

"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch


__A     :    str    =		random.Random()
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__=1.0			,							lowercase__=None			,							lowercase__=None      ):
					"""simple docstring"""
					if rng is None:
										A			=					global_rng

					A			=					[]
					for batch_idx in range(shape[0]      ):
										values.append([]      )
										for _ in range(shape[1]      ):
															values[-1].append(rng.random() * scale      )

					return values





@require_torch
class 			__UpperCamelCase (   unittest.TestCase						):


		def __init__(self :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		str=7     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=4_0_0     ,     __SCREAMING_SNAKE_CASE :		Tuple=2_0_0_0     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=1     ,     __SCREAMING_SNAKE_CASE :		Any=0.0     ,     __SCREAMING_SNAKE_CASE :		Tuple=2_4_0_0_0     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=True     ,     ):
							A			=					parent
							A			=					batch_size
							A			=					min_seq_length
							A			=					max_seq_length
							A			=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							A			=					feature_size
							A			=					padding_value
							A			=					sampling_rate
							A			=					return_attention_mask


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							}


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple     ,     __SCREAMING_SNAKE_CASE :		List[Any]=False     ,     __SCREAMING_SNAKE_CASE :		Any=False):
							def _flatten(__SCREAMING_SNAKE_CASE :		Any):
												return list(itertools.chain(*__SCREAMING_SNAKE_CASE))

							if equal_length:
												A			=					floats_list((self.batch_size, self.max_seq_length))
							else:
												# make sure that inputs increase in size
												A			=					[
												    _flatten(floats_list((x, self.feature_size)))
												    for x in range(self.min_seq_length     ,     self.max_seq_length     ,     self.seq_length_diff)
												]

							if numpify:
												A			=					[np.asarray(__SCREAMING_SNAKE_CASE) for x in audio_inputs]

							return audio_inputs





@require_torch
class 			__UpperCamelCase (   _A     ,		unittest.TestCase						):
		SCREAMING_SNAKE_CASE								=    EncodecFeatureExtractor


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					EnCodecFeatureExtractionTester(self)


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							# Tests that all call wrap to encode_plus and batch_encode_plus
							A			=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
							# create three inputs of length 800, 1000, and 1200
							A			=					[floats_list((1, x))[0] for x in range(8_0_0     ,     1_4_0_0     ,     2_0_0)]
							A			=					[np.asarray(__SCREAMING_SNAKE_CASE) for audio_input in audio_inputs]

							# Test not batched input
							A			=					feat_extract(audio_inputs[0]     ,     return_tensors="np").input_values
							A			=					feat_extract(np_audio_inputs[0]     ,     return_tensors="np").input_values
							self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-3))

							# Test batched
							A			=					feat_extract(__SCREAMING_SNAKE_CASE     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="np").input_values
							A			=					feat_extract(__SCREAMING_SNAKE_CASE     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="np").input_values
							for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
												self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-3))


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
							A			=					np.random.rand(1_0_0).astype(np.floataa)
							A			=					np_audio_inputs.tolist()

							for inputs in [py_audio_inputs, np_audio_inputs]:
												A			=					feature_extractor.pad([{"input_values": inputs}]     ,     return_tensors="np")
												self.assertTrue(np_processed.input_values.dtype == np.floataa)
												A			=					feature_extractor.pad([{"input_values": inputs}]     ,     return_tensors="pt")
												self.assertTrue(pt_processed.input_values.dtype == torch.floataa)


		def 					SCREAMING_SNAKE_CASE__   (self :		Any     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]):
							from datasets import load_dataset

							A			=					load_dataset("hf-internal-testing/librispeech_asr_dummy"     ,     "clean"     ,     split="validation")
							# automatic decoding with librispeech
							A			=					ds.sort("id").select(range(__SCREAMING_SNAKE_CASE))[:num_samples]["audio"]

							return [x["array"] for x in audio_samples]


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							# fmt: off
							A			=					torch.tensor(
							    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
							     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
							     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
							     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
							     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
							     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03])
							# fmt: on
							A			=					self._load_datasamples(1)
							A			=					EncodecFeatureExtractor()
							A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(input_values.shape     ,     (1, 1, 9_3_6_8_0))
							self.assertTrue(torch.allclose(input_values[0, 0, :3_0]     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-6))


		def 					SCREAMING_SNAKE_CASE__   (self :		str):
							# fmt: off
							A			=					torch.tensor(
							    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
							     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
							     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
							     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
							     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
							     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03])
							# fmt: on
							A			=					self._load_datasamples(1)
							A			=					[np.tile(input_audio[0][None]     ,     reps=(2, 1))]
							input_audio[0][1] *= 0.5
							A			=					EncodecFeatureExtractor(feature_size=2)
							A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(input_values.shape     ,     (1, 2, 9_3_6_8_0))
							self.assertTrue(torch.allclose(input_values[0, 0, :3_0]     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-6))
							self.assertTrue(torch.allclose(input_values[0, 1, :3_0]     ,     EXPECTED_INPUT_VALUES * 0.5     ,     atol=1E-6))


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							A			=					self._load_datasamples(2)
							# would be easier if the stride was like
							A			=					EncodecFeatureExtractor(feature_size=1     ,     chunk_length_s=1     ,     overlap=0.0_1)

							# pad and trunc raise an error ?
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE     ,     "^Both padding and truncation were set. Make sure you only set one.$"     ,     ):
												A			=					feature_extractor(
												    __SCREAMING_SNAKE_CASE     ,     padding="max_length"     ,     truncation=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values

							# truncate to chunk
							A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     truncation=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape     ,     (2, 1, 7_1_5_2_0))  # 2 chunks

							# force truncate to max_length
							A			=					feature_extractor(
							    __SCREAMING_SNAKE_CASE     ,     truncation=__SCREAMING_SNAKE_CASE     ,     max_length=4_8_0_0_0     ,     return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape     ,     (2, 1, 4_8_0_0_0))

							# pad to chunk
							A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(padded_outputs.shape     ,     (2, 1, 9_5_2_8_0))

							# pad to chunk
							A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape     ,     (2, 1, 9_5_2_8_0))

							# force pad to max length
							A			=					feature_extractor(
							    __SCREAMING_SNAKE_CASE     ,     padding="max_length"     ,     max_length=1_0_0_0_0_0     ,     return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape     ,     (2, 1, 1_0_0_0_0_0))

							# force no pad
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE     ,     "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"     ,     ):
												A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values

							A			=					feature_extractor(input_audio[0]     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape     ,     (1, 1, 9_3_6_8_0))

							# no pad if no chunk_length_s
							A			=					None
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE     ,     "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"     ,     ):
												A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values

							A			=					feature_extractor(input_audio[0]     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape     ,     (1, 1, 9_3_6_8_0))

							# no pad if no overlap
							A			=					2
							A			=					None
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE     ,     "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"     ,     ):
												A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values

							A			=					feature_extractor(input_audio[0]     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape     ,     (1, 1, 9_3_6_8_0))













"""simple docstring"""


import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
   import sqlitea

   import sqlalchemy


_UpperCamelCase				:    Optional[Any]     =						datasets.utils.logging.get_logger(__name__)






@dataclass
class 						snake_case  (  datasets.BuilderConfig       ):
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     True
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     1_0000
    __magic_name__         =     None





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      if self.sql is None:
        raise ValueError('sql must be specified'			)
      if self.con is None:
        raise ValueError('con must be specified'			)





    def 				lowerCamelCase__      (							self	:					int				,		A	:					dict				,		A	:					Optional[datasets.Features] = None				,		):


      '''simple docstring'''




      a     :			str				=   config_kwargs.copy()
      # We need to stringify the Selectable object to make its hash deterministic

      # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
      a     :			List[str]				=   config_kwargs['sql']
      if not isinstance(A				,		A			):
        if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
          import sqlalchemy

          if isinstance(A				,		sqlalchemy.sql.Selectable			):
            a     :			Union[str, Any]				=   sqlalchemy.create_engine(config_kwargs['con'].split('://'			)[0] + '://'			)
            a     :			List[Any]				=   str(sql.compile(dialect=engine.dialect			)			)
            a     :			str				=   sql_str
          else:
            raise TypeError(
                F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(A			)}: {sql}'''			)
        else:
          raise TypeError(
              F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(A			)}: {sql}'''			)
      a     :			List[str]				=   config_kwargs['con']
      if not isinstance(A				,		A			):
        a     :			Optional[Any]				=   id(A			)
        logger.info(
            F'''SQL connection \'con\' of type {type(A			)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''			)

      return super().create_config_id(A				,		custom_features=A			)





    @property
    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			int				=   {
          'index_col': self.index_col,
          'columns': self.columns,
          'params': self.params,
          'coerce_float': self.coerce_float,
          'parse_dates': self.parse_dates,
      }
      return pd_read_sql_kwargs






class 						snake_case  (  datasets.ArrowBasedBuilder       ):
    __magic_name__         =     SqlConfig





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      return datasets.DatasetInfo(features=self.config.features			)





    def 				lowerCamelCase__      (							self	:					Dict				,		A	:					Optional[int]			):


      '''simple docstring'''




      return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,		gen_kwargs={}			)]





    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		A	:					pa.Table			):


      '''simple docstring'''




      if self.config.features is not None:
        a     :			List[Any]				=   self.config.features.arrow_schema
        if all(not require_storage_cast(A			) for feature in self.config.features.values()			):
          # cheaper cast
          a     :			Dict				=   pa.Table.from_arrays([pa_table[field.name] for field in schema]				,		schema=A			)
        else:
          # more expensive cast; allows str <-> int/float or str to Audio for example
          a     :			Union[str, Any]				=   table_cast(A				,		A			)
      return pa_table





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a     :			List[Any]				=   self.config.chunksize
      a     :			Union[str, Any]				=   pd.read_sql(
          self.config.sql				,		self.config.con				,		chunksize=A				,		**self.config.pd_read_sql_kwargs			)
      a     :			Optional[Any]				=   [sql_reader] if chunksize is None else sql_reader
      for chunk_idx, df in enumerate(A			):
        a     :			List[Any]				=   pa.Table.from_pandas(A			)
        yield chunk_idx, self._cast_table(A			)




