


import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
 from .features import FeatureType


SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE       :      Any												= False, False, False






@dataclass
class _lowerCamelCase:
       lowercase_ :	Optional[int]     =			None
       lowercase_ :	bool     =			True
       lowercase_ :	bool     =			True
       lowercase_ :	Optional[str]     =			None
       # Automatically constructed
       lowercase_ :	ClassVar[str]     =			"dict"
       lowercase_ :	ClassVar[Any]     =			pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}   )
       lowercase_ :	str     =			field(default="""Audio""", init=_a, repr=_a   )
       def __call__(					self)       ->   List[str]:




           """simple docstring"""

           return self.pa_type
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   dict:




           """simple docstring"""

           try:
               import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
           except ImportError as err:
               raise ImportError('To support encoding audio data, please install \'soundfile\'.') from err
           if isinstance(lowerCamelCase,      lowerCamelCase):
               return {"bytes": None, "path": value}
           elif isinstance(lowerCamelCase,      lowerCamelCase):
               return {"bytes": value, "path": None}
           elif "array" in value:
               # convert the audio array to wav bytes
               _lowercase       :							Optional[Any]      		=		BytesIO()
               sf.write(lowerCamelCase,      value['array'],      value['sampling_rate'],      format='wav')
               return {"bytes": buffer.getvalue(), "path": None}
           elif value.get('path') is not None and os.path.isfile(value['path']):
               # we set "bytes": None to not duplicate the data if they're already available locally
               if value["path"].endswith('pcm'):
                   # "PCM" only has raw audio bytes
                   if value.get('sampling_rate') is None:
                       # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
                       raise KeyError('To use PCM files, please specify a \'sampling_rate\' in Audio object')
                   if value.get('bytes'):
                       # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
                       _lowercase       :							List[Any]      		=		np.frombuffer(value['bytes'],      dtype=np.intaa).astype(np.floataa) / 3_27_67
                   else:
                       _lowercase       :							Dict      		=		np.memmap(value['path'],      dtype='h',      mode='r').astype(np.floataa) / 3_27_67

                   _lowercase       :							List[str]      		=		BytesIO(bytes())
                   sf.write(lowerCamelCase,      lowerCamelCase,      value['sampling_rate'],      format='wav')
                   return {"bytes": buffer.getvalue(), "path": None}
               else:
                   return {"bytes": None, "path": value.get('path')}
           elif value.get('bytes') is not None or value.get('path') is not None:
               # store the audio bytes, and path is used to infer the audio format using the file extension
               return {"bytes": value.get('bytes'), "path": value.get('path')}
           else:
               raise ValueError(
                   F'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''')
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase = None)       ->   dict:




           """simple docstring"""

           if not self.decode:
               raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.')

           _lowercase		,				_lowercase       :							List[Any]      		=		(value['path'], BytesIO(value['bytes'])) if value['bytes'] is not None else (value['path'], None)
           if path is None and file is None:
               raise ValueError(F'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.''')

           try:
               import librosa
               import soundfile as sf
           except ImportError as err:
               raise ImportError('To support decoding audio files, please install \'librosa\' and \'soundfile\'.') from err

           _lowercase       :							Optional[Any]      		=		xsplitext(lowerCamelCase)[1][1:].lower() if path is not None else None
           if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
               raise RuntimeError(
                   'Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '
                   'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ')
           elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
               raise RuntimeError(
                   'Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '
                   'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ')

           if file is None:
               _lowercase       :							int      		=		token_per_repo_id or {}
               _lowercase       :							Optional[int]      		=		path.split('::')[-1]
               try:
                   _lowercase       :							Any      		=		string_to_dict(lowerCamelCase,      config.HUB_DATASETS_URL)['repo_id']
                   _lowercase       :							Optional[Any]      		=		token_per_repo_id[repo_id]
               except (ValueError, KeyError):
                   _lowercase       :							int      		=		None

               with xopen(lowerCamelCase,      'rb',      use_auth_token=lowerCamelCase) as f:
                   _lowercase		,				_lowercase       :							int      		=		sf.read(lowerCamelCase)

           else:
               _lowercase		,				_lowercase       :							Union[str, Any]      		=		sf.read(lowerCamelCase)

           _lowercase       :							Any      		=		array.T
           if self.mono:
               _lowercase       :							List[str]      		=		librosa.to_mono(lowerCamelCase)
           if self.sampling_rate and self.sampling_rate != sampling_rate:
               _lowercase       :							Tuple      		=		librosa.resample(lowerCamelCase,      orig_sr=lowerCamelCase,      target_sr=self.sampling_rate)
               _lowercase       :							List[str]      		=		self.sampling_rate

           return {"path": path, "array": array, "sampling_rate": sampling_rate}
       def 			UpperCamelCase     (					self)       ->   Union["FeatureType", Dict[str, "FeatureType"]]:




           """simple docstring"""

           from .features import Value

           if self.decode:
               raise ValueError('Cannot flatten a decoded Audio feature.')
           return {
               "bytes": Value('binary'),
               "path": Value('string'),
           }
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   pa.StructArray:




           """simple docstring"""

           if pa.types.is_string(storage.type):
               _lowercase       :							List[Any]      		=		pa.array([None] * len(lowerCamelCase),      type=pa.binary())
               _lowercase       :							Union[str, Any]      		=		pa.StructArray.from_arrays([bytes_array, storage],      ['bytes', 'path'],      mask=storage.is_null())
           elif pa.types.is_binary(storage.type):
               _lowercase       :							Tuple      		=		pa.array([None] * len(lowerCamelCase),      type=pa.string())
               _lowercase       :							Dict      		=		pa.StructArray.from_arrays([storage, path_array],      ['bytes', 'path'],      mask=storage.is_null())
           elif pa.types.is_struct(storage.type) and storage.type.get_all_field_indices('array'):
               _lowercase       :							int      		=		pa.array([Audio().encode_example(lowerCamelCase) if x is not None else None for x in storage.to_pylist()])
           elif pa.types.is_struct(storage.type):
               if storage.type.get_field_index('bytes') >= 0:
                   _lowercase       :							Optional[int]      		=		storage.field('bytes')
               else:
                   _lowercase       :							str      		=		pa.array([None] * len(lowerCamelCase),      type=pa.binary())
               if storage.type.get_field_index('path') >= 0:
                   _lowercase       :							Union[str, Any]      		=		storage.field('path')
               else:
                   _lowercase       :							List[Any]      		=		pa.array([None] * len(lowerCamelCase),      type=pa.string())
               _lowercase       :							Any      		=		pa.StructArray.from_arrays([bytes_array, path_array],      ['bytes', 'path'],      mask=storage.is_null())
           return array_cast(lowerCamelCase,      self.pa_type)




       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   pa.StructArray:




           """simple docstring"""

           @no_op_if_value_is_null
           def path_to_bytes(lowerCamelCase):
               with xopen(lowerCamelCase,      'rb') as f:
                   _lowercase       :							int      		=		f.read()
               return bytes_

           _lowercase       :							Optional[Any]      		=		pa.array(
               [
                   (path_to_bytes(x['path']) if x['bytes'] is None else x['bytes']) if x is not None else None
                   for x in storage.to_pylist()
               ],      type=pa.binary(),      )
           _lowercase       :							int      		=		pa.array(
               [os.path.basename(lowerCamelCase) if path is not None else None for path in storage.field('path').to_pylist()],      type=pa.string(),      )
           _lowercase       :							Dict      		=		pa.StructArray.from_arrays([bytes_array, path_array],      ['bytes', 'path'],      mask=bytes_array.is_null())
           return array_cast(lowerCamelCase,      self.pa_type)






import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
 import torch

 from transformers import (
     SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     SqueezeBertForMaskedLM,
     SqueezeBertForMultipleChoice,
     SqueezeBertForQuestionAnswering,
     SqueezeBertForSequenceClassification,
     SqueezeBertForTokenClassification,
     SqueezeBertModel,
 )






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase=13,      lowerCamelCase=7,      lowerCamelCase=True,      lowerCamelCase=True,      lowerCamelCase=False,      lowerCamelCase=True,      lowerCamelCase=99,      lowerCamelCase=32,      lowerCamelCase=5,      lowerCamelCase=4,      lowerCamelCase=64,      lowerCamelCase="gelu",      lowerCamelCase=0.1,      lowerCamelCase=0.1,      lowerCamelCase=5_12,      lowerCamelCase=16,      lowerCamelCase=2,      lowerCamelCase=0.0_2,      lowerCamelCase=3,      lowerCamelCase=4,      lowerCamelCase=None,      lowerCamelCase=2,      lowerCamelCase=2,      lowerCamelCase=2,      lowerCamelCase=2,      lowerCamelCase=4,      lowerCamelCase=1,      )       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Dict      		=		parent
           _lowercase       :							Optional[Any]      		=		batch_size
           _lowercase       :							Any      		=		seq_length
           _lowercase       :							Optional[Any]      		=		is_training
           _lowercase       :							Optional[Any]      		=		use_input_mask
           _lowercase       :							List[Any]      		=		use_token_type_ids
           _lowercase       :							List[str]      		=		use_labels
           _lowercase       :							str      		=		vocab_size
           _lowercase       :							List[str]      		=		hidden_size
           _lowercase       :							Dict      		=		num_hidden_layers
           _lowercase       :							List[str]      		=		num_attention_heads
           _lowercase       :							int      		=		intermediate_size
           _lowercase       :							Union[str, Any]      		=		hidden_act
           _lowercase       :							int      		=		hidden_dropout_prob
           _lowercase       :							List[Any]      		=		attention_probs_dropout_prob
           _lowercase       :							Dict      		=		max_position_embeddings
           _lowercase       :							Union[str, Any]      		=		type_vocab_size
           _lowercase       :							List[Any]      		=		type_sequence_label_size
           _lowercase       :							Any      		=		initializer_range
           _lowercase       :							List[str]      		=		num_labels
           _lowercase       :							Any      		=		num_choices
           _lowercase       :							Tuple      		=		scope
           _lowercase       :							Optional[Any]      		=		q_groups
           _lowercase       :							List[str]      		=		k_groups
           _lowercase       :							Optional[int]      		=		v_groups
           _lowercase       :							List[str]      		=		post_attention_groups
           _lowercase       :							Union[str, Any]      		=		intermediate_groups
           _lowercase       :							int      		=		output_groups
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							int      		=		ids_tensor([self.batch_size, self.seq_length],      self.vocab_size)

           _lowercase       :							Any      		=		None
           if self.use_input_mask:
               _lowercase       :							Tuple      		=		random_attention_mask([self.batch_size, self.seq_length])

           _lowercase       :							Dict      		=		None
           _lowercase       :							int      		=		None
           _lowercase       :							List[Any]      		=		None
           if self.use_labels:
               _lowercase       :							List[Any]      		=		ids_tensor([self.batch_size],      self.type_sequence_label_size)
               _lowercase       :							int      		=		ids_tensor([self.batch_size, self.seq_length],      self.num_labels)
               _lowercase       :							Dict      		=		ids_tensor([self.batch_size],      self.num_choices)

           _lowercase       :							Optional[Any]      		=		self.get_config()

           return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           return SqueezeBertConfig(
               embedding_size=self.hidden_size,      vocab_size=self.vocab_size,      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,      attention_probs_dropout_prob=self.hidden_dropout_prob,      attention_dropout=self.attention_probs_dropout_prob,      max_position_embeddings=self.max_position_embeddings,      initializer_range=self.initializer_range,      q_groups=self.q_groups,      k_groups=self.k_groups,      v_groups=self.v_groups,      post_attention_groups=self.post_attention_groups,      intermediate_groups=self.intermediate_groups,      output_groups=self.output_groups,      )
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		SqueezeBertModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Dict      		=		model(lowerCamelCase,      lowerCamelCase)
           _lowercase       :							Any      		=		model(lowerCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape,      (self.batch_size, self.seq_length, self.hidden_size))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   int:




           """simple docstring"""

           _lowercase       :							Dict      		=		SqueezeBertForMaskedLM(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Optional[Any]      		=		model(lowerCamelCase,      attention_mask=lowerCamelCase,      labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.seq_length, self.vocab_size))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		SqueezeBertForQuestionAnswering(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							List[Any]      		=		model(
               lowerCamelCase,      attention_mask=lowerCamelCase,      start_positions=lowerCamelCase,      end_positions=lowerCamelCase)
           self.parent.assertEqual(result.start_logits.shape,      (self.batch_size, self.seq_length))
           self.parent.assertEqual(result.end_logits.shape,      (self.batch_size, self.seq_length))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   str:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		self.num_labels
           _lowercase       :							int      		=		SqueezeBertForSequenceClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Any      		=		model(lowerCamelCase,      attention_mask=lowerCamelCase,      labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.num_labels))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		self.num_labels
           _lowercase       :							List[str]      		=		SqueezeBertForTokenClassification(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()

           _lowercase       :							Union[str, Any]      		=		model(lowerCamelCase,      attention_mask=lowerCamelCase,      labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.seq_length, self.num_labels))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Dict:




           """simple docstring"""

           _lowercase       :							str      		=		self.num_choices
           _lowercase       :							str      		=		SqueezeBertForMultipleChoice(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Dict      		=		input_ids.unsqueeze(1).expand(-1,      self.num_choices,      -1).contiguous()
           _lowercase       :							int      		=		input_mask.unsqueeze(1).expand(-1,      self.num_choices,      -1).contiguous()
           _lowercase       :							Optional[Any]      		=		model(
               lowerCamelCase,      attention_mask=lowerCamelCase,      labels=lowerCamelCase,      )
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.num_choices))




       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		self.prepare_config_and_inputs()
           ((_lowercase)		,				(_lowercase)		,				(_lowercase)		,				(_lowercase)		,				(_lowercase)		,				(_lowercase))       :							Dict      		=		config_and_inputs
           _lowercase       :							Tuple      		=		{'input_ids': input_ids, 'attention_mask': input_mask}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(						_a, _a, unittest.TestCase   ):
       lowercase_ :	Union[str, Any]     =			(
           (
               SqueezeBertModel,
               SqueezeBertForMaskedLM,
               SqueezeBertForMultipleChoice,
               SqueezeBertForQuestionAnswering,
               SqueezeBertForSequenceClassification,
               SqueezeBertForTokenClassification,
           )
           if is_torch_available()
           else None
       )
       lowercase_ :	Optional[int]     =			(
           {
               """feature-extraction""": SqueezeBertModel,
               """fill-mask""": SqueezeBertForMaskedLM,
               """question-answering""": SqueezeBertForQuestionAnswering,
               """text-classification""": SqueezeBertForSequenceClassification,
               """token-classification""": SqueezeBertForTokenClassification,
               """zero-shot""": SqueezeBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       lowercase_ :	Tuple     =			False
       lowercase_ :	List[str]     =			True
       lowercase_ :	int     =			False
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							str      		=		SqueezeBertModelTester(self)
           _lowercase       :							Dict      		=		ConfigTester(self,      config_class=lowerCamelCase,      dim=37)
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_model(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Any      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_question_answering(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_token_classification(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowerCamelCase)




       @slow
       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase       :							List[Any]      		=		SqueezeBertModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)






@require_sentencepiece
@require_tokenizers
@require_torch
class _lowerCamelCase(						unittest.TestCase   ):
       @slow
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli')

           _lowercase       :							Optional[int]      		=		torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]])
           _lowercase       :							List[str]      		=		model(lowerCamelCase)[0]
           _lowercase       :							Union[str, Any]      		=		torch.Size((1, 3))
           self.assertEqual(output.shape,      lowerCamelCase)
           _lowercase       :							Tuple      		=		torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]])
           self.assertTrue(torch.allclose(lowerCamelCase,      lowerCamelCase,      atol=1E-4))


