




'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
 from huggingface_hub import snapshot_download
 from pyctcdecode import BeamSearchDecoderCTC

 from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
 from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
 from transformers import WavaVecaForCTC







@require_pyctcdecode
class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							int     						=  """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
           a_     :							Union[str, Any]     						=  dict(zip(lowercase__						,						range(len(lowercase__       )       )       )       )

           a_     :							List[Any]     						=  {
               """unk_token""": """<unk>""",
               """bos_token""": """<s>""",
               """eos_token""": """</s>""",
           }
           a_     :							Any     						=  {
               """feature_size""": 1,
               """padding_value""": 0.0,
               """sampling_rate""": 1_6000,
               """return_attention_mask""": False,
               """do_normalize""": True,
           }

           a_     :							Optional[int]     						=  tempfile.mkdtemp()
           a_     :							Dict     						=  os.path.join(self.tmpdirname						,						VOCAB_FILES_NAMES["""vocab_file"""]       )
           a_     :							List[str]     						=  os.path.join(self.tmpdirname						,						lowercase__       )
           with open(self.vocab_file						,						"""w"""						,						encoding="""utf-8"""       ) as fp:
                fp.write(json.dumps(lowercase__       ) + """\n"""       )

           with open(self.feature_extraction_file						,						"""w"""						,						encoding="""utf-8"""       ) as fp:
                fp.write(json.dumps(lowercase__       ) + """\n"""       )

           # load decoder from hub
           a_     :							Optional[Any]     						=  """hf-internal-testing/ngram-beam-search-decoder"""






      def 							lowercase_						( self   :  Dict						,						**lowercase__   :  Tuple       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  self.add_kwargs_tokens_map.copy()
           kwargs.update(lowercase__       )
           return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname						,						**lowercase__       )






      def 							lowercase_						( self   :  int						,						**lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname						,						**lowercase__       )






      def 							lowercase_						( self   :  Any						,						**lowercase__   :  List[str]       ):


           '''simple docstring'''

           return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name						,						**lowercase__       )






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           shutil.rmtree(self.tmpdirname       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  self.get_tokenizer()
           a_     :							Optional[Any]     						=  self.get_feature_extractor()
           a_     :							Tuple     						=  self.get_decoder()

           a_     :							Tuple     						=  WavaVecaProcessorWithLM(tokenizer=lowercase__						,						feature_extractor=lowercase__						,						decoder=lowercase__       )

           processor.save_pretrained(self.tmpdirname       )
           a_     :							Dict     						=  WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname       )

           # tokenizer
           self.assertEqual(processor.tokenizer.get_vocab()						,						tokenizer.get_vocab()       )
           self.assertIsInstance(processor.tokenizer						,						lowercase__       )

           # feature extractor
           self.assertEqual(processor.feature_extractor.to_json_string()						,						feature_extractor.to_json_string()       )
           self.assertIsInstance(processor.feature_extractor						,						lowercase__       )

           # decoder
           self.assertEqual(processor.decoder._alphabet.labels						,						decoder._alphabet.labels       )
           self.assertEqual(
               processor.decoder.model_container[decoder._model_key]._unigram_set						,						decoder.model_container[decoder._model_key]._unigram_set						,						)
           self.assertIsInstance(processor.decoder						,						lowercase__       )






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							str     						=  WavaVecaProcessorWithLM(
               tokenizer=self.get_tokenizer()						,						feature_extractor=self.get_feature_extractor()						,						decoder=self.get_decoder()       )
           processor.save_pretrained(self.tmpdirname       )

           # make sure that error is thrown when decoder alphabet doesn't match
           a_     :							Dict     						=  WavaVecaProcessorWithLM.from_pretrained(
               self.tmpdirname						,						alpha=5.0						,						beta=3.0						,						score_boundary=-7.0						,						unk_score_offset=3       )

           # decoder
           self.assertEqual(processor.language_model.alpha						,						5.0       )
           self.assertEqual(processor.language_model.beta						,						3.0       )
           self.assertEqual(processor.language_model.score_boundary						,						-7.0       )
           self.assertEqual(processor.language_model.unk_score_offset						,						3       )






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Any     						=  self.get_tokenizer()
           # add token to trigger raise
           tokenizer.add_tokens(["""xx"""]       )
           with self.assertRaisesRegex(lowercase__						,						"""include"""       ):
                WavaVecaProcessorWithLM(
                    tokenizer=lowercase__						,						feature_extractor=self.get_feature_extractor()						,						decoder=self.get_decoder()       )






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							int     						=  self.get_feature_extractor()
           a_     :							Union[str, Any]     						=  self.get_tokenizer()
           a_     :							str     						=  self.get_decoder()

           a_     :							Optional[Any]     						=  WavaVecaProcessorWithLM(tokenizer=lowercase__						,						feature_extractor=lowercase__						,						decoder=lowercase__       )

           a_     :							Any     						=  floats_list((3, 1000)       )

           a_     :							Optional[Any]     						=  feature_extractor(lowercase__						,						return_tensors="""np"""       )
           a_     :							List[Any]     						=  processor(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 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.get_feature_extractor()
           a_     :							Optional[int]     						=  self.get_tokenizer()
           a_     :							List[str]     						=  self.get_decoder()

           a_     :							Optional[Any]     						=  WavaVecaProcessorWithLM(tokenizer=lowercase__						,						feature_extractor=lowercase__						,						decoder=lowercase__       )

           a_     :							List[str]     						=  """This is a test string"""

           a_     :							Optional[Any]     						=  processor(text=lowercase__       )

           a_     :							Dict     						=  tokenizer(lowercase__       )

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






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  int=(2, 10, 16)						,						lowercase__   :  str=77       ):


           '''simple docstring'''

           np.random.seed(lowercase__       )
           return np.random.rand(*lowercase__       )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.get_feature_extractor()
           a_     :							Union[str, Any]     						=  self.get_tokenizer()
           a_     :							Tuple     						=  self.get_decoder()

           a_     :							str     						=  WavaVecaProcessorWithLM(tokenizer=lowercase__						,						feature_extractor=lowercase__						,						decoder=lowercase__       )

           a_     :							Optional[Any]     						=  self._get_dummy_logits(shape=(10, 16)						,						seed=13       )

           a_     :							Union[str, Any]     						=  processor.decode(lowercase__       )

           a_     :							Optional[int]     						=  decoder.decode_beams(lowercase__       )[0]

           self.assertEqual(decoded_decoder[0]						,						decoded_processor.text       )
           self.assertEqual("""</s> <s> </s>"""						,						decoded_processor.text       )
           self.assertEqual(decoded_decoder[-2]						,						decoded_processor.logit_score       )
           self.assertEqual(decoded_decoder[-1]						,						decoded_processor.lm_score       )






      @parameterized.expand([[None], ["""fork"""], ["""spawn"""]]       )
      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  List[Any]       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  self.get_feature_extractor()
           a_     :							List[str]     						=  self.get_tokenizer()
           a_     :							Union[str, Any]     						=  self.get_decoder()

           a_     :							Any     						=  WavaVecaProcessorWithLM(tokenizer=lowercase__						,						feature_extractor=lowercase__						,						decoder=lowercase__       )

           a_     :							Union[str, Any]     						=  self._get_dummy_logits()

           # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
           #       otherwise, the LM won't be available to the pool's sub-processes.
           # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
           if pool_context is None:
                a_     :							Union[str, Any]     						=  processor.batch_decode(lowercase__       )
           else:
                with get_context(lowercase__       ).Pool() as pool:
                     a_     :							Optional[int]     						=  processor.batch_decode(lowercase__						,						lowercase__       )

           a_     :							Any     						=  list(lowercase__       )

           with get_context("""fork"""       ).Pool() as p:
                a_     :							List[Any]     						=  decoder.decode_beams_batch(lowercase__						,						lowercase__       )

           a_						,     a_						,     a_     :							Dict     						=  [], [], []
           for beams in decoded_beams:
                texts_decoder.append(beams[0][0]       )
                logit_scores_decoder.append(beams[0][-2]       )
                lm_scores_decoder.append(beams[0][-1]       )

           self.assertListEqual(lowercase__						,						decoded_processor.text       )
           self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""]						,						decoded_processor.text       )
           self.assertListEqual(lowercase__						,						decoded_processor.logit_score       )
           self.assertListEqual(lowercase__						,						decoded_processor.lm_score       )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  self.get_feature_extractor()
           a_     :							int     						=  self.get_tokenizer()
           a_     :							Union[str, Any]     						=  self.get_decoder()

           a_     :							Union[str, Any]     						=  WavaVecaProcessorWithLM(tokenizer=lowercase__						,						feature_extractor=lowercase__						,						decoder=lowercase__       )

           a_     :							Any     						=  self._get_dummy_logits()

           a_     :							Any     						=  15
           a_     :							List[str]     						=  -20.0
           a_     :							Tuple     						=  -4.0

           a_     :							Dict     						=  processor.batch_decode(
               lowercase__						,						beam_width=lowercase__						,						beam_prune_logp=lowercase__						,						token_min_logp=lowercase__						,						)
           a_     :							List[Any]     						=  decoded_processor_out.text

           a_     :							int     						=  list(lowercase__       )

           with get_context("""fork"""       ).Pool() as pool:
                a_     :							Optional[int]     						=  decoder.decode_beams_batch(
                    lowercase__						,						lowercase__						,						beam_width=lowercase__						,						beam_prune_logp=lowercase__						,						token_min_logp=lowercase__						,						)

           a_     :							Any     						=  [d[0][0] for d in decoded_decoder_out]
           a_     :							Dict     						=  [d[0][2] for d in decoded_decoder_out]
           a_     :							Optional[int]     						=  [d[0][3] for d in decoded_decoder_out]

           self.assertListEqual(lowercase__						,						lowercase__       )
           self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""]						,						lowercase__       )

           self.assertTrue(np.array_equal(lowercase__						,						decoded_processor_out.logit_score       )       )
           self.assertTrue(np.allclose([-20.054, -18.447]						,						lowercase__						,						atol=1e-3       )       )

           self.assertTrue(np.array_equal(lowercase__						,						decoded_processor_out.lm_score       )       )
           self.assertTrue(np.allclose([-15.554, -13.9474]						,						lowercase__						,						atol=1e-3       )       )






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Tuple     						=  self.get_feature_extractor()
           a_     :							Any     						=  self.get_tokenizer()
           a_     :							List[Any]     						=  self.get_decoder()

           a_     :							List[str]     						=  WavaVecaProcessorWithLM(tokenizer=lowercase__						,						feature_extractor=lowercase__						,						decoder=lowercase__       )

           a_     :							List[Any]     						=  self._get_dummy_logits()

           a_     :							List[Any]     						=  2.0
           a_     :							Optional[int]     						=  5.0
           a_     :							Union[str, Any]     						=  -20.0
           a_     :							Optional[int]     						=  True

           a_     :							Any     						=  processor.batch_decode(
               lowercase__						,						alpha=lowercase__						,						beta=lowercase__						,						unk_score_offset=lowercase__						,						lm_score_boundary=lowercase__						,						)
           a_     :							str     						=  decoded_processor_out.text

           a_     :							Any     						=  list(lowercase__       )
           decoder.reset_params(
               alpha=lowercase__						,						beta=lowercase__						,						unk_score_offset=lowercase__						,						lm_score_boundary=lowercase__						,						)

           with get_context("""fork"""       ).Pool() as pool:
                a_     :							Optional[int]     						=  decoder.decode_beams_batch(
                    lowercase__						,						lowercase__						,						)

           a_     :							Union[str, Any]     						=  [d[0][0] for d in decoded_decoder_out]

           self.assertListEqual(lowercase__						,						lowercase__       )
           self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""]						,						lowercase__       )
           a_     :							Any     						=  processor.decoder.model_container[processor.decoder._model_key]
           self.assertEqual(lm_model.alpha						,						2.0       )
           self.assertEqual(lm_model.beta						,						5.0       )
           self.assertEqual(lm_model.unk_score_offset						,						-20.0       )
           self.assertEqual(lm_model.score_boundary						,						lowercase__       )






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Tuple     						=  WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""       )

           a_     :							Union[str, Any]     						=  processor.decoder.model_container[processor.decoder._model_key]
           a_     :							str     						=  Path(language_model._kenlm_model.path.decode("""utf-8"""       )       ).parent.parent.absolute()

           a_     :							Dict     						=  os.listdir(lowercase__       )
           a_     :							Optional[int]     						=  ["""alphabet.json""", """language_model"""]

           downloaded_decoder_files.sort()
           expected_decoder_files.sort()

           # test that only decoder relevant files from
           # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
           # are downloaded and none of the rest (e.g. README.md, ...)
           self.assertListEqual(lowercase__						,						lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							str     						=  snapshot_download("""hf-internal-testing/processor_with_lm"""       )

           a_     :							Optional[Any]     						=  WavaVecaProcessorWithLM.from_pretrained(lowercase__       )

           a_     :							List[str]     						=  processor.decoder.model_container[processor.decoder._model_key]
           a_     :							Optional[Any]     						=  Path(language_model._kenlm_model.path.decode("""utf-8"""       )       ).parent.parent.absolute()

           a_     :							Optional[int]     						=  os.listdir(lowercase__       )
           a_     :							int     						=  os.listdir(lowercase__       )

           local_decoder_files.sort()
           expected_decoder_files.sort()

           # test that both decoder form hub and local files in cache are the same
           self.assertListEqual(lowercase__						,						lowercase__       )






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Any     						=  WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""       )
           a_     :							Union[str, Any]     						=  AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""       )

           a_     :							List[str]     						=  floats_list((3, 1000)       )

           a_     :							Tuple     						=  processor_wavaveca(lowercase__						,						return_tensors="""np"""       )
           a_     :							Optional[Any]     						=  processor_auto(lowercase__						,						return_tensors="""np"""       )

           for key in input_wavaveca.keys():
                self.assertAlmostEqual(input_wavaveca[key].sum()						,						input_auto[key].sum()						,						delta=1e-2       )

           a_     :							int     						=  self._get_dummy_logits()

           a_     :							Optional[Any]     						=  processor_wavaveca.batch_decode(lowercase__       )
           a_     :							Tuple     						=  processor_auto.batch_decode(lowercase__       )

           self.assertListEqual(decoded_wavaveca.text						,						decoded_auto.text       )






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							int     						=  self.get_feature_extractor()
           a_     :							Optional[int]     						=  self.get_tokenizer()
           a_     :							Union[str, Any]     						=  self.get_decoder()

           a_     :							Optional[Any]     						=  WavaVecaProcessorWithLM(tokenizer=lowercase__						,						feature_extractor=lowercase__						,						decoder=lowercase__       )

           self.assertListEqual(
               processor.model_input_names						,						feature_extractor.model_input_names						,						msg="""`processor` and `feature_extractor` model input names do not match"""						,						)






      @staticmethod
      def 							lowercase_						( lowercase__   :  Optional[Any]						,						lowercase__   :  List[Any]       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  [d[key] for d in offsets]
           return retrieved_list






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""       )
           a_     :							List[Any]     						=  self._get_dummy_logits()[0]

           a_     :							Optional[Any]     						=  processor.decode(lowercase__						,						output_word_offsets=lowercase__       )
           # check Wav2Vec2CTCTokenizerOutput keys for word
           self.assertEqual(len(outputs.keys()       )						,						4       )
           self.assertTrue("""text""" in outputs       )
           self.assertTrue("""word_offsets""" in outputs       )
           self.assertTrue(isinstance(lowercase__						,						lowercase__       )       )

           self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""]						,						"""word"""       )       )						,						outputs.text       )
           self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]						,						"""word"""       )						,						["""<s>""", """<s>""", """</s>"""]       )
           self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]						,						"""start_offset"""       )						,						[0, 2, 4]       )
           self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]						,						"""end_offset"""       )						,						[1, 3, 5]       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							str     						=  WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""       )
           a_     :							Optional[Any]     						=  self._get_dummy_logits()

           a_     :							Optional[Any]     						=  processor.batch_decode(lowercase__						,						output_word_offsets=lowercase__       )

           # check Wav2Vec2CTCTokenizerOutput keys for word
           self.assertEqual(len(outputs.keys()       )						,						4       )
           self.assertTrue("""text""" in outputs       )
           self.assertTrue("""word_offsets""" in outputs       )
           self.assertTrue(isinstance(lowercase__						,						lowercase__       )       )

           self.assertListEqual(
               [""" """.join(self.get_from_offsets(lowercase__						,						"""word"""       )       ) for o in outputs["""word_offsets"""]]						,						outputs.text       )
           self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]						,						"""word"""       )						,						["""<s>""", """<s>""", """</s>"""]       )
           self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]						,						"""start_offset"""       )						,						[0, 2, 4]       )
           self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]						,						"""end_offset"""       )						,						[1, 3, 5]       )






      @slow
      @require_torch
      @require_torchaudio
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           import torch

           a_     :							List[str]     						=  load_dataset("""common_voice"""						,						"""en"""						,						split="""train"""						,						streaming=lowercase__       )
           a_     :							List[str]     						=  ds.cast_column("""audio"""						,						datasets.Audio(sampling_rate=1_6000       )       )
           a_     :							List[Any]     						=  iter(lowercase__       )
           a_     :							Any     						=  next(lowercase__       )

           a_     :							str     						=  AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""       )
           a_     :							List[Any]     						=  WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""       )

           # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
           a_     :							int     						=  processor(sample["""audio"""]["""array"""]						,						return_tensors="""pt"""       ).input_values

           with torch.no_grad():
                a_     :							List[Any]     						=  model(lowercase__       ).logits.cpu().numpy()

           a_     :							Optional[Any]     						=  processor.decode(logits[0]						,						output_word_offsets=lowercase__       )

           a_     :							List[Any]     						=  model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
           a_     :							str     						=  [
               {
                   """start_time""": d["""start_offset"""] * time_offset,
                   """end_time""": d["""end_offset"""] * time_offset,
                   """word""": d["""word"""],
               }
               for d in output["""word_offsets"""]
           ]

           a_     :							Union[str, Any]     						=  """WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""

           # output words
           self.assertEqual(""" """.join(self.get_from_offsets(lowercase__						,						"""word"""       )       )						,						lowercase__       )
           self.assertEqual(""" """.join(self.get_from_offsets(lowercase__						,						"""word"""       )       )						,						output.text       )

           # output times
           a_     :							List[Any]     						=  torch.tensor(self.get_from_offsets(lowercase__						,						"""start_time"""       )       )
           a_     :							Any     						=  torch.tensor(self.get_from_offsets(lowercase__						,						"""end_time"""       )       )

           # fmt: off
           a_     :							Any     						=  torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599]       )
           a_     :							Optional[int]     						=  torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94]       )
           # fmt: on

           self.assertTrue(torch.allclose(lowercase__						,						lowercase__						,						atol=0.01       )       )
           self.assertTrue(torch.allclose(lowercase__						,						lowercase__						,						atol=0.01       )       )






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase_      :  Optional[Any]				     =  {
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  int				     =  ['MobileViTFeatureExtractor']
 lowerCAmelCase_      :  int				     =  ['MobileViTImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  str				     =  [
     'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MobileViTForImageClassification',
     'MobileViTForSemanticSegmentation',
     'MobileViTModel',
     'MobileViTPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  Optional[Any]				     =  [
     'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMobileViTForImageClassification',
     'TFMobileViTForSemanticSegmentation',
     'TFMobileViTModel',
     'TFMobileViTPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_mobilevit import MobileViTFeatureExtractor
  from .image_processing_mobilevit import MobileViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevit import (
      MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTForImageClassification,
      MobileViTForSemanticSegmentation,
      MobileViTModel,
      MobileViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mobilevit import (
      TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMobileViTForImageClassification,
      TFMobileViTForSemanticSegmentation,
      TFMobileViTModel,
      TFMobileViTPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_      :  List[Any]				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
