


'''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 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 def   __UpperCamelCase   (    self      )      ->    str:
    _a					:  Optional[int]						=  tempfile.mkdtemp()

    _a					:  List[Any]						=  BlipImageProcessor()
    _a					:  Dict						=  GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'      )

    _a					:  Optional[int]						=  BlipaProcessor(lowerCamelCase_		,      lowerCamelCase_      )

    processor.save_pretrained(self.tmpdirname      )




 def   __UpperCamelCase   (    self		,      **lowerCamelCase_      )      ->    List[Any]:
    return AutoProcessor.from_pretrained(self.tmpdirname		,      **lowerCamelCase_      ).tokenizer




 def   __UpperCamelCase   (    self		,      **lowerCamelCase_      )      ->    Tuple:
    return AutoProcessor.from_pretrained(self.tmpdirname		,      **lowerCamelCase_      ).image_processor




 def   __UpperCamelCase   (    self      )      ->    Dict:
    shutil.rmtree(self.tmpdirname      )




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

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

    return image_inputs




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Union[str, Any]						=  BlipaProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()      )
    processor.save_pretrained(self.tmpdirname      )

    _a					:  List[Any]						=  self.get_tokenizer(bos_token='(BOS)'		,      eos_token='(EOS)'      )
    _a					:  int						=  self.get_image_processor(do_normalize=lowerCamelCase_		,      padding_value=1.0      )

    _a					:  Any						=  BlipaProcessor.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		,      lowerCamelCase_      )

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




 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  Optional[Any]						=  self.get_image_processor()
    _a					:  Optional[Any]						=  self.get_tokenizer()

    _a					:  Optional[int]						=  BlipaProcessor(tokenizer=lowerCamelCase_		,      image_processor=lowerCamelCase_      )

    _a					:  str						=  self.prepare_image_inputs()

    _a					:  str						=  image_processor(lowerCamelCase_		,      return_tensors='np'      )
    _a					:  Tuple						=  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      )      ->    List[str]:
    _a					:  int						=  self.get_image_processor()
    _a					:  Any						=  self.get_tokenizer()

    _a					:  str						=  BlipaProcessor(tokenizer=lowerCamelCase_		,      image_processor=lowerCamelCase_      )

    _a					:  str						=  'lower newer'

    _a					:  Optional[int]						=  processor(text=lowerCamelCase_      )

    _a					:  Optional[int]						=  tokenizer(lowerCamelCase_		,      return_token_type_ids=lowerCamelCase_      )

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




 def   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  str						=  self.get_image_processor()
    _a					:  Dict						=  self.get_tokenizer()

    _a					:  int						=  BlipaProcessor(tokenizer=lowerCamelCase_		,      image_processor=lowerCamelCase_      )

    _a					:  Optional[int]						=  'lower newer'
    _a					:  Tuple						=  self.prepare_image_inputs()

    _a					:  List[Any]						=  processor(text=lowerCamelCase_		,      images=lowerCamelCase_      )

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

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




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  Optional[Any]						=  self.get_image_processor()
    _a					:  Optional[Any]						=  self.get_tokenizer()

    _a					:  Dict						=  BlipaProcessor(tokenizer=lowerCamelCase_		,      image_processor=lowerCamelCase_      )

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

    _a					:  List[str]						=  processor.batch_decode(lowerCamelCase_      )
    _a					:  int						=  tokenizer.batch_decode(lowerCamelCase_      )

    self.assertListEqual(lowerCamelCase_		,      lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    List[str]:
    _a					:  Tuple						=  self.get_image_processor()
    _a					:  Union[str, Any]						=  self.get_tokenizer()

    _a					:  Any						=  BlipaProcessor(tokenizer=lowerCamelCase_		,      image_processor=lowerCamelCase_      )

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

    _a					:  Optional[int]						=  processor(text=lowerCamelCase_		,      images=lowerCamelCase_      )

    # 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'''




import functools
import operator

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


UpperCAmelCase_      :		Any          =		logging.get_logger(__name__)

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







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Tuple							  =		"""sew"""




 def __init__(    self		,      lowerCamelCase_=3_2		,      lowerCamelCase_=7_6_8		,      lowerCamelCase_=1_2		,      lowerCamelCase_=1_2		,      lowerCamelCase_=3_0_7_2		,      lowerCamelCase_=2		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.0		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.02		,      lowerCamelCase_=1e-5		,      lowerCamelCase_="group"		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)		,      lowerCamelCase_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,      lowerCamelCase_=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,      lowerCamelCase_=False		,      lowerCamelCase_=1_2_8		,      lowerCamelCase_=1_6		,      lowerCamelCase_=True		,      lowerCamelCase_=0.05		,      lowerCamelCase_=1_0		,      lowerCamelCase_=2		,      lowerCamelCase_=0.0		,      lowerCamelCase_=1_0		,      lowerCamelCase_=0		,      lowerCamelCase_="mean"		,      lowerCamelCase_=False		,      lowerCamelCase_=False		,      lowerCamelCase_=2_5_6		,      lowerCamelCase_=0		,      lowerCamelCase_=1		,      lowerCamelCase_=2		,      **lowerCamelCase_		,      )      ->    Tuple:
    super().__init__(**lowerCamelCase_		,      pad_token_id=lowerCamelCase_		,      bos_token_id=lowerCamelCase_		,      eos_token_id=lowerCamelCase_      )
    _a					:  Optional[int]						=  hidden_size
    _a					:  int						=  feat_extract_norm
    _a					:  Optional[int]						=  feat_extract_activation
    _a					:  str						=  list(lowerCamelCase_      )
    _a					:  Union[str, Any]						=  list(lowerCamelCase_      )
    _a					:  List[Any]						=  list(lowerCamelCase_      )
    _a					:  Union[str, Any]						=  conv_bias
    _a					:  Optional[int]						=  num_conv_pos_embeddings
    _a					:  Dict						=  num_conv_pos_embedding_groups
    _a					:  str						=  len(self.conv_dim      )
    _a					:  Any						=  num_hidden_layers
    _a					:  List[Any]						=  intermediate_size
    _a					:  Tuple						=  squeeze_factor
    _a					:  Tuple						=  hidden_act
    _a					:  Any						=  num_attention_heads
    _a					:  Optional[int]						=  hidden_dropout
    _a					:  List[str]						=  attention_dropout
    _a					:  Optional[Any]						=  activation_dropout
    _a					:  str						=  feat_proj_dropout
    _a					:  str						=  final_dropout
    _a					:  str						=  layerdrop
    _a					:  Optional[Any]						=  layer_norm_eps
    _a					:  Optional[Any]						=  initializer_range
    _a					:  Any						=  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					:  str						=  apply_spec_augment
    _a					:  List[Any]						=  mask_time_prob
    _a					:  Optional[Any]						=  mask_time_length
    _a					:  Union[str, Any]						=  mask_time_min_masks
    _a					:  List[str]						=  mask_feature_prob
    _a					:  List[str]						=  mask_feature_length
    _a					:  str						=  mask_feature_min_masks

    # ctc loss
    _a					:  Any						=  ctc_loss_reduction
    _a					:  Optional[Any]						=  ctc_zero_infinity

    # sequence classification
    _a					:  List[Any]						=  use_weighted_layer_sum
    _a					:  Tuple						=  classifier_proj_size




 @property
 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    return functools.reduce(operator.mul		,      self.conv_stride		,      1      )


