

import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
			import torch

if is_datasets_available():
			from datasets import load_dataset

a_      =     random.Random()

def 			lowerCamelCase__			(       _a				,      _a=1.0				,      _a=None				,      _a=None):
	if rng is None:
		SCREAMING_SNAKE_CASE :   List[str]					  =							global_rng

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

	return values






class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def __init__(      self				:			str   ,   a				:			Any   ,   a				:			Union[str, Any]=7   ,   a				:			List[Any]=400   ,   a				:			str=2000   ,   a				:			Dict=2048   ,   a				:			List[Any]=128   ,   a				:			Tuple=1   ,   a				:			Union[str, Any]=512   ,   a				:			List[str]=30   ,   a				:			Tuple=4_4100   ,   )				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							parent
				SCREAMING_SNAKE_CASE :   List[str]					  =							batch_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							min_seq_length
				SCREAMING_SNAKE_CASE :   List[str]					  =							max_seq_length
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				SCREAMING_SNAKE_CASE :   Dict					  =							spectrogram_length
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							feature_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							num_audio_channels
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							hop_length
				SCREAMING_SNAKE_CASE :   List[Any]					  =							chunk_length
				SCREAMING_SNAKE_CASE :   List[str]					  =							sampling_rate
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Optional[int]:

				"""simple docstring"""

				return {
				    "spectrogram_length": self.spectrogram_length,
				    "feature_size": self.feature_size,
				    "num_audio_channels": self.num_audio_channels,
				    "hop_length": self.hop_length,
				    "chunk_length": self.chunk_length,
				    "sampling_rate": self.sampling_rate,
				}






			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			int=False   ,   a				:			Tuple=False						)				->							Union[str, Any]:

				"""simple docstring"""

				def _flatten(a				:			Any						):
					return list(itertools.chain(*a						)						)

				if equal_length:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							[floats_list((self.max_seq_length, self.feature_size)						) for _ in range(self.batch_size						)]
				else:
					# make sure that inputs increase in size
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							[
					    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 :   Any					  =							[np.asarray(a						) for x in speech_inputs]
				return speech_inputs






@require_torch
@require_torchaudio
class _UpperCamelCase				(				__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =TvltFeatureExtractor
			def 				__UpperCamelCase			(      self				:			int						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							TvltFeatureExtractionTester(self						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.feature_extraction_class(**self.feat_extract_dict						)
				self.assertTrue(hasattr(a   ,   "spectrogram_length"						)						)
				self.assertTrue(hasattr(a   ,   "feature_size"						)						)
				self.assertTrue(hasattr(a   ,   "num_audio_channels"						)						)
				self.assertTrue(hasattr(a   ,   "hop_length"						)						)
				self.assertTrue(hasattr(a   ,   "chunk_length"						)						)
				self.assertTrue(hasattr(a   ,   "sampling_rate"						)						)
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.feature_extraction_class(**self.feat_extract_dict						)

				with tempfile.TemporaryDirectory() as tmpdirname:
					SCREAMING_SNAKE_CASE :   List[Any]					  =							feat_extract_first.save_pretrained(a						)[0]
					check_json_file_has_correct_format(a						)
					SCREAMING_SNAKE_CASE :   Any					  =							self.feature_extraction_class.from_pretrained(a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							feat_extract_first.to_dict()
				SCREAMING_SNAKE_CASE :   int					  =							feat_extract_second.to_dict()
				SCREAMING_SNAKE_CASE :   str					  =							dict_first.pop("mel_filters"						)
				SCREAMING_SNAKE_CASE :   str					  =							dict_second.pop("mel_filters"						)
				self.assertTrue(np.allclose(a   ,   a						)						)
				self.assertEqual(a   ,   a						)
			def 				__UpperCamelCase			(      self				:			Any						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.feature_extraction_class(**self.feat_extract_dict						)

				with tempfile.TemporaryDirectory() as tmpdirname:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							os.path.join(a   ,   "feat_extract.json"						)
					feat_extract_first.to_json_file(a						)
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.feature_extraction_class.from_json_file(a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							feat_extract_first.to_dict()
				SCREAMING_SNAKE_CASE :   Tuple					  =							feat_extract_second.to_dict()
				SCREAMING_SNAKE_CASE :   str					  =							dict_first.pop("mel_filters"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							dict_second.pop("mel_filters"						)
				self.assertTrue(np.allclose(a   ,   a						)						)
				self.assertEqual(a   ,   a						)
			def 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							self.feature_extraction_class(**self.feat_extract_dict						)

				# create three inputs of length 800, 1000, and 1200
				SCREAMING_SNAKE_CASE :   str					  =							[floats_list((1, x)						)[0] for x in range(800   ,   1400   ,   200						)]
				SCREAMING_SNAKE_CASE :   str					  =							[np.asarray(a						) for speech_input in speech_inputs]

				# Test not batched input
				SCREAMING_SNAKE_CASE :   str					  =							feature_extractor(np_speech_inputs[0]   ,   return_tensors="np"   ,   sampling_rate=4_4100						).audio_values

				self.assertTrue(encoded_audios.ndim == 4						)
				self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size						)
				self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length						)
				self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels						)

				# Test batched
				SCREAMING_SNAKE_CASE :   Tuple					  =							feature_extractor(a   ,   return_tensors="np"   ,   sampling_rate=4_4100						).audio_values

				self.assertTrue(encoded_audios.ndim == 4						)
				self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size						)
				self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length						)
				self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels						)

				# Test audio masking
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							feature_extractor(
				    a   ,   return_tensors="np"   ,   sampling_rate=4_4100   ,   mask_audio=a						).audio_values

				self.assertTrue(encoded_audios.ndim == 4						)
				self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size						)
				self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length						)
				self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels						)

				# Test 2-D numpy arrays are batched.
				SCREAMING_SNAKE_CASE :   Any					  =							[floats_list((1, x)						)[0] for x in (800, 800, 800)]
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							np.asarray(a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							feature_extractor(a   ,   return_tensors="np"   ,   sampling_rate=4_4100						).audio_values
				self.assertTrue(encoded_audios.ndim == 4						)
				self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size						)
				self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length						)
				self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels						)
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			str						)				->							str:

				"""simple docstring"""

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

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






			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self._load_datasamples(1						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							TvltFeatureExtractor()
				SCREAMING_SNAKE_CASE :   Any					  =							feature_extractor(a   ,   return_tensors="pt"						).audio_values

				self.assertEquals(audio_values.shape   ,   (1, 1, 192, 128)						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]						)
				self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]   ,   a   ,   atol=1e-4						)						)







'''simple docstring'''

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

from tabulate import DataRow, TableFormat, tabulate


UpperCamelCase      					=TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow("", "|", "|"),
    datarow=DataRow("", "|", "|"),
    padding=1,
    with_header_hide=None,
)


UpperCamelCase      					=[]
UpperCamelCase      					=[]

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

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

UpperCamelCase      					=0
for log in Path().glob("*.log"):
      UpperCamelCase      					=0
      with open(log, "r") as f:
            for line in f:
                  UpperCamelCase      					=json.loads(line)
                  if line.get("nodeid", "") != "":
                        UpperCamelCase      					=line["nodeid"]
                        if line.get("duration", None) is not None:
                              UpperCamelCase      					=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])
      UpperCamelCase      					=[]
      log.unlink()

UpperCamelCase      					=""
UpperCamelCase      					=[]
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"
                  UpperCamelCase      					=[]
                  UpperCamelCase      					={}
                  for test in failed_tests:
                        UpperCamelCase      					=test[0].split("::")
                        UpperCamelCase      					=data[0].split("/")[-1]
                        if data[0] not in filesafailed:
                              UpperCamelCase      					=[data[1:]]
                        else:
                              filesafailed[data[0]] += [data[1:]]
                        failed_table.append(data)

                  UpperCamelCase      					=[test[0] for test in failed_table]
                  UpperCamelCase      					=list(set(files))
                  # Count number of instances in failed_tests
                  UpperCamelCase      					=[]
                  for file in individual_files:
                        table.append([file, len(filesafailed[file])])

                  UpperCamelCase      					=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) > 3000:
            UpperCamelCase      					="Too many failed tests, please see the full report in the Action results."
            UpperCamelCase      					=len(err) + 10
            UpperCamelCase      					=message[: 3000 - offset] + f"\n...\n```\n{err}"
      print(f"### {message}")
else:
      UpperCamelCase      					="No failed tests! 🤗"
      print(f"## {message}")
      payload.append(no_error_payload)

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

      UpperCamelCase      					=WebClient(token=os.environ["SLACK_API_TOKEN"])
      if message != "No failed tests! 🤗":
            UpperCamelCase      					={
                "type": "section",
                "text": {
                    "type": "mrkdwn",
                    "text": message,
                },
            }
            payload.append(md_report)
            UpperCamelCase      					={
                "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)
            UpperCamelCase      					={
                "type": "context",
                "elements": [
                    {
                        "type": "plain_text",
                        "text": f"Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}",
                    }
                ],
            }
            payload.append(date_report)
      UpperCamelCase      					=client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
      UpperCamelCase      					=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
                  UpperCamelCase      					=""
                  for i, row in enumerate(test_failures):
                        if row[0] != test_class:
                              UpperCamelCase      					=row[0]
                        else:
                              UpperCamelCase      					=""

                  UpperCamelCase      					={
                      "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],
                  )






