





"""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 		__snake_case (		unittest.TestCase					):





 """simple docstring"""


 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					tempfile.mkdtemp()

     # fmt: off
     __A   :						Union[str, Any]									=					['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
     # fmt: on
     __A   :						Dict									=					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]  )  )

     __A   :						Union[str, 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],
     }
     __A   :						Optional[int]									=					os.path.join(self.tmpdirname ,						__lowerCamelCase  )
     with open(self.image_processor_file ,						'''w''' ,						encoding='''utf-8'''  ) as fp:
         json.dump(__lowerCamelCase ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




     return BertTokenizer.from_pretrained(self.tmpdirname ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




     return ViTImageProcessor.from_pretrained(self.tmpdirname ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     shutil.rmtree(self.tmpdirname  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					[np.random.randint(255 ,						size=(3, 30, 400) ,						dtype=np.uinta  )]

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

     return image_inputs

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					self.get_tokenizer()
     __A   :						int									=					self.get_image_processor()

     __A   :						List[Any]									=					VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase ,						image_processor=__lowerCamelCase  )

     processor.save_pretrained(self.tmpdirname  )
     __A   :						List[Any]									=					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 ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Tuple									=					VisionTextDualEncoderProcessor(
         tokenizer=self.get_tokenizer() ,						image_processor=self.get_image_processor()  )
     processor.save_pretrained(self.tmpdirname  )

     __A   :						Dict									=					self.get_tokenizer(bos_token='''(BOS)''' ,						eos_token='''(EOS)'''  )
     __A   :						Optional[Any]									=					self.get_image_processor(do_normalize=__lowerCamelCase ,						padding_value=1.0  )

     __A   :						Dict									=					VisionTextDualEncoderProcessor.from_pretrained(
         self.tmpdirname ,						bos_token='''(BOS)''' ,						eos_token='''(EOS)''' ,						do_normalize=__lowerCamelCase ,						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 ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					self.get_image_processor()
     __A   :						Optional[Any]									=					self.get_tokenizer()

     __A   :						str									=					VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase ,						image_processor=__lowerCamelCase  )

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

     __A   :						Dict									=					image_processor(__lowerCamelCase ,						return_tensors='''np'''  )
     __A   :						List[Any]									=					processor(images=__lowerCamelCase ,						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  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					self.get_image_processor()
     __A   :						Any									=					self.get_tokenizer()

     __A   :						Optional[int]									=					VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase ,						image_processor=__lowerCamelCase  )

     __A   :						Optional[int]									=					'''lower newer'''

     __A   :						List[str]									=					processor(text=__lowerCamelCase  )

     __A   :						Optional[Any]									=					tokenizer(__lowerCamelCase  )

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

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					self.get_image_processor()
     __A   :						Dict									=					self.get_tokenizer()

     __A   :						Union[str, Any]									=					VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase ,						image_processor=__lowerCamelCase  )

     __A   :						Any									=					'''lower newer'''
     __A   :						Tuple									=					self.prepare_image_inputs()

     __A   :						List[str]									=					processor(text=__lowerCamelCase ,						images=__lowerCamelCase  )

     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(__lowerCamelCase  ):
         processor()

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						str									=					self.get_image_processor()
     __A   :						Dict									=					self.get_tokenizer()

     __A   :						Any									=					VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase ,						image_processor=__lowerCamelCase  )

     __A   :						Dict									=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     __A   :						Optional[int]									=					processor.batch_decode(__lowerCamelCase  )
     __A   :						Optional[Any]									=					tokenizer.batch_decode(__lowerCamelCase  )

     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					self.get_image_processor()
     __A   :						str									=					self.get_tokenizer()

     __A   :						Optional[int]									=					VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase ,						image_processor=__lowerCamelCase  )

     __A   :						List[str]									=					'''lower newer'''
     __A   :						Tuple									=					self.prepare_image_inputs()

     __A   :						List[Any]									=					processor(text=__lowerCamelCase ,						images=__lowerCamelCase  )

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











"""simple docstring"""



import functools
import operator

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


a_			=      logging.get_logger(__name__)

a_			=      {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					"""sew-d"""

 def __init__(   self ,						__lowerCamelCase=32 ,						__lowerCamelCase=768 ,						__lowerCamelCase=12 ,						__lowerCamelCase=12 ,						__lowerCamelCase=3072 ,						__lowerCamelCase=2 ,						__lowerCamelCase=512 ,						__lowerCamelCase=256 ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=("p2c", "c2p") ,						__lowerCamelCase="layer_norm" ,						__lowerCamelCase="gelu_python" ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-7 ,						__lowerCamelCase=1e-5 ,						__lowerCamelCase="group" ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,						__lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,						__lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,						__lowerCamelCase=False ,						__lowerCamelCase=128 ,						__lowerCamelCase=16 ,						__lowerCamelCase=True ,						__lowerCamelCase=0.0_5 ,						__lowerCamelCase=10 ,						__lowerCamelCase=2 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=10 ,						__lowerCamelCase=0 ,						__lowerCamelCase="mean" ,						__lowerCamelCase=False ,						__lowerCamelCase=False ,						__lowerCamelCase=256 ,						__lowerCamelCase=0 ,						__lowerCamelCase=1 ,						__lowerCamelCase=2 ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase ,						pad_token_id=__lowerCamelCase ,						bos_token_id=__lowerCamelCase ,						eos_token_id=__lowerCamelCase  )
     __A   :						str									=					hidden_size
     __A   :						List[Any]									=					feat_extract_norm
     __A   :						Tuple									=					feat_extract_activation
     __A   :						Dict									=					list(__lowerCamelCase  )
     __A   :						int									=					list(__lowerCamelCase  )
     __A   :						List[Any]									=					list(__lowerCamelCase  )
     __A   :						Any									=					conv_bias
     __A   :						List[Any]									=					num_conv_pos_embeddings
     __A   :						Any									=					num_conv_pos_embedding_groups
     __A   :						Optional[Any]									=					len(self.conv_dim  )
     __A   :						int									=					num_hidden_layers
     __A   :						Union[str, Any]									=					intermediate_size
     __A   :						Union[str, Any]									=					squeeze_factor
     __A   :						int									=					max_position_embeddings
     __A   :						Tuple									=					position_buckets
     __A   :						Tuple									=					share_att_key
     __A   :						List[str]									=					relative_attention
     __A   :						Optional[Any]									=					norm_rel_ebd
     __A   :						Dict									=					list(__lowerCamelCase  )
     __A   :						str									=					hidden_act
     __A   :						List[str]									=					num_attention_heads
     __A   :						Union[str, Any]									=					hidden_dropout
     __A   :						Optional[int]									=					attention_dropout
     __A   :						Optional[Any]									=					activation_dropout
     __A   :						List[str]									=					feat_proj_dropout
     __A   :						str									=					final_dropout
     __A   :						Tuple									=					layer_norm_eps
     __A   :						int									=					feature_layer_norm_eps
     __A   :						Optional[int]									=					initializer_range
     __A   :						Dict									=					vocab_size

     if (
         (len(self.conv_stride  ) != self.num_feat_extract_layers)
         or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
         or (len(self.conv_dim  ) != self.num_feat_extract_layers)
     ):
         raise ValueError(
             '''Configuration for convolutional layers is incorrect.'''
             '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
             F"""but is `len(config.conv_dim) = {len(self.conv_dim  )}`, `len(config.conv_stride)"""
             F"""= {len(self.conv_stride  )}`, `len(config.conv_kernel) = {len(self.conv_kernel  )}`."""  )

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     __A   :						int									=					apply_spec_augment
     __A   :						Any									=					mask_time_prob
     __A   :						Optional[int]									=					mask_time_length
     __A   :						Any									=					mask_time_min_masks
     __A   :						int									=					mask_feature_prob
     __A   :						Tuple									=					mask_feature_length
     __A   :						Dict									=					mask_feature_min_masks

     # ctc loss
     __A   :						Tuple									=					ctc_loss_reduction
     __A   :						Union[str, Any]									=					ctc_zero_infinity

     # sequence classification
     __A   :						Tuple									=					use_weighted_layer_sum
     __A   :						List[str]									=					classifier_proj_size







 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return functools.reduce(operator.mul ,						self.conv_stride ,						1  )




