






'''simple docstring'''


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

from tabulate import DataRow, TableFormat, tabulate


lowerCamelCase_				: Dict			=							TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('''''', '''|''', '''|'''),
    datarow=DataRow('''''', '''|''', '''|'''),
    padding=1,
    with_header_hide=None,
)


lowerCamelCase_				: Union[str, Any]			=							[]
lowerCamelCase_				: str			=							[]

lowerCamelCase_				: List[Any]			=							{"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}

lowerCamelCase_				: int			=							[
    {
        "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_				: Union[str, Any]			=							0
					with open(log, '''r''') as f:
										for line in f:
															lowerCamelCase_				: Tuple			=							json.loads(line)
															if line.get('''nodeid''', '''''') != "":
																				lowerCamelCase_				: str			=							line["nodeid"]
																				if line.get('''duration''', None) is not None:
																									lowerCamelCase_				: Tuple			=							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_				: Optional[int]			=							[]
					log.unlink()

lowerCamelCase_				: Tuple			=							""
lowerCamelCase_				: str			=							[]
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_				: Tuple			=							[]
															lowerCamelCase_				: Dict			=							{}
															for test in failed_tests:
																				lowerCamelCase_				: Union[str, Any]			=							test[0].split('''::''')
																				lowerCamelCase_				: Any			=							data[0].split('''/''')[-1]
																				if data[0] not in filesafailed:
																									lowerCamelCase_				: Optional[Any]			=							[data[1:]]
																				else:
																									filesafailed[data[0]] += [data[1:]]
																				failed_table.append(data)

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

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

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

					lowerCamelCase_				: str			=							WebClient(token=os.environ['''SLACK_API_TOKEN'''])
					if message != "No failed tests! 🤗":
										lowerCamelCase_				: Union[str, Any]			=							{
										    "type": "section",
										    "text": {
										        "type": "mrkdwn",
										        "text": message,
										    },
										}
										payload.append(md_report)
										lowerCamelCase_				: Dict			=							{
										    "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_				: Tuple			=							{
										    "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[Any]			=							client.chat_postMessage(channel='''#accelerate-ci-daily''', text=message, blocks=payload)
					lowerCamelCase_				: int			=							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_				: List[Any]			=							""
															for i, row in enumerate(test_failures):
																				if row[0] != test_class:
																									lowerCamelCase_				: Union[str, Any]			=							row[0]
																				else:
																									lowerCamelCase_				: List[Any]			=							""

															lowerCamelCase_				: Union[str, Any]			=							{
															    "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],
															)








'''simple docstring'''


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							int					     =					"microsoft/speecht5_tts"
			__a							:							int					     =					(
			    "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
			    "text to read (in English) and returns a waveform object containing the sound."
			)
			__a							:							Optional[Any]					     =					"text_reader"
			__a							:							Tuple					     =					SpeechTaProcessor
			__a							:							int					     =					SpeechTaForTextToSpeech
			__a							:							int					     =					SpeechTaHifiGan

			__a							:							Optional[int]					     =					["text"]
			__a							:							Union[str, Any]					     =					["audio"]






			def 				A      (   self							:						str			)							->    Optional[int]:

									'''simple docstring'''







									if self.post_processor is None:
															UpperCamelCase__         = """microsoft/speecht5_hifigan"""
									super().setup()






			def 				A      (   self							:						List[Any] ,      lowercase							:						Any ,      lowercase							:						Union[str, Any]=None			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = self.pre_processor(text=lowercase ,      return_tensors="""pt""" ,      truncation=lowercase			)

									if speaker_embeddings is None:
															if not is_datasets_available():
																					raise ImportError("""Datasets needs to be installed if not passing speaker embeddings."""			)

															UpperCamelCase__         = load_dataset("""Matthijs/cmu-arctic-xvectors""" ,      split="""validation"""			)
															UpperCamelCase__         = torch.tensor(embeddings_dataset[7_3_0_5]["""xvector"""]			).unsqueeze(0			)

									return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[int]			)							->    int:

									'''simple docstring'''







									with torch.no_grad():
															return self.model.generate_speech(**lowercase			)







			def 				A      (   self							:						str ,      lowercase							:						Union[str, Any]			)							->    Tuple:

									'''simple docstring'''







									with torch.no_grad():
															return self.post_processor(lowercase			).cpu().detach()
