

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

from tabulate import DataRow, TableFormat, tabulate


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


_lowerCamelCase    :       Dict        =							[]
_lowerCamelCase    :       Tuple        =							[]

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

_lowerCamelCase    :       Union[str, Any]        =							[
    {
        "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    :       Dict        =							0
      with open(log, "r") as f:
            for line in f:
                  _lowerCamelCase    :       Optional[Any]        =							json.loads(line)
                  if line.get("nodeid", "") != "":
                        _lowerCamelCase    :       List[Any]        =							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    :       Union[str, Any]        =							[]
      log.unlink()

_lowerCamelCase    :       int        =							""
_lowerCamelCase    :       Tuple        =							[]
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    :       str        =							[]
                  _lowerCamelCase    :       int        =							{}
                  for test in failed_tests:
                        _lowerCamelCase    :       Dict        =							test[0].split("::")
                        _lowerCamelCase    :       List[Any]        =							data[0].split("/")[-1]
                        if data[0] not in filesafailed:
                              _lowerCamelCase    :       int        =							[data[1:]]
                        else:
                              filesafailed[data[0]] += [data[1:]]
                        failed_table.append(data)

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

                  _lowerCamelCase    :       Optional[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) > 3_0_0_0:
            _lowerCamelCase    :       Optional[int]        =							"Too many failed tests, please see the full report in the Action results."
            _lowerCamelCase    :       Optional[int]        =							len(err) + 1_0
            _lowerCamelCase    :       Optional[Any]        =							message[: 3_0_0_0 - offset] + f"""\n...\n```\n{err}"""
      print(f"""### {message}""")
else:
      _lowerCamelCase    :       Union[str, Any]        =							"No failed tests! 🤗"
      print(f"""## {message}""")
      payload.append(no_error_payload)

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

      _lowerCamelCase    :       Any        =							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    :       Optional[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    :       int        =							{
                "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    :       str        =							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    :       Optional[int]        =							""

                  _lowerCamelCase    :       Optional[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],
                  )








import torch
from torch import nn





class  __UpperCAmelCase					(	nn.Module						):



   def __init__(							self							:					List[Any],		__A							:					List[Any],		__A							:					Optional[Any],		__A							:					int,		__A							:					List[Any],		__A							:					int=1,		__A							:					List[str]=False						):
     super().__init__()

     UpperCAmelCase	:							Union[str, Any]						=			n_token
     UpperCAmelCase	:							List[str]						=			d_embed
     UpperCAmelCase	:							Dict						=			d_proj

     UpperCAmelCase	:							List[Any]						=			cutoffs + [n_token]
     UpperCAmelCase	:							Dict						=			[0] + self.cutoffs
     UpperCAmelCase	:							int						=			div_val

     UpperCAmelCase	:							Union[str, Any]						=			self.cutoffs[0]
     UpperCAmelCase	:							str						=			len(self.cutoffs						) - 1
     UpperCAmelCase	:							Optional[int]						=			self.shortlist_size + self.n_clusters

     if self.n_clusters > 0:
       UpperCAmelCase	:							str						=			nn.Parameter(torch.zeros(self.n_clusters,		self.d_embed						)						)
       UpperCAmelCase	:							List[str]						=			nn.Parameter(torch.zeros(self.n_clusters						)						)

     UpperCAmelCase	:							Dict						=			nn.ModuleList()
     UpperCAmelCase	:							Optional[int]						=			nn.ParameterList()

     if div_val == 1:
       for i in range(len(self.cutoffs						)						):
         if d_proj != d_embed:
           self.out_projs.append(nn.Parameter(torch.FloatTensor(__A,		__A						)						)						)
         else:
           self.out_projs.append(__A						)

       self.out_layers.append(nn.Linear(__A,		__A						)						)
     else:
       for i in range(len(self.cutoffs						)						):
         UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
         UpperCAmelCase	:							str						=			d_embed // (div_val**i)

         self.out_projs.append(nn.Parameter(torch.FloatTensor(__A,		__A						)						)						)

         self.out_layers.append(nn.Linear(__A,		r_idx - l_idx						)						)

     UpperCAmelCase	:							Optional[int]						=			keep_order



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					List[str],		__A							:					Any,		__A							:					Dict,		__A							:					Optional[Any]						):
     if proj is None:
       UpperCAmelCase	:							List[Any]						=			nn.functional.linear(__A,		__A,		bias=__A						)
     else:
       # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
       UpperCAmelCase	:							Union[str, Any]						=			nn.functional.linear(__A,		proj.t().contiguous()						)
       UpperCAmelCase	:							Optional[int]						=			nn.functional.linear(__A,		__A,		bias=__A						)
       # else:
       #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
       #     if bias is not None:
       #         logit = logit + bias

     return logit



   def 	__magic_name__   (							self							:					int,		__A							:					int,		__A							:					List[Any]=None,		__A							:					Dict=False						):

     if labels is not None:
       # Shift so that tokens < n predict n
       UpperCAmelCase	:							List[Any]						=			hidden[..., :-1, :].contiguous()
       UpperCAmelCase	:							Any						=			labels[..., 1:].contiguous()
       UpperCAmelCase	:							Optional[Any]						=			hidden.view(-1,		hidden.size(-1						)						)
       UpperCAmelCase	:							Union[str, Any]						=			labels.view(-1						)
       if hidden.size(0						) != labels.size(0						):
         raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''						)
     else:
       UpperCAmelCase	:							str						=			hidden.view(-1,		hidden.size(-1						)						)

     if self.n_clusters == 0:
       UpperCAmelCase	:							List[str]						=			self._compute_logit(__A,		self.out_layers[0].weight,		self.out_layers[0].bias,		self.out_projs[0]						)
       if labels is not None:
         UpperCAmelCase	:							Optional[int]						=			labels != -1_0_0
         UpperCAmelCase	:							Dict						=			torch.zeros_like(__A,		dtype=hidden.dtype,		device=hidden.device						)
         UpperCAmelCase	:							Any						=			(
             -nn.functional.log_softmax(__A,		dim=-1						)[mask].gather(1,		labels[mask].unsqueeze(1						)						).squeeze(1						)
         )
       else:
         UpperCAmelCase	:							Any						=			nn.functional.log_softmax(__A,		dim=-1						)
     else:
       # construct weights and biases
       UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			[], []
       for i in range(len(self.cutoffs						)						):
         if self.div_val == 1:
           UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
           UpperCAmelCase	:							List[str]						=			self.out_layers[0].weight[l_idx:r_idx]
           UpperCAmelCase	:							Optional[int]						=			self.out_layers[0].bias[l_idx:r_idx]
         else:
           UpperCAmelCase	:							List[Any]						=			self.out_layers[i].weight
           UpperCAmelCase	:							Dict						=			self.out_layers[i].bias

         if i == 0:
           UpperCAmelCase	:							List[str]						=			torch.cat([weight_i, self.cluster_weight],		dim=0						)
           UpperCAmelCase	:							List[Any]						=			torch.cat([bias_i, self.cluster_bias],		dim=0						)

         weights.append(__A						)
         biases.append(__A						)

       UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							str						=			weights[0], biases[0], self.out_projs[0]

       UpperCAmelCase	:							Dict						=			self._compute_logit(__A,		__A,		__A,		__A						)
       UpperCAmelCase	:							int						=			nn.functional.log_softmax(__A,		dim=1						)

       if labels is None:
         UpperCAmelCase	:							Optional[int]						=			hidden.new_empty((head_logit.size(0						), self.n_token)						)
       else:
         UpperCAmelCase	:							Union[str, Any]						=			torch.zeros_like(__A,		dtype=hidden.dtype,		device=hidden.device						)

       UpperCAmelCase	:							Union[str, Any]						=			0
       UpperCAmelCase	:							Any						=			[0] + self.cutoffs
       for i in range(len(__A						) - 1						):
         UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			cutoff_values[i], cutoff_values[i + 1]

         if labels is not None:
           UpperCAmelCase	:							List[str]						=			(labels >= l_idx) & (labels < r_idx)
           UpperCAmelCase	:							Tuple						=			mask_i.nonzero().squeeze()

           if indices_i.numel() == 0:
             continue

           UpperCAmelCase	:							Any						=			labels.index_select(0,		__A						) - l_idx
           UpperCAmelCase	:							Dict						=			head_logprob.index_select(0,		__A						)
           UpperCAmelCase	:							List[str]						=			hidden.index_select(0,		__A						)
         else:
           UpperCAmelCase	:							Tuple						=			hidden

         if i == 0:
           if labels is not None:
             UpperCAmelCase	:							Union[str, Any]						=			head_logprob_i.gather(1,		target_i[:, None]						).squeeze(1						)
           else:
             UpperCAmelCase	:							Optional[int]						=			head_logprob[:, : self.cutoffs[0]]
         else:
           UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							List[str]						=			weights[i], biases[i], self.out_projs[i]

           UpperCAmelCase	:							List[str]						=			self._compute_logit(__A,		__A,		__A,		__A						)
           UpperCAmelCase	:							Dict						=			nn.functional.log_softmax(__A,		dim=1						)
           UpperCAmelCase	:							int						=			self.cutoffs[0] + i - 1  # No probability for the head cluster
           if labels is not None:
             UpperCAmelCase	:							Union[str, Any]						=			head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                 1,		target_i[:, None]						).squeeze(1						)
           else:
             UpperCAmelCase	:							int						=			head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
             UpperCAmelCase	:							Optional[Any]						=			logprob_i

         if labels is not None:
           if (hasattr(self,		'''keep_order'''						) and self.keep_order) or keep_order:
             out.index_copy_(0,		__A,		-logprob_i						)
           else:
             out[offset : offset + logprob_i.size(0						)].copy_(-logprob_i						)
           offset += logprob_i.size(0						)

     return out





   def 	__magic_name__   (							self							:					Tuple,		__A							:					List[str]						):
     if self.n_clusters == 0:
       UpperCAmelCase	:							int						=			self._compute_logit(__A,		self.out_layers[0].weight,		self.out_layers[0].bias,		self.out_projs[0]						)
       return nn.functional.log_softmax(__A,		dim=-1						)
     else:
       # construct weights and biases
       UpperCAmelCase			,						UpperCAmelCase	:							Any						=			[], []
       for i in range(len(self.cutoffs						)						):
         if self.div_val == 1:
           UpperCAmelCase			,						UpperCAmelCase	:							Any						=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
           UpperCAmelCase	:							List[Any]						=			self.out_layers[0].weight[l_idx:r_idx]
           UpperCAmelCase	:							List[str]						=			self.out_layers[0].bias[l_idx:r_idx]
         else:
           UpperCAmelCase	:							List[str]						=			self.out_layers[i].weight
           UpperCAmelCase	:							Dict						=			self.out_layers[i].bias

         if i == 0:
           UpperCAmelCase	:							Dict						=			torch.cat([weight_i, self.cluster_weight],		dim=0						)
           UpperCAmelCase	:							str						=			torch.cat([bias_i, self.cluster_bias],		dim=0						)

         weights.append(__A						)
         biases.append(__A						)

       UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			weights[0], biases[0], self.out_projs[0]
       UpperCAmelCase	:							int						=			self._compute_logit(__A,		__A,		__A,		__A						)

       UpperCAmelCase	:							Optional[int]						=			hidden.new_empty((head_logit.size(0						), self.n_token)						)
       UpperCAmelCase	:							Dict						=			nn.functional.log_softmax(__A,		dim=1						)

       UpperCAmelCase	:							List[str]						=			[0] + self.cutoffs
       for i in range(len(__A						) - 1						):
         UpperCAmelCase			,						UpperCAmelCase	:							Optional[Any]						=			cutoff_values[i], cutoff_values[i + 1]

         if i == 0:
           UpperCAmelCase	:							Any						=			head_logprob[:, : self.cutoffs[0]]
         else:
           UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							int						=			weights[i], biases[i], self.out_projs[i]

           UpperCAmelCase	:							Tuple						=			self._compute_logit(__A,		__A,		__A,		__A						)
           UpperCAmelCase	:							List[Any]						=			nn.functional.log_softmax(__A,		dim=1						)

           UpperCAmelCase	:							Optional[int]						=			head_logprob[:, -i] + tail_logprob_i
           UpperCAmelCase	:							Optional[Any]						=			logprob_i

       return out





