"""simple docstring"""



import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import ASTForAudioClassification, ASTModel
   from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
       AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
   )


if is_torchaudio_available():
   import torchaudio

   from transformers import ASTFeatureExtractor






class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase=13  ,						lowerCamelCase=2  ,						lowerCamelCase=24  ,						lowerCamelCase=16  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=32  ,						lowerCamelCase=5  ,						lowerCamelCase=4  ,						lowerCamelCase=37  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=10  ,						lowerCamelCase=0.02  ,						lowerCamelCase=None  ,						lowerCamelCase=2  ,						lowerCamelCase=2  ,						):
        __a     				=  parent
        __a     				=  batch_size
        __a     				=  patch_size
        __a     				=  max_length
        __a     				=  num_mel_bins
        __a     				=  is_training
        __a     				=  use_labels
        __a     				=  hidden_size
        __a     				=  num_hidden_layers
        __a     				=  num_attention_heads
        __a     				=  intermediate_size
        __a     				=  hidden_act
        __a     				=  hidden_dropout_prob
        __a     				=  attention_probs_dropout_prob
        __a     				=  type_sequence_label_size
        __a     				=  initializer_range
        __a     				=  scope
        __a     				=  frequency_stride
        __a     				=  time_stride

        # in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
        __a     				=  (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
        __a     				=  (self.max_length - self.patch_size) // self.time_stride + 1
        __a     				=  frequency_out_dimension * time_out_dimension
        __a     				=  num_patches + 2


 def 		a__ (				self		):
        __a     				=  floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]		)

        __a     				=  None
        if self.use_labels:
               __a     				=  ids_tensor([self.batch_size]  ,						self.type_sequence_label_size		)

        __a     				=  self.get_config()

        return config, input_values, labels


 def 		a__ (				self		):
        return ASTConfig(
            patch_size=self.patch_size  ,						max_length=self.max_length  ,						num_mel_bins=self.num_mel_bins  ,						hidden_size=self.hidden_size  ,						num_hidden_layers=self.num_hidden_layers  ,						num_attention_heads=self.num_attention_heads  ,						intermediate_size=self.intermediate_size  ,						hidden_act=self.hidden_act  ,						hidden_dropout_prob=self.hidden_dropout_prob  ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,						is_decoder=lowerCamelCase  ,						initializer_range=self.initializer_range  ,						frequency_stride=self.frequency_stride  ,						time_stride=self.time_stride  ,						)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  ASTModel(config=lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        __a     				=  model(lowerCamelCase		)
        self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size)		)






 def 		a__ (				self		):
        __a     				=  self.prepare_config_and_inputs()
        (
            (
            __a 
        )				, (
            __a 
        )				, (
            __a 
        )				,  
        )     				=  config_and_inputs
        __a     				=  {"input_values": input_values}
        return config, inputs_dict






@require_torch
class 				snake_case__     (   snake_case_,		snake_case_,		unittest.TestCase   ):
 _snake_case   :			Any    			=		(
     (
         ASTModel,
         ASTForAudioClassification,
     )
     if is_torch_available()
     else ()
 )
 _snake_case   :			List[Any]    			=		(
     {"""audio-classification""": ASTForAudioClassification, """feature-extraction""": ASTModel}
     if is_torch_available()
     else {}
 )
 _snake_case   :			Optional[int]    			=		False
 _snake_case   :			int    			=		False
 _snake_case   :			str    			=		False
 _snake_case   :			str    			=		False


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        if pipeline_test_casse_name == "AudioClassificationPipelineTests":
               return True

        return False


 def 		a__ (				self		):
        __a     				=  ASTModelTester(self		)
        __a     				=  ConfigTester(self  ,						config_class=lowerCamelCase  ,						has_text_modality=lowerCamelCase  ,						hidden_size=37		)


 def 		a__ (				self		):
        self.config_tester.run_common_tests()


 @unittest.skip(reason="AST does not use inputs_embeds"		)
 def 		a__ (				self		):
        pass


 def 		a__ (				self		):
        __a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               __a     				=  model_class(lowerCamelCase		)
               self.assertIsInstance(model.get_input_embeddings()  ,						(nn.Module)		)
               __a     				=  model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCamelCase  ,						nn.Linear		)		)


 def 		a__ (				self		):
        __a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               __a     				=  model_class(lowerCamelCase		)
               __a     				=  inspect.signature(model.forward		)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __a     				=  [*signature.parameters.keys()]

               __a     				=  ["input_values"]
               self.assertListEqual(arg_names[:1]  ,						lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase		)






 @slow
 def 		a__ (				self		):
        for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a     				=  ASTModel.from_pretrained(lowerCamelCase		)
               self.assertIsNotNone(lowerCamelCase		)



def 					_lowerCamelCase(					):
       __a     				=  hf_hub_download(
           repo_id="nielsr/audio-spectogram-transformer-checkpoint" ,  filename="sample_audio.flac" ,  repo_type="dataset"							)

       __a				, __a     				=  torchaudio.load(a							)

       return audio, sampling_rate






@require_torch
@require_torchaudio
class 				snake_case__     (   unittest.TestCase   ):


 @cached_property
 def 		a__ (				self		):
        return (
            ASTFeatureExtractor.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593"		)
            if is_torchaudio_available()
            else None
        )






 @slow
 def 		a__ (				self		):
        __a     				=  self.default_feature_extractor
        __a     				=  ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593"		).to(lowerCamelCase		)

        __a     				=  self.default_feature_extractor
        __a				, __a     				=  prepare_audio()
        __a     				=  audio.squeeze().numpy()
        __a     				=  feature_extractor(lowerCamelCase  ,						sampling_rate=lowerCamelCase  ,						return_tensors="pt"		).to(lowerCamelCase		)

        # forward pass
        with torch.no_grad():
               __a     				=  model(**lowerCamelCase		)

        # verify the logits
        __a     				=  torch.Size((1, 527)		)
        self.assertEqual(outputs.logits.shape  ,						lowerCamelCase		)

        __a     				=  torch.tensor([-0.8760, -7.0042, -8.6602]		).to(lowerCamelCase		)

        self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,						lowerCamelCase  ,						atol=1E-4		)		)

"""simple docstring"""



import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__:int			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[Any]			=	{"""vocab_file""": """vocab.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

SCREAMING_SNAKE_CASE__:Optional[Any]			=	{"""mgp-str""": 27}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Tuple           =		VOCAB_FILES_NAMES
 _snake_case   :			Tuple           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[Any]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase="[GO]"  ,						lowerCamelCase="[GO]"  ,						lowerCamelCase="[s]"  ,						lowerCamelCase="[GO]"  ,						**lowerCamelCase		):
        super().__init__(
            unk_token=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						**lowerCamelCase  ,						)

        with open(lowerCamelCase  ,						encoding="utf-8"		) as vocab_handle:
               __a     				=  json.load(lowerCamelCase		)
        __a     				=  {v: k for k, v in self.vocab.items()}


 @property
 def 		a__ (				self		):
        return len(self.vocab		)


 def 		a__ (				self		):
        return dict(self.vocab  ,						**self.added_tokens_encoder		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  []
        for s in text:
               char_tokens.extend(lowerCamelCase		)
        return char_tokens


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.vocab.get(lowerCamelCase  ,						self.vocab.get(self.unk_token		)		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.decoder.get(lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not os.path.isdir(lowerCamelCase		):
               logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase		)		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        with open(lowerCamelCase  ,						"w"  ,						encoding="utf-8"		) as f:
               f.write(json.dumps(self.vocab  ,						indent=2  ,						sort_keys=lowerCamelCase  ,						ensure_ascii=lowerCamelCase		) + "\n"		)

        return (vocab_file,)
