
'''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    UpperCAmelCase_				(	unittest.TestCase					):





 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						tempfile.mkdtemp()

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

     snake_case_ :    int     			=						{
         "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],
     }
     snake_case_ :    List[str]     			=						os.path.join(self.tmpdirname ,       lowercase_)
     with open(self.image_processor_file ,       "w" ,       encoding="utf-8") as fp:
         json.dump(lowercase_ ,       lowercase_)





 def        snake_case__		(    self ,       **lowercase_):
     return BertTokenizer.from_pretrained(self.tmpdirname ,       **lowercase_)





 def        snake_case__		(    self ,       **lowercase_):
     return ViTImageProcessor.from_pretrained(self.tmpdirname ,       **lowercase_)





 def        snake_case__		(    self):
     shutil.rmtree(self.tmpdirname)





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						[np.random.randint(2_55 ,       size=(3, 30, 4_00) ,       dtype=np.uinta)]

     snake_case_ :    Tuple     			=						[Image.fromarray(np.moveaxis(lowercase_ ,       0 ,       -1)) for x in image_inputs]

     return image_inputs





 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						self.get_tokenizer()
     snake_case_ :    Optional[Any]     			=						self.get_image_processor()

     snake_case_ :    Dict     			=						VisionTextDualEncoderProcessor(tokenizer=lowercase_ ,       image_processor=lowercase_)

     processor.save_pretrained(self.tmpdirname)
     snake_case_ :    Tuple     			=						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 ,       lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						VisionTextDualEncoderProcessor(
         tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor())
     processor.save_pretrained(self.tmpdirname)

     snake_case_ :    Any     			=						self.get_tokenizer(bos_token="(BOS)" ,       eos_token="(EOS)")
     snake_case_ :    List[Any]     			=						self.get_image_processor(do_normalize=lowercase_ ,       padding_value=1.0)

     snake_case_ :    int     			=						VisionTextDualEncoderProcessor.from_pretrained(
         self.tmpdirname ,       bos_token="(BOS)" ,       eos_token="(EOS)" ,       do_normalize=lowercase_ ,       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 ,       lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						self.get_image_processor()
     snake_case_ :    int     			=						self.get_tokenizer()

     snake_case_ :    Tuple     			=						VisionTextDualEncoderProcessor(tokenizer=lowercase_ ,       image_processor=lowercase_)

     snake_case_ :    int     			=						self.prepare_image_inputs()

     snake_case_ :    Tuple     			=						image_processor(lowercase_ ,       return_tensors="np")
     snake_case_ :    Optional[int]     			=						processor(images=lowercase_ ,       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        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						self.get_image_processor()
     snake_case_ :    Any     			=						self.get_tokenizer()

     snake_case_ :    Optional[int]     			=						VisionTextDualEncoderProcessor(tokenizer=lowercase_ ,       image_processor=lowercase_)

     snake_case_ :    Dict     			=						"lower newer"

     snake_case_ :    Tuple     			=						processor(text=lowercase_)

     snake_case_ :    str     			=						tokenizer(lowercase_)

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





 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						self.get_image_processor()
     snake_case_ :    int     			=						self.get_tokenizer()

     snake_case_ :    int     			=						VisionTextDualEncoderProcessor(tokenizer=lowercase_ ,       image_processor=lowercase_)

     snake_case_ :    Optional[Any]     			=						"lower newer"
     snake_case_ :    Tuple     			=						self.prepare_image_inputs()

     snake_case_ :    Optional[int]     			=						processor(text=lowercase_ ,       images=lowercase_)

     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(lowercase_):
         processor()





 def        snake_case__		(    self):
     snake_case_ :    str     			=						self.get_image_processor()
     snake_case_ :    List[str]     			=						self.get_tokenizer()

     snake_case_ :    List[Any]     			=						VisionTextDualEncoderProcessor(tokenizer=lowercase_ ,       image_processor=lowercase_)

     snake_case_ :    Optional[int]     			=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     snake_case_ :    str     			=						processor.batch_decode(lowercase_)
     snake_case_ :    Optional[int]     			=						tokenizer.batch_decode(lowercase_)

     self.assertListEqual(lowercase_ ,       lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						self.get_image_processor()
     snake_case_ :    List[str]     			=						self.get_tokenizer()

     snake_case_ :    str     			=						VisionTextDualEncoderProcessor(tokenizer=lowercase_ ,       image_processor=lowercase_)

     snake_case_ :    List[Any]     			=						"lower newer"
     snake_case_ :    Dict     			=						self.prepare_image_inputs()

     snake_case_ :    str     			=						processor(text=lowercase_ ,       images=lowercase_)

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






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_											=						{"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "WavLMForAudioFrameClassification",
      "WavLMForCTC",
      "WavLMForSequenceClassification",
      "WavLMForXVector",
      "WavLMModel",
      "WavLMPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  a_											=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




