optional[int]=0.1    ,						lowerCamelCase    :  List[Any]=20_48    ,						lowerCamelCase    :  List[Any]=0.02    ,						lowerCamelCase    :  Any=1E-5    ,						lowerCamelCase    :  Dict=True    ,						lowerCamelCase    :  Optional[int]=0    ,						lowerCamelCase    :  List[str]=2    ,						lowerCamelCase    :  Dict=False    ,						lowerCamelCase    :  Tuple=True    ,						lowerCamelCase    :  Optional[int]=None    ,						**lowerCamelCase    :  int    ,						)   ->  Optional[Any]:
      super().__init__(bos_token_id=lowerCamelCase    ,						eos_token_id=lowerCamelCase    ,						**lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   vocab_size
      lowerCAmelCase_			:      Union[str, Any]			=   max_position_embeddings
      lowerCAmelCase_			:      Any			=   hidden_size
      lowerCAmelCase_			:      List[Any]			=   num_hidden_layers
      lowerCAmelCase_			:      Optional[int]			=   num_attention_heads
      lowerCAmelCase_			:      str			=   intermediate_size
      lowerCAmelCase_			:      int			=   hidden_act
      lowerCAmelCase_			:      List[Any]			=   rotary_pct
      lowerCAmelCase_			:      Any			=   rotary_emb_base
      lowerCAmelCase_			:      List[str]			=   attention_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   hidden_dropout
      lowerCAmelCase_			:      Tuple			=   classifier_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   initializer_range
      lowerCAmelCase_			:      Any			=   layer_norm_eps
      lowerCAmelCase_			:      str			=   use_cache
      lowerCAmelCase_			:      str			=   tie_word_embeddings
      lowerCAmelCase_			:      str			=   use_parallel_residual
      lowerCAmelCase_			:      Any			=   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 __lowercase   (   self    :  List[str]		)   ->  List[str]:
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,						lowerCamelCase		) 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}'		)
      lowerCAmelCase_			:      Optional[Any]			=   self.rope_scaling.get("""type"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      int			=   self.rope_scaling.get("""factor"""    ,						lowerCamelCase		)
      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(lowerCamelCase    ,						lowerCamelCase		) or rope_scaling_factor <= 1.0:
        raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'		)



