




'''simple docstring'''

import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


a       :						int								=		logging.get_logger(__name__)


a       :						Any								=		r'''
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

'''



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			@add_start_docstrings(a_							)
			def __call__(      self						:    int      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    Tuple							):



						"""simple docstring"""




						raise NotImplementedError("StoppingCriteria needs to be subclassed"							)







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def __init__(      self						:    Dict      ,		a_						:    int      ,		a_						:    Optional[int] = None							):



						"""simple docstring"""




						__snake_case			=		max_length
						__snake_case			=		max_position_embeddings






			@add_start_docstrings(a_							)
			def __call__(      self						:    str      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case			=		input_ids.shape[-1]
						__snake_case			=		cur_len >= self.max_length
						if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
									logger.warning_once(
									    "This is a friendly reminder - the current text generation call will exceed the model's predefined "
									    f'''maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '''
									    "exceptions, performance degradation, or nothing at all."							)
						return is_done







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def __init__(      self						:    List[str]      ,		a_						:    int      ,		a_						:    int							):



						"""simple docstring"""




						warnings.warn(
						    "The class `MaxNewTokensCriteria` is deprecated. "
						    f'''Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '''
						    "with `max_length = start_length + max_new_tokens` instead."      ,		a_      ,		)
						__snake_case			=		start_length
						__snake_case			=		max_new_tokens
						__snake_case			=		start_length + max_new_tokens






			@add_start_docstrings(a_							)
			def __call__(      self						:    Union[str, Any]      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    Tuple							):



						"""simple docstring"""




						return input_ids.shape[-1] >= self.max_length







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def __init__(      self						:    Tuple      ,		a_						:    float      ,		a_						:    Optional[float] = None							):



						"""simple docstring"""




						__snake_case			=		max_time
						__snake_case			=		time.time() if initial_timestamp is None else initial_timestamp






			@add_start_docstrings(a_							)
			def __call__(      self						:    str      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    str							):



						"""simple docstring"""




						return time.time() - self.initial_timestamp > self.max_time







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			@add_start_docstrings(a_							)
			def __call__(      self						:    Tuple      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    Tuple							):



						"""simple docstring"""




						return any(criteria(a_      ,		a_							) for criteria in self							)






			@property
			def 							A (      self						:    Any							):



						"""simple docstring"""




						for stopping_criterium in self:
									if isinstance(a_      ,		a_							):
												return stopping_criterium.max_length
									elif isinstance(a_      ,		a_							):
												return stopping_criterium.max_length
						return None







def __UpperCAmelCase	(   _UpperCAmelCase		:   StoppingCriteriaList		,				_UpperCAmelCase		:   int						)      ->      StoppingCriteriaList:
			__snake_case			=		stopping_criteria.max_length
			__snake_case			=		deepcopy(_UpperCAmelCase						)
			if stopping_max_length is not None and stopping_max_length != max_length:
						warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"		,				_UpperCAmelCase						)
			elif stopping_max_length is None:
						new_stopping_criteria.append(MaxLengthCriteria(max_length=_UpperCAmelCase						)						)
			return new_stopping_criteria













'''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],
                  )






