




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_lowerCamelCase      :  Optional[int]      						=						logging.get_logger(__name__)

_lowerCamelCase      :  Optional[int]      						=						{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class        __snake_case  (__lowercase		):
				lowerCAmelCase__				=     'gptj'
				lowerCAmelCase__				=     {
				    'max_position_embeddings': 'n_positions',
				    'hidden_size': 'n_embd',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}



				def __init__(      self      :		Union[str, Any]		,	_UpperCAmelCase      :		List[Any]=5_0400		,	_UpperCAmelCase      :		Tuple=2048		,	_UpperCAmelCase      :		Optional[Any]=4096		,	_UpperCAmelCase      :		Tuple=28		,	_UpperCAmelCase      :		Optional[int]=16		,	_UpperCAmelCase      :		int=64		,	_UpperCAmelCase      :		str=None		,	_UpperCAmelCase      :		int="gelu_new"		,	_UpperCAmelCase      :		List[str]=0.0		,	_UpperCAmelCase      :		Optional[int]=0.0		,	_UpperCAmelCase      :		Any=0.0		,	_UpperCAmelCase      :		Dict=1E-5		,	_UpperCAmelCase      :		List[Any]=0.02		,	_UpperCAmelCase      :		Dict=True		,	_UpperCAmelCase      :		Dict=5_0256		,	_UpperCAmelCase      :		Union[str, Any]=5_0256		,	_UpperCAmelCase      :		Dict=False		,	**_UpperCAmelCase      :		Dict		,	)  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      vocab_size
								_lowerCAmelCase    :    str										=      n_positions
								_lowerCAmelCase    :    Any										=      n_embd
								_lowerCAmelCase    :    Union[str, Any]										=      n_layer
								_lowerCAmelCase    :    Tuple										=      n_head
								_lowerCAmelCase    :    Union[str, Any]										=      n_inner
								_lowerCAmelCase    :    Optional[int]										=      rotary_dim
								_lowerCAmelCase    :    int										=      activation_function
								_lowerCAmelCase    :    int										=      resid_pdrop
								_lowerCAmelCase    :    Tuple										=      embd_pdrop
								_lowerCAmelCase    :    str										=      attn_pdrop
								_lowerCAmelCase    :    Dict										=      layer_norm_epsilon
								_lowerCAmelCase    :    Optional[Any]										=      initializer_range
								_lowerCAmelCase    :    str										=      use_cache

								_lowerCAmelCase    :    Any										=      bos_token_id
								_lowerCAmelCase    :    Any										=      eos_token_id

								super().__init__(
								    bos_token_id=__A		,	eos_token_id=__A		,	tie_word_embeddings=__A		,	**__A      )






class        __snake_case  (__lowercase		):



				def __init__(      self      :		Any		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		Optional[int] = "default"		,	_UpperCAmelCase      :		str = None		,	_UpperCAmelCase      :		List[str] = False		,	)  ->    Dict:





								'''simple docstring'''




								super().__init__(__A		,	task=__A		,	patching_specs=__A		,	use_past=__A      )
								if not getattr(self._config		,	"""pad_token_id"""		,	__A      ):
												# TODO: how to do that better?
												_lowerCAmelCase    :    Optional[Any]										=      0



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Mapping[str, Mapping[int, str]]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
								if self.use_past:
												self.fill_with_past_key_values_(__A		,	direction="""inputs"""      )
												_lowerCAmelCase    :    Tuple										=      {0: """batch""", 1: """past_sequence + sequence"""}
								else:
												_lowerCAmelCase    :    List[str]										=      {0: """batch""", 1: """sequence"""}

								return common_inputs



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    int:





								'''simple docstring'''




								return self._config.n_layer



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    int:





								'''simple docstring'''




								return self._config.n_head



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		Dict = -1		,	_UpperCAmelCase      :		Optional[int] = -1		,	_UpperCAmelCase      :		int = False		,	_UpperCAmelCase      :		Optional[int] = None		,	)  ->    Mapping[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      super(__A		,	self      ).generate_dummy_inputs(
								    __A		,	batch_size=__A		,	seq_length=__A		,	is_pair=__A		,	framework=__A      )

								# We need to order the input in the way they appears in the forward()
								_lowerCAmelCase    :    int										=      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

								# Need to add the past_keys
								if self.use_past:
												if not is_torch_available():
																raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
												else:
																import torch

																_lowerCAmelCase					,    _lowerCAmelCase    :    int										=      common_inputs["""input_ids"""].shape
																# Not using the same length for past_key_values
																_lowerCAmelCase    :    Any										=      seqlen + 2
																_lowerCAmelCase    :    Optional[int]										=      (
																    batch,
																    self.num_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // self.num_attention_heads,
																)
																_lowerCAmelCase    :    Union[str, Any]										=      [
																    (torch.zeros(__A      ), torch.zeros(__A      )) for _ in range(self.num_layers      )
																]

								_lowerCAmelCase    :    str										=      common_inputs["""attention_mask"""]
								if self.use_past:
												_lowerCAmelCase    :    Optional[int]										=      ordered_inputs["""attention_mask"""].dtype
												_lowerCAmelCase    :    List[str]										=      torch.cat(
												    [ordered_inputs["""attention_mask"""], torch.ones(__A		,	__A		,	dtype=__A      )]		,	dim=1      )

								return ordered_inputs



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    int:





								'''simple docstring'''




								return 13







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

from tabulate import DataRow, TableFormat, tabulate


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


_lowerCamelCase      :  Optional[int]      						=						[]
_lowerCamelCase      :  Tuple      						=						[]

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

_lowerCamelCase      :  Optional[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      :  str      						=						0
	with open(log, "r") as f:
		for line in f:
			_lowerCamelCase      :  Optional[Any]      						=						json.loads(line)
			if line.get("nodeid", "") != "":
				_lowerCamelCase      :  str      						=						line["nodeid"]
				if line.get("duration", None) is not None:
					_lowerCamelCase      :  str      						=						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      :  str      						=						""
_lowerCamelCase      :  Optional[Any]      						=						[]
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      :  Any      						=						[]
			_lowerCamelCase      :  Dict      						=						{}
			for test in failed_tests:
				_lowerCamelCase      :  Union[str, Any]      						=						test[0].split("::")
				_lowerCamelCase      :  str      						=						data[0].split("/")[-1]
				if data[0] not in filesafailed:
					_lowerCamelCase      :  List[str]      						=						[data[1:]]
				else:
					filesafailed[data[0]] += [data[1:]]
				failed_table.append(data)

			_lowerCamelCase      :  List[Any]      						=						[test[0] for test in failed_table]
			_lowerCamelCase      :  Optional[Any]      						=						list(set(files))
			# Count number of instances in failed_tests
			_lowerCamelCase      :  Union[str, Any]      						=						[]
			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[str]      						=						"Too many failed tests, please see the full report in the Action results."
		_lowerCamelCase      :  Tuple      						=						len(err) + 1_0
		_lowerCamelCase      :  Union[str, Any]      						=						message[: 3_0_0_0 - offset] + F'''\n...\n```\n{err}'''
	print(F'''### {message}''')
else:
	_lowerCamelCase      :  List[str]      						=						"No failed tests! 🤗"
	print(F'''## {message}''')
	payload.append(no_error_payload)

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

	_lowerCamelCase      :  Optional[int]      						=						WebClient(token=os.environ["SLACK_API_TOKEN"])
	if message != "No failed tests! 🤗":
		_lowerCamelCase      :  int      						=						{
		    "type": "section",
		    "text": {
		        "type": "mrkdwn",
		        "text": message,
		    },
		}
		payload.append(md_report)
		_lowerCamelCase      :  List[Any]      						=						{
		    "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      :  Union[str, Any]      						=						{
		    "type": "context",
		    "elements": [
		        {
		            "type": "plain_text",
		            "text": F'''Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}''',
		        }
		    ],
		}
		payload.append(date_report)
	_lowerCamelCase      :  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      :  Dict      						=						""
			for i, row in enumerate(test_failures):
				if row[0] != test_class:
					_lowerCamelCase      :  Optional[int]      						=						row[0]
				else:
					_lowerCamelCase      :  List[str]      						=						""

			_lowerCamelCase      :  int      						=						{
			    "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],
			)

