import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


A   :     Dict       =				random.Random()

if is_torch_available():
      import torch


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase=1.0   ,					__UpperCamelCase=None   ,					__UpperCamelCase=None ):
      if rng is None:
            SCREAMING_SNAKE_CASE_      =     global_rng

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

      return values


class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def __init__(							self       :				Any     ,   __magic_name__       :				int     ,   __magic_name__       :				Optional[int]=7     ,   __magic_name__       :				Optional[Any]=400     ,   __magic_name__       :				Optional[int]=2_000     ,   __magic_name__       :				List[str]=1     ,   __magic_name__       :				Optional[Any]=0.0     ,   __magic_name__       :				Tuple=16_000     ,   __magic_name__       :				List[str]=True     ,   __magic_name__       :				Optional[int]=True     ,   )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     parent
        SCREAMING_SNAKE_CASE_      =     batch_size
        SCREAMING_SNAKE_CASE_      =     min_seq_length
        SCREAMING_SNAKE_CASE_      =     max_seq_length
        SCREAMING_SNAKE_CASE_      =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        SCREAMING_SNAKE_CASE_      =     feature_size
        SCREAMING_SNAKE_CASE_      =     padding_value
        SCREAMING_SNAKE_CASE_      =     sampling_rate
        SCREAMING_SNAKE_CASE_      =     return_attention_mask
        SCREAMING_SNAKE_CASE_      =     do_normalize







  def       __A   (							self       :				Dict      )     ->		Union[str, Any]:
        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }







  def       __A   (							self       :				Any     ,   __magic_name__       :				Any=False     ,   __magic_name__       :				List[Any]=False      )     ->		Tuple:
        def _flatten(__magic_name__       :				str      ):
              return list(itertools.chain(*__magic_name__      )      )

        if equal_length:
              SCREAMING_SNAKE_CASE_      =     floats_list((self.batch_size, self.max_seq_length)      )
        else:
              # make sure that inputs increase in size
              SCREAMING_SNAKE_CASE_      =     [
                  _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:
              SCREAMING_SNAKE_CASE_      =     [np.asarray(__magic_name__      ) for x in speech_inputs]

        return speech_inputs



@require_torch
@require_torchaudio
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  ASTFeatureExtractor







  def       __A   (							self       :				List[Any]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     ASTFeatureExtractionTester(self      )







  def       __A   (							self       :				str      )     ->		str:
        # Tests that all call wrap to encode_plus and batch_encode_plus
        SCREAMING_SNAKE_CASE_      =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        # create three inputs of length 800, 1000, and 1200
        SCREAMING_SNAKE_CASE_      =     [floats_list((1, x)      )[0] for x in range(800     ,   1_400     ,   200      )]
        SCREAMING_SNAKE_CASE_      =     [np.asarray(__magic_name__      ) for speech_input in speech_inputs]

        # Test not batched input
        SCREAMING_SNAKE_CASE_      =     feat_extract(speech_inputs[0]     ,   return_tensors="np"      ).input_values
        SCREAMING_SNAKE_CASE_      =     feat_extract(np_speech_inputs[0]     ,   return_tensors="np"      ).input_values
        self.assertTrue(np.allclose(__magic_name__     ,   __magic_name__     ,   atol=1e-3      )      )

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

        # Test 2-D numpy arrays are batched.
        SCREAMING_SNAKE_CASE_      =     [floats_list((1, x)      )[0] for x in (800, 800, 800)]
        SCREAMING_SNAKE_CASE_      =     np.asarray(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     feat_extract(__magic_name__     ,   return_tensors="np"      ).input_values
        SCREAMING_SNAKE_CASE_      =     feat_extract(__magic_name__     ,   return_tensors="np"      ).input_values
        for enc_seq_a, enc_seq_a in zip(__magic_name__     ,   __magic_name__      ):
              self.assertTrue(np.allclose(__magic_name__     ,   __magic_name__     ,   atol=1e-3      )      )







  @require_torch
  def       __A   (							self       :				Dict      )     ->		Union[str, Any]:
        import torch

        SCREAMING_SNAKE_CASE_      =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        SCREAMING_SNAKE_CASE_      =     np.random.rand(100      ).astype(np.floataa      )
        SCREAMING_SNAKE_CASE_      =     np_speech_inputs.tolist()

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







  def       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				Optional[int]      )     ->		Optional[int]:
        from datasets import load_dataset

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

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







  @require_torch
  def       __A   (							self       :				Optional[Any]      )     ->		Union[str, Any]:
        # fmt: off
        SCREAMING_SNAKE_CASE_      =     torch.tensor(
            [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
             -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
             -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
             -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]      )
        # fmt: on

        SCREAMING_SNAKE_CASE_      =     self._load_datasamples(1      )
        SCREAMING_SNAKE_CASE_      =     ASTFeatureExtractor()
        SCREAMING_SNAKE_CASE_      =     feature_extractor(__magic_name__     ,   return_tensors="pt"      ).input_values
        self.assertEquals(input_values.shape     ,   (1, 1_024, 128)      )
        self.assertTrue(torch.allclose(input_values[0, 0, :30]     ,   __magic_name__     ,   atol=1e-4      )      )



import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


A   :     Tuple       =				datasets.utils.logging.get_logger(__name__)


@dataclass
class      lowerCamelCase    (datasets.BuilderConfig    ):
  """simple docstring"""







  lowerCamelCase__   =  None
  lowerCamelCase__   =  "utf-8"
  lowerCamelCase__   =  None
  lowerCamelCase__   =  None
  lowerCamelCase__   =  True  # deprecated
  lowerCamelCase__   =  None  # deprecated
  lowerCamelCase__   =  1_0 << 2_0  # 10MB
  lowerCamelCase__   =  None



class      lowerCamelCase    (datasets.ArrowBasedBuilder    ):
  """simple docstring"""







  lowerCamelCase__   =  JsonConfig







  def       __A   (							self       :				Optional[int]      )     ->		Optional[int]:
        if self.config.block_size is not None:
              logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"      )
              SCREAMING_SNAKE_CASE_      =     self.config.block_size
        if self.config.use_threads is not True:
              logger.warning(
                  "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."      )
        if self.config.newlines_in_values is not None:
              raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported"      )
        return datasets.DatasetInfo(features=self.config.features      )







  def       __A   (							self       :				List[str]     ,   __magic_name__       :				str      )     ->		Tuple:
        if not self.config.data_files:
              raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''      )
        SCREAMING_SNAKE_CASE_      =     dl_manager.download_and_extract(self.config.data_files      )
        if isinstance(__magic_name__     ,   (str, list, tuple)      ):
              SCREAMING_SNAKE_CASE_      =     data_files
              if isinstance(__magic_name__     ,   __magic_name__      ):
                    SCREAMING_SNAKE_CASE_      =     [files]
              SCREAMING_SNAKE_CASE_      =     [dl_manager.iter_files(__magic_name__      ) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,   gen_kwargs={"files": files}      )]
        SCREAMING_SNAKE_CASE_      =     []
        for split_name, files in data_files.items():
              if isinstance(__magic_name__     ,   __magic_name__      ):
                    SCREAMING_SNAKE_CASE_      =     [files]
              SCREAMING_SNAKE_CASE_      =     [dl_manager.iter_files(__magic_name__      ) for file in files]
              splits.append(datasets.SplitGenerator(name=__magic_name__     ,   gen_kwargs={"files": files}      )      )
        return splits







  def       __A   (							self       :				str     ,   __magic_name__       :				pa.Table      )     ->		pa.Table:
        if self.config.features is not None:
              # adding missing columns
              for column_name in set(self.config.features      ) - set(pa_table.column_names      ):
                    SCREAMING_SNAKE_CASE_      =     self.config.features.arrow_schema.field(__magic_name__      ).type
                    SCREAMING_SNAKE_CASE_      =     pa_table.append_column(__magic_name__     ,   pa.array([None] * len(__magic_name__      )     ,   type=__magic_name__      )      )
              # more expensive cast to support nested structures with keys in a different order
              # allows str <-> int/float or str to Audio for example
              SCREAMING_SNAKE_CASE_      =     table_cast(__magic_name__     ,   self.config.features.arrow_schema      )
        return pa_table







  def       __A   (							self       :				List[str]     ,   __magic_name__       :				List[str]      )     ->		int:
        for file_idx, file in enumerate(itertools.chain.from_iterable(__magic_name__      )      ):
              # If the file is one json object and if we need to look at the list of items in one specific field
              if self.config.field is not None:
                    with open(__magic_name__     ,   encoding=self.config.encoding     ,   errors=self.config.encoding_errors      ) as f:
                          SCREAMING_SNAKE_CASE_      =     json.load(__magic_name__      )

                    # We keep only the field we are interested in
                    SCREAMING_SNAKE_CASE_      =     dataset[self.config.field]

                    # We accept two format: a list of dicts or a dict of lists
                    if isinstance(__magic_name__     ,   (list, tuple)      ):
                          SCREAMING_SNAKE_CASE_      =     set().union(*[row.keys() for row in dataset]      )
                          SCREAMING_SNAKE_CASE_      =     {col: [row.get(__magic_name__      ) for row in dataset] for col in keys}
                    else:
                          SCREAMING_SNAKE_CASE_      =     dataset
                    SCREAMING_SNAKE_CASE_      =     pa.Table.from_pydict(__magic_name__      )
                    yield file_idx, self._cast_table(__magic_name__      )

              # If the file has one json object per line
              else:
                    with open(__magic_name__     ,   "rb"      ) as f:
                          SCREAMING_SNAKE_CASE_      =     0
                          # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                          # Set a default minimum value of 16kB if the chunk size is really small
                          SCREAMING_SNAKE_CASE_      =     max(self.config.chunksize // 32     ,   16 << 10      )
                          SCREAMING_SNAKE_CASE_      =     (
                              self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
                          )
                          while True:
                                SCREAMING_SNAKE_CASE_      =     f.read(self.config.chunksize      )
                                if not batch:
                                      break
                                # Finish current line
                                try:
                                      batch += f.readline()
                                except (AttributeError, io.UnsupportedOperation):
                                      batch += readline(__magic_name__      )
                                # PyArrow only accepts utf-8 encoded bytes
                                if self.config.encoding != "utf-8":
                                      SCREAMING_SNAKE_CASE_      =     batch.decode(self.config.encoding     ,   errors=__magic_name__      ).encode("utf-8"      )
                                try:
                                      while True:
                                            try:
                                                  SCREAMING_SNAKE_CASE_      =     paj.read_json(
                                                      io.BytesIO(__magic_name__      )     ,   read_options=paj.ReadOptions(block_size=__magic_name__      )      )
                                                  break
                                            except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                  if (
                                                      isinstance(__magic_name__     ,   pa.ArrowInvalid      )
                                                      and "straddling" not in str(__magic_name__      )
                                                      or block_size > len(__magic_name__      )
                                                  ):
                                                        raise
                                                  else:
                                                        # Increase the block size in case it was too small.
                                                        # The block size will be reset for the next file.
                                                        logger.debug(
                                                            F'''Batch of {len(__magic_name__      )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''      )
                                                        block_size *= 2
                                except pa.ArrowInvalid as e:
                                      try:
                                            with open(
                                                __magic_name__     ,   encoding=self.config.encoding     ,   errors=self.config.encoding_errors      ) as f:
                                                  SCREAMING_SNAKE_CASE_      =     json.load(__magic_name__      )
                                      except json.JSONDecodeError:
                                            logger.error(F'''Failed to read file \'{file}\' with error {type(__magic_name__      )}: {e}'''      )
                                            raise e
                                      # If possible, parse the file as a list of json objects and exit the loop
                                      if isinstance(__magic_name__     ,   __magic_name__      ):  # list is the only sequence type supported in JSON
                                            try:
                                                  SCREAMING_SNAKE_CASE_      =     set().union(*[row.keys() for row in dataset]      )
                                                  SCREAMING_SNAKE_CASE_      =     {col: [row.get(__magic_name__      ) for row in dataset] for col in keys}
                                                  SCREAMING_SNAKE_CASE_      =     pa.Table.from_pydict(__magic_name__      )
                                            except (pa.ArrowInvalid, AttributeError) as e:
                                                  logger.error(F'''Failed to read file \'{file}\' with error {type(__magic_name__      )}: {e}'''      )
                                                  raise ValueError(F'''Not able to read records in the JSON file at {file}.'''      ) from None
                                            yield file_idx, self._cast_table(__magic_name__      )
                                            break
                                      else:
                                            logger.error(F'''Failed to read file \'{file}\' with error {type(__magic_name__      )}: {e}'''      )
                                            raise ValueError(
                                                F'''Not able to read records in the JSON file at {file}. '''
                                                F'''You should probably indicate the field of the JSON file containing your records. '''
                                                F'''This JSON file contain the following fields: {str(list(dataset.keys()      )      )}. '''
                                                F'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''      ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                yield (file_idx, batch_idx), self._cast_table(__magic_name__      )
                                batch_idx += 1


