




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 						lowercase							(   unittest.TestCase		):

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     str			=						tempfile.mkdtemp()

           _A							:     List[Any]			=						BlipImageProcessor()
           _A							:     Optional[Any]			=						GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""				)
           _A							:     Tuple			=						BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""				)

           _A							:     Any			=						InstructBlipProcessor(_a		,     _a		,     _a				)

           processor.save_pretrained(self.tmpdirname				)

     def 					a__	(  self		,     **_a				)						->							Optional[Any]:
           return AutoProcessor.from_pretrained(self.tmpdirname		,     **_a				).tokenizer

     def 					a__	(  self		,     **_a				)						->							int:
           return AutoProcessor.from_pretrained(self.tmpdirname		,     **_a				).image_processor

     def 					a__	(  self		,     **_a				)						->							Any:
           return AutoProcessor.from_pretrained(self.tmpdirname		,     **_a				).qformer_tokenizer

     def 					a__	(  self				)						->							int:
           shutil.rmtree(self.tmpdirname				)

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     int			=						[np.random.randint(255		,     size=(3, 30, 400)		,     dtype=np.uinta				)]

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

           return image_inputs

     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     Tuple			=						InstructBlipProcessor(
               tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor()		,     qformer_tokenizer=self.get_qformer_tokenizer()		,     )
           processor.save_pretrained(self.tmpdirname				)

           _A							:     Union[str, Any]			=						self.get_tokenizer(bos_token="""(BOS)"""		,     eos_token="""(EOS)"""				)
           _A							:     Tuple			=						self.get_image_processor(do_normalize=_a		,     padding_value=1.0				)

           _A							:     Tuple			=						InstructBlipProcessor.from_pretrained(
               self.tmpdirname		,     bos_token="""(BOS)"""		,     eos_token="""(EOS)"""		,     do_normalize=_a		,     padding_value=1.0				)

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

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

     def 					a__	(  self				)						->							Dict:
           _A							:     int			=						self.get_image_processor()
           _A							:     Dict			=						self.get_tokenizer()
           _A							:     Dict			=						self.get_qformer_tokenizer()

           _A							:     int			=						InstructBlipProcessor(
               tokenizer=_a		,     image_processor=_a		,     qformer_tokenizer=_a				)

           _A							:     Optional[Any]			=						self.prepare_image_inputs()

           _A							:     List[Any]			=						image_processor(_a		,     return_tensors="""np"""				)
           _A							:     str			=						processor(images=_a		,     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 					a__	(  self				)						->							Union[str, Any]:
           _A							:     Optional[int]			=						self.get_image_processor()
           _A							:     Optional[int]			=						self.get_tokenizer()
           _A							:     List[Any]			=						self.get_qformer_tokenizer()

           _A							:     List[Any]			=						InstructBlipProcessor(
               tokenizer=_a		,     image_processor=_a		,     qformer_tokenizer=_a				)

           _A							:     Optional[int]			=						"""lower newer"""

           _A							:     List[str]			=						processor(text=_a				)

           _A							:     str			=						tokenizer(_a		,     return_token_type_ids=_a				)
           _A							:     List[str]			=						qformer_tokenizer(_a		,     return_token_type_ids=_a				)

           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 					a__	(  self				)						->							List[str]:
           _A							:     Any			=						self.get_image_processor()
           _A							:     List[str]			=						self.get_tokenizer()
           _A							:     Dict			=						self.get_qformer_tokenizer()

           _A							:     List[str]			=						InstructBlipProcessor(
               tokenizer=_a		,     image_processor=_a		,     qformer_tokenizer=_a				)

           _A							:     Tuple			=						"""lower newer"""
           _A							:     Optional[int]			=						self.prepare_image_inputs()

           _A							:     Any			=						processor(text=_a		,     images=_a				)

           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(_a				):
                 processor()

     def 					a__	(  self				)						->							Dict:
           _A							:     Dict			=						self.get_image_processor()
           _A							:     Any			=						self.get_tokenizer()
           _A							:     str			=						self.get_qformer_tokenizer()

           _A							:     int			=						InstructBlipProcessor(
               tokenizer=_a		,     image_processor=_a		,     qformer_tokenizer=_a				)

           _A							:     Tuple			=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _A							:     List[str]			=						processor.batch_decode(_a				)
           _A							:     Optional[Any]			=						tokenizer.batch_decode(_a				)

           self.assertListEqual(_a		,     _a				)



     def 					a__	(  self				)						->							str:
           _A							:     List[Any]			=						self.get_image_processor()
           _A							:     Optional[int]			=						self.get_tokenizer()
           _A							:     int			=						self.get_qformer_tokenizer()

           _A							:     List[Any]			=						InstructBlipProcessor(
               tokenizer=_a		,     image_processor=_a		,     qformer_tokenizer=_a				)

           _A							:     Dict			=						"""lower newer"""
           _A							:     int			=						self.prepare_image_inputs()

           _A							:     int			=						processor(text=_a		,     images=_a				)

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







from collections import OrderedDict
from typing import Mapping

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


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						"xmod"

     def __init__(  self		,     _a=3_0522		,     _a=768		,     _a=12		,     _a=12		,     _a=3072		,     _a="gelu"		,     _a=0.1		,     _a=0.1		,     _a=512		,     _a=2		,     _a=0.02		,     _a=1e-12		,     _a=1		,     _a=0		,     _a=2		,     _a="absolute"		,     _a=True		,     _a=None		,     _a=False		,     _a=2		,     _a=False		,     _a=True		,     _a=True		,     _a=("en_XX",)		,     _a=None		,     **_a		,     )						->							str:
           super().__init__(pad_token_id=_a		,     bos_token_id=_a		,     eos_token_id=_a		,     **_a				)

           _A							:     Tuple			=						vocab_size
           _A							:     Union[str, Any]			=						hidden_size
           _A							:     Dict			=						num_hidden_layers
           _A							:     Dict			=						num_attention_heads
           _A							:     List[Any]			=						hidden_act
           _A							:     Optional[Any]			=						intermediate_size
           _A							:     Any			=						hidden_dropout_prob
           _A							:     str			=						attention_probs_dropout_prob
           _A							:     Dict			=						max_position_embeddings
           _A							:     Any			=						type_vocab_size
           _A							:     List[Any]			=						initializer_range
           _A							:     int			=						layer_norm_eps
           _A							:     int			=						position_embedding_type
           _A							:     Any			=						use_cache
           _A							:     int			=						classifier_dropout
           _A							:     int			=						pre_norm
           _A							:     Optional[Any]			=						adapter_reduction_factor
           _A							:     List[Any]			=						adapter_layer_norm
           _A							:     Optional[int]			=						adapter_reuse_layer_norm
           _A							:     Any			=						ln_before_adapter
           _A							:     Union[str, Any]			=						list(_a				)
           _A							:     List[Any]			=						default_language







class 						lowercase							(   UpperCamelCase__		):

     @property
     def 					a__	(  self				)						->							Mapping[str, Mapping[int, str]]:
           if self.task == "multiple-choice":
                 _A							:     Dict			=						{0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
                 _A							:     Dict			=						{0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
               ]				)

