
import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


_lowerCAmelCase						:   Union[str, Any]		=   TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow("", "|", "|"),
    datarow=DataRow("", "|", "|"),
    padding=1,
    with_header_hide=None,
)


_lowerCAmelCase						:   Optional[int]		=   []
_lowerCAmelCase						:   Optional[int]		=   []

_lowerCAmelCase						:   str		=   {"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}

_lowerCAmelCase						:   List[Any]		=   [
    {
        "type": "header",
        "text": {
            "type": "plain_text",
            "text": F'''🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results''',
            "emoji": True,
        },
    }
]

_lowerCAmelCase						:   str		=   0
for log in Path().glob("*.log"):
  _lowerCAmelCase						:   Any		=   0
  with open(log, "r") as f:
    for line in f:
      _lowerCAmelCase						:   List[Any]		=   json.loads(line)
      if line.get("nodeid", "") != "":
        _lowerCAmelCase						:   int		=   line["nodeid"]
        if line.get("duration", None) is not None:
          _lowerCAmelCase						:   Dict		=   F'''{line["duration"]:.4f}'''
          if line.get("outcome", "") == "failed":
            section_num_failed += 1
            failed.append([test, duration, log.name.split("_")[0]])
            total_num_failed += 1
  group_info.append([str(log), section_num_failed, failed])
  _lowerCAmelCase						:   int		=   []
  log.unlink()

_lowerCAmelCase						:   Optional[int]		=   ""
_lowerCAmelCase						:   Tuple		=   []
if total_num_failed > 0:
  for name, num_failed, failed_tests in group_info:
    if num_failed > 0:
      if num_failed == 1:
        message += F"*{name[1:]}: {num_failed} failed test*\n"
      else:
        message += F"*{name[1:]}: {num_failed} failed tests*\n"
      _lowerCAmelCase						:   List[str]		=   []
      _lowerCAmelCase						:   Optional[Any]		=   {}
      for test in failed_tests:
        _lowerCAmelCase						:   Any		=   test[0].split("::")
        _lowerCAmelCase						:   Union[str, Any]		=   data[0].split("/")[-1]
        if data[0] not in filesafailed:
          _lowerCAmelCase						:   List[Any]		=   [data[1:]]
        else:
          filesafailed[data[0]] += [data[1:]]
        failed_table.append(data)

      _lowerCAmelCase						:   Optional[int]		=   [test[0] for test in failed_table]
      _lowerCAmelCase						:   Union[str, Any]		=   list(set(files))
      # Count number of instances in failed_tests
      _lowerCAmelCase						:   List[str]		=   []
      for file in individual_files:
        table.append([file, len(filesafailed[file])])

      _lowerCAmelCase						:   Optional[Any]		=   tabulate(
          table,
          headers=["Test Location", "Num Failed"],
          tablefmt=hf_table_format,
          stralign="right",
      )
      message += F"\n```\n{failed_table}\n```"
      all_filesafailed.append(filesafailed)
  if len(message) > 3_0_0_0:
    _lowerCAmelCase						:   List[Any]		=   "Too many failed tests, please see the full report in the Action results."
    _lowerCAmelCase						:   Dict		=   len(err) + 1_0
    _lowerCAmelCase						:   Union[str, Any]		=   message[: 3_0_0_0 - offset] + F'''\n...\n```\n{err}'''
  print(F'''### {message}''')
else:
  _lowerCAmelCase						:   Dict		=   "No failed tests! 🤗"
  print(F'''## {message}''')
  payload.append(no_error_payload)

if os.environ.get("TEST_TYPE", "") != "":
  from slack_sdk import WebClient

  _lowerCAmelCase						:   Dict		=   WebClient(token=os.environ["SLACK_API_TOKEN"])
  if message != "No failed tests! 🤗":
    _lowerCAmelCase						:   Tuple		=   {
        "type": "section",
        "text": {
            "type": "mrkdwn",
            "text": message,
        },
    }
    payload.append(md_report)
    _lowerCAmelCase						:   Optional[int]		=   {
        "type": "section",
        "text": {
            "type": "mrkdwn",
            "text": "*For more details:*",
        },
        "accessory": {
            "type": "button",
            "text": {
                "type": "plain_text",
                "text": "Check Action results",
                "emoji": True,
            },
            "url": F'''https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}''',
        },
    }
    payload.append(action_button)
    _lowerCAmelCase						:   str		=   {
        "type": "context",
        "elements": [
            {
                "type": "plain_text",
                "text": F'''Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}''',
            }
        ],
    }
    payload.append(date_report)
  _lowerCAmelCase						:   Optional[int]		=   client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
  _lowerCAmelCase						:   List[Any]		=   response.data["ts"]
  for failed_file in all_filesafailed:
    for test_location, test_failures in failed_file.items():
      # Keep only the first instance of the test name
      _lowerCAmelCase						:   str		=   ""
      for i, row in enumerate(test_failures):
        if row[0] != test_class:
          _lowerCAmelCase						:   Any		=   row[0]
        else:
          _lowerCAmelCase						:   List[str]		=   ""

      _lowerCAmelCase						:   List[str]		=   {
          "type": "section",
          "text": {
              "type": "mrkdwn",
              "text": F'''Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```''',
          },
      }

      client.chat_postMessage(
          channel="#accelerate-ci-daily",
          thread_ts=ts,
          blocks=[payload],
      )











import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class 			_UpperCamelCase      (				lowerCAmelCase		):
      # to overwrite at feature extractactor specific tests
      UpperCAmelCase_        =		None
      UpperCAmelCase_        =		None
      @property
      def 	UpperCAmelCase_						(			self   :int      )     ->			int:
       return self.feat_extract_tester.prepare_feat_extract_dict()
      def 	UpperCAmelCase_						(			self   :Any      )     ->			str:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       self.assertTrue(hasattr(lowerCamelCase ,      "feature_size"      )      )
       self.assertTrue(hasattr(lowerCamelCase ,      "sampling_rate"      )      )
       self.assertTrue(hasattr(lowerCamelCase ,      "padding_value"      )      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			int:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       self.assertTrue(all(len(lowerCamelCase      ) == len(lowerCamelCase      ) for x, y in zip(lowerCamelCase ,      processed_features[input_name]      )      )      )

       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="np"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      @require_torch
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="pt"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      @require_tf
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Dict:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="tf"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      def 	UpperCAmelCase_						(			self   :int ,      lowerCamelCase   :int=False      )     ->			str:
       def _inputs_have_equal_length(lowerCamelCase   :Union[str, Any]      ):
        UpperCAmelCase__							 =						len(input[0]      )
        for input_slice in input[1:]:
         if len(lowerCamelCase      ) != length:
          return False
        return True

       def _inputs_are_equal(lowerCamelCase   :Dict ,      lowerCamelCase   :Optional[Any]      ):
        if len(lowerCamelCase      ) != len(lowerCamelCase      ):
         return False

        for input_slice_a, input_slice_a in zip(lowerCamelCase ,      lowerCamelCase      ):
         if not np.allclose(np.asarray(lowerCamelCase      ) ,      np.asarray(lowerCamelCase      ) ,      atol=1e-3      ):
          return False
        return True

       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						self.feat_extract_tester.seq_length_diff
       UpperCAmelCase__							 =						self.feat_extract_tester.max_seq_length + pad_diff
       UpperCAmelCase__							 =						self.feat_extract_tester.min_seq_length
       UpperCAmelCase__							 =						self.feat_extract_tester.batch_size
       UpperCAmelCase__							 =						self.feat_extract_tester.feature_size

       # test padding for List[int] + numpy
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest"      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[-1]      )      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       # max_length parameter has to be provided when setting `padding="max_length"`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="max_length"      )[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=lowerCamelCase ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )
       self.assertTrue(len(input_a[0]      ) == pad_min_length      )
       self.assertTrue(len(input_a[1]      ) == pad_min_length + pad_diff      )
       self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]      ))      )
       self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)      )

       if feature_size > 1:
        self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size      )

       # test padding for `pad_to_multiple_of` for List[int] + numpy
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      pad_to_multiple_of=10      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      pad_to_multiple_of=10      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      pad_to_multiple_of=10 ,      max_length=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      pad_to_multiple_of=10 ,      max_length=lowerCamelCase ,      return_tensors="np" ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(all(len(lowerCamelCase      ) % 10 == 0 for x in input_a      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )

       UpperCAmelCase__							 =						pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
       self.assertTrue(all(len(lowerCamelCase      ) == expected_mult_pad_length for x in input_a      )      )
       self.assertEqual(input_a.shape[:2] ,      (batch_size, expected_mult_pad_length)      )

       if feature_size > 1:
        self.assertTrue(input_a.shape[2] == feature_size      )

       # Check padding value is correct
       UpperCAmelCase__							 =						(np.ones(self.feat_extract_tester.feature_size      ) * feat_extract.padding_value).sum()
       self.assertTrue(
           abs(np.asarray(input_a[0]      )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)      )
           < 1e-3      )
       self.assertTrue(
           abs(
               np.asarray(input_a[1]      )[pad_min_length + pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)      )
           < 1e-3      )
       self.assertTrue(
           abs(
               np.asarray(input_a[2]      )[pad_min_length + 2 * pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)      )
           < 1e-3      )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)      ) < 1e-3      )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)      )
           < 1e-3      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :int=False      )     ->			str:
       def _inputs_have_equal_length(lowerCamelCase   :Any      ):
        UpperCAmelCase__							 =						len(input[0]      )
        for input_slice in input[1:]:
         if len(lowerCamelCase      ) != length:
          return False
        return True

       def _inputs_are_equal(lowerCamelCase   :Optional[int] ,      lowerCamelCase   :str      ):
        if len(lowerCamelCase      ) != len(lowerCamelCase      ):
         return False

        for input_slice_a, input_slice_a in zip(lowerCamelCase ,      lowerCamelCase      ):
         if not np.allclose(np.asarray(lowerCamelCase      ) ,      np.asarray(lowerCamelCase      ) ,      atol=1e-3      ):
          return False
        return True

       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       # truncate to smallest
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      truncation=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      )      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )

       # truncate to smallest with np
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      return_tensors="np" ,      truncation=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(input_a.shape[1] == len(speech_inputs[0]      )      )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )

       # truncate to middle
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      truncation=lowerCamelCase ,      return_tensors="np" ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      truncation=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(input_a.shape[1] == len(speech_inputs[1]      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(len(input_a[-1]      ) == len(speech_inputs[-1]      )      )

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      truncation=lowerCamelCase      )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="longest" ,      truncation=lowerCamelCase      )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="longest" ,      truncation=lowerCamelCase      )[input_name]

       # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      truncation=lowerCamelCase      )[input_name]

       # test truncation for `pad_to_multiple_of` for List[int] + numpy
       UpperCAmelCase__							 =						12
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      pad_to_multiple_of=lowerCamelCase ,      truncation=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      pad_to_multiple_of=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       # retrieve expected_length as multiple of pad_to_multiple_of
       UpperCAmelCase__							 =						len(speech_inputs[0]      )
       if expected_length % pad_to_multiple_of != 0:
        UpperCAmelCase__							 =						((len(speech_inputs[0]      ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

       self.assertTrue(len(input_a[0]      ) == expected_length      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
      def 	UpperCAmelCase_						(			self   :int      )     ->			List[str]:
       self._check_padding(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			int:
       self._check_padding(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			str:
       self._check_truncation(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			str:
       self._check_truncation(numpify=lowerCamelCase      )
      @require_torch
      def 	UpperCAmelCase_						(			self   :int      )     ->			Any:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )[input_name]
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="pt"      )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa      ).sum() - input_pt.numpy().astype(np.floataa      ).sum()      ) < 1e-2      )
      @require_tf
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )[input_name]
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="tf"      )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa      ).sum() - input_tf.numpy().astype(np.floataa      ).sum()      ) < 1e-2      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			str:
       UpperCAmelCase__							 =						self.feat_extract_dict
       UpperCAmelCase__							 =						True
       UpperCAmelCase__							 =						self.feature_extraction_class(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						[len(lowerCamelCase      ) for x in speech_inputs]
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )
       self.assertIn("attention_mask" ,      lowerCamelCase      )
       self.assertListEqual(list(processed.attention_mask.shape      ) ,      list(processed[input_name].shape[:2]      )      )
       self.assertListEqual(processed.attention_mask.sum(-1      ).tolist() ,      lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :int      )     ->			int:
       UpperCAmelCase__							 =						self.feat_extract_dict
       UpperCAmelCase__							 =						True
       UpperCAmelCase__							 =						self.feature_extraction_class(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						[len(lowerCamelCase      ) for x in speech_inputs]
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )
       UpperCAmelCase__							 =						min(lowerCamelCase      )

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=lowerCamelCase ,      truncation=lowerCamelCase ,      return_tensors="np"      )
       self.assertIn("attention_mask" ,      lowerCamelCase      )
       self.assertListEqual(
           list(processed_pad.attention_mask.shape      ) ,      [processed_pad[input_name].shape[0], max_length]      )
       self.assertListEqual(
           processed_pad.attention_mask[:, :max_length].sum(-1      ).tolist() ,      [max_length for x in speech_inputs]      )





