


"""simple docstring"""







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_   :   List[Any]									=  []
lowerCamelCase_   :   Optional[Any]									=  []

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

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

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

lowerCamelCase_   :   List[Any]									=  """"""
lowerCamelCase_   :   List[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_   :   Optional[Any]									=  []
                     lowerCamelCase_   :   Optional[int]									=  {}
                     for test in failed_tests:
                            lowerCamelCase_   :   Union[str, Any]									=  test[0].split("""::""")
                            lowerCamelCase_   :   Union[str, Any]									=  data[0].split("""/""")[-1]
                            if data[0] not in filesafailed:
                                   lowerCamelCase_   :   str									=  [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_   :   str									=  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_   :   int									=  """Too many failed tests, please see the full report in the Action results."""
              lowerCamelCase_   :   str									=  len(err) + 1_0
              lowerCamelCase_   :   Optional[Any]									=  message[: 3_0_0_0 - 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_   :   List[Any]									=  WebClient(token=os.environ["""SLACK_API_TOKEN"""])
       if message != "No failed tests! 🤗":
              lowerCamelCase_   :   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_   :   Optional[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_   :   Optional[int]									=  client.chat_postMessage(channel="""#accelerate-ci-daily""", text=message, blocks=payload)
       lowerCamelCase_   :   Union[str, 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_   :   int									=  """"""
                     for i, row in enumerate(test_failures):
                            if row[0] != test_class:
                                   lowerCamelCase_   :   str									=  row[0]
                            else:
                                   lowerCamelCase_   :   str									=  """"""

                     lowerCamelCase_   :   Dict									=  {
                         """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"""
from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)






class 					_UpperCAmelCase   (	lowercase_ ):


					def __init__(					self			:int     ,       __UpperCamelCase			:Distribution     ,       __UpperCamelCase			:Dict=None     ,       __UpperCamelCase			:Optional[int]=None     ,       __UpperCamelCase			:List[str]=0		):
												A		=  1.0 if scale is None else scale
												A		=  0.0 if loc is None else loc

												super().__init__(__UpperCamelCase     ,       [AffineTransform(loc=self.loc     ,       scale=self.scale     ,       event_dim=__UpperCamelCase		)]		)


					@property
					def 	lowerCamelCase       (					self			:Any		):
												return self.base_dist.mean * self.scale + self.loc


					@property
					def 	lowerCamelCase       (					self			:Optional[int]		):
												return self.base_dist.variance * self.scale**2


					@property
					def 	lowerCamelCase       (					self			:Dict		):
												return self.variance.sqrt()







class 					_UpperCAmelCase   (	nn.Module ):


					def __init__(					self			:Dict     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:Dict[str, int]     ,       __UpperCamelCase			:Callable[..., Tuple[torch.Tensor]]     ,       **__UpperCamelCase			:str		):
												super().__init__(**__UpperCamelCase		)
												A		=  args_dim
												A		=  nn.ModuleList([nn.Linear(__UpperCamelCase     ,       __UpperCamelCase		) for dim in args_dim.values()]		)
												A		=  domain_map


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  [proj(__UpperCamelCase		) for proj in self.proj]

												return self.domain_map(*__UpperCamelCase		)







class 					_UpperCAmelCase   (	nn.Module ):


					def __init__(					self			:Dict     ,       __UpperCamelCase			:int		):
												super().__init__()
												A		=  function


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:Any     ,       *__UpperCamelCase			:Any		):
												return self.function(__UpperCamelCase     ,       *__UpperCamelCase		)







class 					_UpperCAmelCase   :
					UpperCamelCase      =							42
					UpperCamelCase      =							42
					UpperCamelCase      =							42


					def __init__(					self			:Any     ,       __UpperCamelCase			:int = 1		):
												A		=  dim
												A		=  {k: dim * self.args_dim[k] for k in self.args_dim}


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:Dict		):
												if self.dim == 1:
																			return self.distribution_class(*__UpperCamelCase		)
												else:
																			return Independent(self.distribution_class(*__UpperCamelCase		)     ,       1		)


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Optional[torch.Tensor] = None     ,       __UpperCamelCase			:Optional[torch.Tensor] = None     ,       ):
												A		=  self._base_distribution(__UpperCamelCase		)
												if loc is None and scale is None:
																			return distr
												else:
																			return AffineTransformed(__UpperCamelCase     ,       loc=__UpperCamelCase     ,       scale=__UpperCamelCase     ,       event_dim=self.event_dim		)


					@property
					def 	lowerCamelCase       (					self			:List[Any]		):
												return () if self.dim == 1 else (self.dim,)


					@property
					def 	lowerCamelCase       (					self			:Tuple		):
												return len(self.event_shape		)


					@property
					def 	lowerCamelCase       (					self			:int		):
												return 0.0


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:int		):
												return ParameterProjection(
												    in_features=__UpperCamelCase     ,       args_dim=self.args_dim     ,       domain_map=LambdaLayer(self.domain_map		)     ,       )


					def 	lowerCamelCase       (					self			:List[Any]     ,       *__UpperCamelCase			:torch.Tensor		):
												raise NotImplementedError()


					@staticmethod
					def 	lowerCamelCase       (					__UpperCamelCase			:torch.Tensor		):
												return (x + torch.sqrt(torch.square(__UpperCamelCase		) + 4.0		)) / 2.0







class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							{"df": 1, "loc": 1, "scale": 1}
					UpperCamelCase      =							StudentT


					@classmethod
					def 	lowerCamelCase       (					cls			:List[str]     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  cls.squareplus(__UpperCamelCase		).clamp_min(torch.finfo(scale.dtype		).eps		)
												A		=  2.0 + cls.squareplus(__UpperCamelCase		)
												return df.squeeze(-1		), loc.squeeze(-1		), scale.squeeze(-1		)







class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							{"loc": 1, "scale": 1}
					UpperCamelCase      =							Normal


					@classmethod
					def 	lowerCamelCase       (					cls			:List[Any]     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  cls.squareplus(__UpperCamelCase		).clamp_min(torch.finfo(scale.dtype		).eps		)
												return loc.squeeze(-1		), scale.squeeze(-1		)







class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							{"total_count": 1, "logits": 1}
					UpperCamelCase      =							NegativeBinomial


					@classmethod
					def 	lowerCamelCase       (					cls			:str     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  cls.squareplus(__UpperCamelCase		)
												return total_count.squeeze(-1		), logits.squeeze(-1		)


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:List[str]		):
												A,	A		=  distr_args
												if self.dim == 1:
																			return self.distribution_class(total_count=__UpperCamelCase     ,       logits=__UpperCamelCase		)
												else:
																			return Independent(self.distribution_class(total_count=__UpperCamelCase     ,       logits=__UpperCamelCase		)     ,       1		)


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Optional[torch.Tensor] = None     ,       __UpperCamelCase			:Optional[torch.Tensor] = None		):
												A,	A		=  distr_args

												if scale is not None:
																			# See scaling property of Gamma.
																			logits += scale.log()

												return self._base_distribution((total_count, logits)		)


