





'''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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast




@require_vision
class 					UpperCAmelCase	(     unittest.TestCase  ):




      def 							lowercase__       (     self     :					str    )							-> List[Any]:
            _lowerCAmelCase					=				tempfile.mkdtemp()

            _lowerCAmelCase					=				BlipImageProcessor()
            _lowerCAmelCase					=				GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""    )

            _lowerCAmelCase					=				BlipaProcessor(__snake_case     ,     __snake_case    )

            processor.save_pretrained(self.tmpdirname    )




      def 							lowercase__       (     self     :					Optional[int]     ,     **__snake_case     :					Dict    )							-> int:
            return AutoProcessor.from_pretrained(self.tmpdirname     ,     **__snake_case    ).tokenizer




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




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




      def 							lowercase__       (     self     :					List[Any]    )							-> Optional[int]:
            _lowerCAmelCase					=				[np.random.randint(2_55     ,     size=(3, 30, 4_00)     ,     dtype=np.uinta    )]

            _lowerCAmelCase					=				[Image.fromarray(np.moveaxis(__snake_case     ,     0     ,     -1    )    ) for x in image_inputs]

            return image_inputs




      def 							lowercase__       (     self     :					int    )							-> Union[str, Any]:
            _lowerCAmelCase					=				BlipaProcessor(tokenizer=self.get_tokenizer()     ,     image_processor=self.get_image_processor()    )
            processor.save_pretrained(self.tmpdirname    )

            _lowerCAmelCase					=				self.get_tokenizer(bos_token="""(BOS)"""     ,     eos_token="""(EOS)"""    )
            _lowerCAmelCase					=				self.get_image_processor(do_normalize=__snake_case     ,     padding_value=1.0    )

            _lowerCAmelCase					=				BlipaProcessor.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    )




      def 							lowercase__       (     self     :					List[str]    )							-> Optional[int]:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				BlipaProcessor(tokenizer=__snake_case     ,     image_processor=__snake_case    )

            _lowerCAmelCase					=				self.prepare_image_inputs()

            _lowerCAmelCase					=				image_processor(__snake_case     ,     return_tensors="""np"""    )
            _lowerCAmelCase					=				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 							lowercase__       (     self     :					Optional[Any]    )							-> int:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				BlipaProcessor(tokenizer=__snake_case     ,     image_processor=__snake_case    )

            _lowerCAmelCase					=				"""lower newer"""

            _lowerCAmelCase					=				processor(text=__snake_case    )

            _lowerCAmelCase					=				tokenizer(__snake_case     ,     return_token_type_ids=__snake_case    )

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




      def 							lowercase__       (     self     :					Optional[Any]    )							-> str:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				BlipaProcessor(tokenizer=__snake_case     ,     image_processor=__snake_case    )

            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				self.prepare_image_inputs()

            _lowerCAmelCase					=				processor(text=__snake_case     ,     images=__snake_case    )

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

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




      def 							lowercase__       (     self     :					Any    )							-> Union[str, Any]:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				BlipaProcessor(tokenizer=__snake_case     ,     image_processor=__snake_case    )

            _lowerCAmelCase					=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _lowerCAmelCase					=				processor.batch_decode(__snake_case    )
            _lowerCAmelCase					=				tokenizer.batch_decode(__snake_case    )

            self.assertListEqual(__snake_case     ,     __snake_case    )




      def 							lowercase__       (     self     :					List[Any]    )							-> Optional[int]:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				BlipaProcessor(tokenizer=__snake_case     ,     image_processor=__snake_case    )

            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				self.prepare_image_inputs()

            _lowerCAmelCase					=				processor(text=__snake_case     ,     images=__snake_case    )

            # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
            self.assertListEqual(list(inputs.keys()    )     ,     ["""pixel_values""", """input_ids""", """attention_mask"""]    )













'''simple docstring'''






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, TensorType

A__							:  Tuple    =logging.get_logger(__name__)

A__							:  Any    ={
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Tuple			=  '''imagegpt'''
      _lowercase:     Optional[int]			=  ['''past_key_values''']
      _lowercase:     str			=  {
          '''hidden_size''': '''n_embd''',
          '''max_position_embeddings''': '''n_positions''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }




      def __init__(     self     :					Optional[int]     ,     __snake_case     :					Optional[Any]=5_12 + 1     ,     __snake_case     :					Union[str, Any]=32 * 32     ,     __snake_case     :					List[Any]=5_12     ,     __snake_case     :					Any=24     ,     __snake_case     :					Optional[Any]=8     ,     __snake_case     :					List[Any]=None     ,     __snake_case     :					str="quick_gelu"     ,     __snake_case     :					List[Any]=0.1     ,     __snake_case     :					str=0.1     ,     __snake_case     :					Optional[int]=0.1     ,     __snake_case     :					Tuple=1E-5     ,     __snake_case     :					Optional[int]=0.02     ,     __snake_case     :					str=True     ,     __snake_case     :					Dict=True     ,     __snake_case     :					Union[str, Any]=False     ,     __snake_case     :					Dict=False     ,     __snake_case     :					Union[str, Any]=False     ,     **__snake_case     :					Union[str, Any]     ,     )							-> List[Any]:
            _lowerCAmelCase					=				vocab_size
            _lowerCAmelCase					=				n_positions
            _lowerCAmelCase					=				n_embd
            _lowerCAmelCase					=				n_layer
            _lowerCAmelCase					=				n_head
            _lowerCAmelCase					=				n_inner
            _lowerCAmelCase					=				activation_function
            _lowerCAmelCase					=				resid_pdrop
            _lowerCAmelCase					=				embd_pdrop
            _lowerCAmelCase					=				attn_pdrop
            _lowerCAmelCase					=				layer_norm_epsilon
            _lowerCAmelCase					=				initializer_range
            _lowerCAmelCase					=				scale_attn_weights
            _lowerCAmelCase					=				use_cache
            _lowerCAmelCase					=				scale_attn_by_inverse_layer_idx
            _lowerCAmelCase					=				reorder_and_upcast_attn
            _lowerCAmelCase					=				tie_word_embeddings

            super().__init__(tie_word_embeddings=__snake_case     ,     **__snake_case    )




class 					UpperCAmelCase	(     snake_case_  ):




      @property
      def 							lowercase__       (     self     :					Optional[Any]    )							-> Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ]    )




      def 							lowercase__       (     self     :					Union[str, Any]     ,     __snake_case     :					"FeatureExtractionMixin"     ,     __snake_case     :					int = 1     ,     __snake_case     :					int = -1     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional["TensorType"] = None     ,     __snake_case     :					int = 3     ,     __snake_case     :					int = 32     ,     __snake_case     :					int = 32     ,     )							-> Mapping[str, Any]:
            _lowerCAmelCase					=				self._generate_dummy_images(__snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case    )
            _lowerCAmelCase					=				dict(preprocessor(images=__snake_case     ,     return_tensors=__snake_case    )    )

            return inputs






