



"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """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       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					int							  =     "xmod"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=30522						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=3072						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-12						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__="absolute"						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=("en_XX",)						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Optional[int]:
				super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__						,	bos_token_id=SCREAMING_SNAKE_CASE__						,	eos_token_id=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)

				A__											=					vocab_size
				A__											=					hidden_size
				A__											=					num_hidden_layers
				A__											=					num_attention_heads
				A__											=					hidden_act
				A__											=					intermediate_size
				A__											=					hidden_dropout_prob
				A__											=					attention_probs_dropout_prob
				A__											=					max_position_embeddings
				A__											=					type_vocab_size
				A__											=					initializer_range
				A__											=					layer_norm_eps
				A__											=					position_embedding_type
				A__											=					use_cache
				A__											=					classifier_dropout
				A__											=					pre_norm
				A__											=					adapter_reduction_factor
				A__											=					adapter_layer_norm
				A__											=					adapter_reuse_layer_norm
				A__											=					ln_before_adapter
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					default_language






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


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











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 	A_      ( self    : str      )     ->      Dict:
     lowerCamelCase__       : Optional[Any]   					=						tempfile.mkdtemp()

     # fmt: off
     lowerCamelCase__       : Tuple   					=						['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
     # fmt: on
     lowerCamelCase__       : int   					=						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]      )      )

     lowerCamelCase__       : Any   					=						{
         'do_resize': True,
         'size': {'height': 18, 'width': 18},
         'do_normalize': True,
         'image_mean': [0.5, 0.5, 0.5],
         'image_std': [0.5, 0.5, 0.5],
     }
     lowerCamelCase__       : int   					=						os.path.join(self.tmpdirname     ,	UpperCAmelCase      )
     with open(self.image_processor_file     ,	'w'     ,	encoding='utf-8'      ) as fp:
         json.dump(UpperCAmelCase     ,	UpperCAmelCase      )
 def 	A_      ( self    : List[str]     ,	**UpperCAmelCase    : str      )     ->      int:
     return BertTokenizer.from_pretrained(self.tmpdirname     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Optional[int]     ,	**UpperCAmelCase    : List[str]      )     ->      List[str]:
     return ViTImageProcessor.from_pretrained(self.tmpdirname     ,	**UpperCAmelCase      )
 def 	A_      ( self    : int      )     ->      Any:
     shutil.rmtree(self.tmpdirname      )
 def 	A_      ( self    : str      )     ->      str:
     lowerCamelCase__       : List[Any]   					=						[np.random.randint(255     ,	size=(3, 30, 400)     ,	dtype=np.uinta      )]

     lowerCamelCase__       : Union[str, Any]   					=						[Image.fromarray(np.moveaxis(UpperCAmelCase     ,	0     ,	-1      )      ) for x in image_inputs]

     return image_inputs
 def 	A_      ( self    : Dict      )     ->      Dict:
     lowerCamelCase__       : List[Any]   					=						self.get_tokenizer()
     lowerCamelCase__       : Tuple   					=						self.get_image_processor()

     lowerCamelCase__       : Union[str, Any]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     processor.save_pretrained(self.tmpdirname      )
     lowerCamelCase__       : List[str]   					=						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 	A_      ( self    : List[str]      )     ->      Tuple:
     lowerCamelCase__       : List[Any]   					=						VisionTextDualEncoderProcessor(
         tokenizer=self.get_tokenizer()     ,	image_processor=self.get_image_processor()      )
     processor.save_pretrained(self.tmpdirname      )

     lowerCamelCase__       : Tuple   					=						self.get_tokenizer(bos_token='(BOS)'     ,	eos_token='(EOS)'      )
     lowerCamelCase__       : int   					=						self.get_image_processor(do_normalize=UpperCAmelCase     ,	padding_value=1.0      )

     lowerCamelCase__       : Union[str, Any]   					=						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 	A_      ( self    : Any      )     ->      Tuple:
     lowerCamelCase__       : List[Any]   					=						self.get_image_processor()
     lowerCamelCase__       : Any   					=						self.get_tokenizer()

     lowerCamelCase__       : Optional[int]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : Optional[int]   					=						self.prepare_image_inputs()

     lowerCamelCase__       : int   					=						image_processor(UpperCAmelCase     ,	return_tensors='np'      )
     lowerCamelCase__       : List[str]   					=						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 	A_      ( self    : Tuple      )     ->      List[Any]:
     lowerCamelCase__       : Dict   					=						self.get_image_processor()
     lowerCamelCase__       : List[str]   					=						self.get_tokenizer()

     lowerCamelCase__       : str   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : Tuple   					=						'lower newer'

     lowerCamelCase__       : Tuple   					=						processor(text=UpperCAmelCase      )

     lowerCamelCase__       : Any   					=						tokenizer(UpperCAmelCase      )

     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]     ,	encoded_processor[key]      )
 def 	A_      ( self    : Optional[int]      )     ->      Any:
     lowerCamelCase__       : Optional[int]   					=						self.get_image_processor()
     lowerCamelCase__       : Tuple   					=						self.get_tokenizer()

     lowerCamelCase__       : Tuple   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : str   					=						'lower newer'
     lowerCamelCase__       : Dict   					=						self.prepare_image_inputs()

     lowerCamelCase__       : int   					=						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 	A_      ( self    : Optional[Any]      )     ->      List[str]:
     lowerCamelCase__       : Dict   					=						self.get_image_processor()
     lowerCamelCase__       : Union[str, Any]   					=						self.get_tokenizer()

     lowerCamelCase__       : List[str]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : List[str]   					=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase__       : Dict   					=						processor.batch_decode(UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						tokenizer.batch_decode(UpperCAmelCase      )

     self.assertListEqual(UpperCAmelCase     ,	UpperCAmelCase      )







 def 	A_      ( self    : Optional[int]      )     ->      List[str]:
     lowerCamelCase__       : Tuple   					=						self.get_image_processor()
     lowerCamelCase__       : List[str]   					=						self.get_tokenizer()

     lowerCamelCase__       : Union[str, Any]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						'lower newer'
     lowerCamelCase__       : List[Any]   					=						self.prepare_image_inputs()

     lowerCamelCase__       : Union[str, Any]   					=						processor(text=UpperCAmelCase     ,	images=UpperCAmelCase      )

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





