

"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor





@require_tokenizers
@require_vision
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   tempfile.mkdtemp()

						# fmt: off
						SCREAMING_SNAKE_CASE__										=   ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
						# fmt: on
						SCREAMING_SNAKE_CASE__										=   os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["""vocab_file"""]							)
						with open(self.vocab_file     ,					"""w"""     ,					encoding="""utf-8"""							) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)

						SCREAMING_SNAKE_CASE__										=   {
						    """do_resize""": True,
						    """size""": {"""height""": 1_8, """width""": 1_8},
						    """do_normalize""": True,
						    """image_mean""": [0.5, 0.5, 0.5],
						    """image_std""": [0.5, 0.5, 0.5],
						}
						SCREAMING_SNAKE_CASE__										=   os.path.join(self.tmpdirname     ,					__UpperCAmelCase							)
						with open(self.image_processor_file     ,					"""w"""     ,					encoding="""utf-8"""							) as fp:
									json.dump(__UpperCAmelCase     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					**__UpperCAmelCase					:    Tuple							) ->     Dict:
						return BertTokenizer.from_pretrained(self.tmpdirname     ,					**__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    str     ,					**__UpperCAmelCase					:    Union[str, Any]							) ->     Dict:
						return ViTImageProcessor.from_pretrained(self.tmpdirname     ,					**__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Union[str, Any]:
						shutil.rmtree(self.tmpdirname							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   [np.random.randint(2_5_5     ,					size=(3, 3_0, 4_0_0)     ,					dtype=np.uinta							)]

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

						return image_inputs

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

						SCREAMING_SNAKE_CASE__										=   VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase     ,					image_processor=__UpperCAmelCase							)

						processor.save_pretrained(self.tmpdirname							)
						SCREAMING_SNAKE_CASE__										=   VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname							)

						self.assertEqual(processor.tokenizer.get_vocab()     ,					tokenizer.get_vocab()							)
						self.assertIsInstance(processor.tokenizer     ,					(BertTokenizer, BertTokenizerFast)							)

						self.assertEqual(processor.image_processor.to_json_string()     ,					image_processor.to_json_string()							)
						self.assertIsInstance(processor.image_processor     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   VisionTextDualEncoderProcessor(
						    tokenizer=self.get_tokenizer()     ,					image_processor=self.get_image_processor()							)
						processor.save_pretrained(self.tmpdirname							)

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

						SCREAMING_SNAKE_CASE__										=   VisionTextDualEncoderProcessor.from_pretrained(
						    self.tmpdirname     ,					bos_token="""(BOS)"""     ,					eos_token="""(EOS)"""     ,					do_normalize=__UpperCAmelCase     ,					padding_value=1.0							)

						self.assertEqual(processor.tokenizer.get_vocab()     ,					tokenizer_add_kwargs.get_vocab()							)
						self.assertIsInstance(processor.tokenizer     ,					(BertTokenizer, BertTokenizerFast)							)

						self.assertEqual(processor.image_processor.to_json_string()     ,					image_processor_add_kwargs.to_json_string()							)
						self.assertIsInstance(processor.image_processor     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   self.get_image_processor()
						SCREAMING_SNAKE_CASE__										=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE__										=   VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase     ,					image_processor=__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE__										=   image_processor(__UpperCAmelCase     ,					return_tensors="""np"""							)
						SCREAMING_SNAKE_CASE__										=   processor(images=__UpperCAmelCase     ,					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        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.get_image_processor()
						SCREAMING_SNAKE_CASE__										=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE__										=   VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase     ,					image_processor=__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   """lower newer"""

						SCREAMING_SNAKE_CASE__										=   processor(text=__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   tokenizer(__UpperCAmelCase							)

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

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   self.get_image_processor()
						SCREAMING_SNAKE_CASE__										=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE__										=   VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase     ,					image_processor=__UpperCAmelCase							)

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

						SCREAMING_SNAKE_CASE__										=   processor(text=__UpperCAmelCase     ,					images=__UpperCAmelCase							)

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

						# test if it raises when no input is passed
						with self.assertRaises(__UpperCAmelCase							):
									processor()

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.get_image_processor()
						SCREAMING_SNAKE_CASE__										=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE__										=   VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase     ,					image_processor=__UpperCAmelCase							)

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

						SCREAMING_SNAKE_CASE__										=   processor.batch_decode(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   tokenizer.batch_decode(__UpperCAmelCase							)

						self.assertListEqual(__UpperCAmelCase     ,					__UpperCAmelCase							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   self.get_image_processor()
						SCREAMING_SNAKE_CASE__										=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE__										=   VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase     ,					image_processor=__UpperCAmelCase							)

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

						SCREAMING_SNAKE_CASE__										=   processor(text=__UpperCAmelCase     ,					images=__UpperCAmelCase							)

						self.assertListEqual(list(inputs.keys()							)     ,					processor.model_input_names							)





"""simple docstring"""




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


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

A_				:      Optional[int]           =			{
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[int]       		=				'dpr'

			def __init__(       self					:    Optional[int]     ,					__UpperCAmelCase					:    List[str]=3_0_5_2_2     ,					__UpperCAmelCase					:    int=7_6_8     ,					__UpperCAmelCase					:    Optional[Any]=1_2     ,					__UpperCAmelCase					:    Dict=1_2     ,					__UpperCAmelCase					:    Any=3_0_7_2     ,					__UpperCAmelCase					:    int="gelu"     ,					__UpperCAmelCase					:    int=0.1     ,					__UpperCAmelCase					:    Optional[Any]=0.1     ,					__UpperCAmelCase					:    Any=5_1_2     ,					__UpperCAmelCase					:    Union[str, Any]=2     ,					__UpperCAmelCase					:    Any=0.02     ,					__UpperCAmelCase					:    Dict=1e-12     ,					__UpperCAmelCase					:    Optional[Any]=0     ,					__UpperCAmelCase					:    Union[str, Any]="absolute"     ,					__UpperCAmelCase					:    int = 0     ,					**__UpperCAmelCase					:    Dict     ,					) ->     Tuple:
						super().__init__(pad_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   type_vocab_size
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   projection_dim
						SCREAMING_SNAKE_CASE__										=   position_embedding_type


