
'''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) > 3_000:
												UpperCAmelCase    					=   "Too many failed tests, please see the full report in the Action results."
												UpperCAmelCase    					=   len(err) + 10
												UpperCAmelCase    					=   message[: 3_000 - 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],
																		)



'''simple docstring'''





import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
						if is_torch_available():
												import torch
						if is_tf_available():
												import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase    					=   {
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase    					=   {
    "Salesforce/codegen-350M-mono": 2_048,
}

class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = CodeGenTokenizer




				def __init__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				Dict=None,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=None,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=None,    SCREAMING_SNAKE_CASE_	:				Optional[int]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				List[Any]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				List[str]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				int=False,    **SCREAMING_SNAKE_CASE_	:				Optional[int],    ):
										super().__init__(
										    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    bos_token=SCREAMING_SNAKE_CASE_,    eos_token=SCREAMING_SNAKE_CASE_,    add_prefix_space=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										if kwargs.pop('''add_bos_token''',    SCREAMING_SNAKE_CASE_      ):
																snake_case       :     Dict =    kwargs.pop('''name_or_path''',    ''''''      )
																raise ValueError(
																    '''Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'''
																    '''Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'''
																    F"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
																    F"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
																    '''This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'''
																    ''' so that the fast tokenizer works correctly.'''      )

										snake_case       :     int =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
										if pre_tok_state.get('''add_prefix_space''',    SCREAMING_SNAKE_CASE_      ) != add_prefix_space:
																snake_case       :     Optional[Any] =    getattr(SCREAMING_SNAKE_CASE_,    pre_tok_state.pop('''type'''      )      )
																snake_case       :     Union[str, Any] =    add_prefix_space
																snake_case       :     Optional[Any] =    pre_tok_class(**SCREAMING_SNAKE_CASE_      )

										snake_case       :     int =    add_prefix_space




				def 	__snake_case							(							self	:				List[Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     Dict =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )
										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[str],    *SCREAMING_SNAKE_CASE_	:				str,    **SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										snake_case       :     Dict =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )

										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										snake_case       :     Optional[int] =    self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_      )
										return tuple(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],    SCREAMING_SNAKE_CASE_	:				bool = False,    SCREAMING_SNAKE_CASE_	:				bool = None,    SCREAMING_SNAKE_CASE_	:				Optional[List[str]] = None,    **SCREAMING_SNAKE_CASE_	:				Union[str, Any],    ):
										snake_case       :     Dict =    super().decode(
										    token_ids=SCREAMING_SNAKE_CASE_,    skip_special_tokens=SCREAMING_SNAKE_CASE_,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										if truncate_before_pattern is not None and len(SCREAMING_SNAKE_CASE_      ) > 0:
																snake_case       :     Optional[int] =    self.truncate(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )

										return decoded_text




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										def find_re(SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
																snake_case       :     Optional[Any] =    pattern.search(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
																return m.start() if m else -1

										snake_case       :     Union[str, Any] =    [re.compile(SCREAMING_SNAKE_CASE_,    re.MULTILINE      ) for pattern in truncate_before_pattern]

										snake_case       :     Union[str, Any] =    list(re.finditer('''^print''',    SCREAMING_SNAKE_CASE_,    re.MULTILINE      )      )

										if len(SCREAMING_SNAKE_CASE_      ) > 1:
																snake_case       :     Tuple =    completion[: prints[1].start()]

										snake_case       :     List[str] =    list(re.finditer('''^def''',    SCREAMING_SNAKE_CASE_,    re.MULTILINE      )      )

										if len(SCREAMING_SNAKE_CASE_      ) > 1:
																snake_case       :     Optional[Any] =    completion[: defs[1].start()]

										snake_case       :     Tuple =    0

										snake_case       :     List[Any] =    [
										    pos for pos in [find_re(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) for terminal in terminals] if pos != -1
										]

										if len(SCREAMING_SNAKE_CASE_      ) > 0:
																return completion[: min(SCREAMING_SNAKE_CASE_      )]
										else:
																return completion

