



"""simple docstring"""

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

from tabulate import DataRow, TableFormat, tabulate


lowercase__					:						Tuple												=			TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('''''', '''|''', '''|'''),
    datarow=DataRow('''''', '''|''', '''|'''),
    padding=1,
    with_header_hide=None,
)


lowercase__					:						Any												=			[]
lowercase__					:						Dict												=			[]

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

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

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

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

               lowercase__					:						List[str]												=			[test[0] for test in failed_table]
               lowercase__					:						Optional[Any]												=			list(set(files))
               # Count number of instances in failed_tests
               lowercase__					:						Tuple												=			[]
               for file in individual_files:
                    table.append([file, len(filesafailed[file])])

               lowercase__					:						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) > 30_00:
          lowercase__					:						Tuple												=			"Too many failed tests, please see the full report in the Action results."
          lowercase__					:						Optional[int]												=			len(err) + 10
          lowercase__					:						str												=			message[: 30_00 - offset] + f'\n...\n```\n{err}'
     print(f'### {message}')
else:
     lowercase__					:						List[Any]												=			"No failed tests! 🤗"
     print(f'## {message}')
     payload.append(no_error_payload)

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

     lowercase__					:						List[str]												=			WebClient(token=os.environ['''SLACK_API_TOKEN'''])
     if message != "No failed tests! 🤗":
          lowercase__					:						List[Any]												=			{
              "type": "section",
              "text": {
                  "type": "mrkdwn",
                  "text": message,
              },
          }
          payload.append(md_report)
          lowercase__					:						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)
          lowercase__					:						List[str]												=			{
              "type": "context",
              "elements": [
                  {
                      "type": "plain_text",
                      "text": f'Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}',
                  }
              ],
          }
          payload.append(date_report)
     lowercase__					:						Dict												=			client.chat_postMessage(channel='''#accelerate-ci-daily''', text=message, blocks=payload)
     lowercase__					:						int												=			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
               lowercase__					:						List[Any]												=			""
               for i, row in enumerate(test_failures):
                    if row[0] != test_class:
                         lowercase__					:						str												=			row[0]
                    else:
                         lowercase__					:						Any												=			""

               lowercase__					:						str												=			{
                   "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 tensorflow as tf

from ...tf_utils import shape_list




class  _UpperCAmelCase (    tf.keras.layers.Layer):

 def __init__(  self	: int						,  lowercase_	: Tuple						,  lowercase_	: Union[str, Any]						,  lowercase_	: Tuple						,  lowercase_	: List[Any]						,  lowercase_	: Tuple=1						,  lowercase_	: List[str]=False						,  **lowercase_	: Optional[Any]       ):
     super().__init__(**lowercase_       )

     snake_case_				:  int       =			vocab_size
     snake_case_				:  Union[str, Any]       =			d_embed
     snake_case_				:  Optional[int]       =			d_proj

     snake_case_				:  int       =			cutoffs + [vocab_size]
     snake_case_				:  Optional[int]       =			[0] + self.cutoffs
     snake_case_				:  List[str]       =			div_val

     snake_case_				:  int       =			self.cutoffs[0]
     snake_case_				:  Optional[Any]       =			len(self.cutoffs       ) - 1
     snake_case_				:  Any       =			self.shortlist_size + self.n_clusters
     snake_case_				:  Dict       =			keep_order

     snake_case_				:  Tuple       =			[]
     snake_case_				:  Optional[Any]       =			[]

 def 		_snake_case      (  self	: Dict						,  lowercase_	: int       ):
     if self.n_clusters > 0:
         snake_case_				:  Tuple       =			self.add_weight(
             shape=(self.n_clusters, self.d_embed)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name='''cluster_weight'''       )
         snake_case_				:  List[str]       =			self.add_weight(
             shape=(self.n_clusters,)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name='''cluster_bias'''       )

     if self.div_val == 1:
         for i in range(len(self.cutoffs       )       ):
             if self.d_proj != self.d_embed:
                 snake_case_				:  List[Any]       =			self.add_weight(
                     shape=(self.d_embed, self.d_proj)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name=f"out_projs_._{i}"						,  )
                 self.out_projs.append(lowercase_       )
             else:
                 self.out_projs.append(lowercase_       )
             snake_case_				:  Any       =			self.add_weight(
                 shape=(self.vocab_size, self.d_embed)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name=f"out_layers_._{i}_._weight"						,  )
             snake_case_				:  Any       =			self.add_weight(
                 shape=(self.vocab_size,)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name=f"out_layers_._{i}_._bias"						,  )
             self.out_layers.append((weight, bias)       )
     else:
         for i in range(len(self.cutoffs       )       ):
             snake_case_,			snake_case_				:  str       =			self.cutoff_ends[i], self.cutoff_ends[i + 1]
             snake_case_				:  List[str]       =			self.d_embed // (self.div_val**i)

             snake_case_				:  Optional[Any]       =			self.add_weight(
                 shape=(d_emb_i, self.d_proj)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name=f"out_projs_._{i}"       )
             self.out_projs.append(lowercase_       )
             snake_case_				:  Any       =			self.add_weight(
                 shape=(r_idx - l_idx, d_emb_i)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name=f"out_layers_._{i}_._weight"						,  )
             snake_case_				:  List[Any]       =			self.add_weight(
                 shape=(r_idx - l_idx,)						,  initializer='''zeros'''						,  trainable=lowercase_						,  name=f"out_layers_._{i}_._bias"						,  )
             self.out_layers.append((weight, bias)       )
     super().build(lowercase_       )

 @staticmethod
 def 		_snake_case      (  lowercase_	: Dict						,  lowercase_	: Union[str, Any]						,  lowercase_	: Optional[int]						,  lowercase_	: str=None       ):
     snake_case_				:  str       =			x
     if proj is not None:
         snake_case_				:  str       =			tf.einsum('''ibd,ed->ibe'''						,  lowercase_						,  lowercase_       )
     return tf.einsum('''ibd,nd->ibn'''						,  lowercase_						,  lowercase_       ) + b

 @staticmethod
 def 		_snake_case      (  lowercase_	: Tuple						,  lowercase_	: Union[str, Any]       ):
     snake_case_				:  Optional[int]       =			shape_list(lowercase_       )
     snake_case_				:  Any       =			tf.range(lp_size[0]						,  dtype=target.dtype       )
     snake_case_				:  Optional[int]       =			tf.stack([r, target]						,  1       )
     return tf.gather_nd(lowercase_						,  lowercase_       )






 def 		_snake_case      (  self	: Tuple						,  lowercase_	: Union[str, Any]						,  lowercase_	: int						,  lowercase_	: Tuple=True						,  lowercase_	: Tuple=False       ):
     snake_case_				:  Optional[int]       =			0
     if self.n_clusters == 0:
         snake_case_				:  List[str]       =			self._logit(lowercase_						,  self.out_layers[0][0]						,  self.out_layers[0][1]						,  self.out_projs[0]       )
         if target is not None:
             snake_case_				:  List[Any]       =			tf.nn.sparse_softmax_cross_entropy_with_logits(labels=lowercase_						,  logits=lowercase_       )
         snake_case_				:  List[Any]       =			tf.nn.log_softmax(lowercase_						,  axis=-1       )
     else:
         snake_case_				:  Optional[int]       =			shape_list(lowercase_       )
         snake_case_				:  Any       =			[]
         snake_case_				:  List[str]       =			tf.zeros(hidden_sizes[:2]       )
         for i in range(len(self.cutoffs       )       ):
             snake_case_,			snake_case_				:  Union[str, Any]       =			self.cutoff_ends[i], self.cutoff_ends[i + 1]
             if target is not None:
                 snake_case_				:  Optional[Any]       =			(target >= l_idx) & (target < r_idx)
                 snake_case_				:  Any       =			tf.where(lowercase_       )
                 snake_case_				:  List[Any]       =			tf.boolean_mask(lowercase_						,  lowercase_       ) - l_idx

             if self.div_val == 1:
                 snake_case_				:  Optional[int]       =			self.out_layers[0][0][l_idx:r_idx]
                 snake_case_				:  List[Any]       =			self.out_layers[0][1][l_idx:r_idx]
             else:
                 snake_case_				:  Dict       =			self.out_layers[i][0]
                 snake_case_				:  str       =			self.out_layers[i][1]

             if i == 0:
                 snake_case_				:  Tuple       =			tf.concat([cur_W, self.cluster_weight]						,  0       )
                 snake_case_				:  List[str]       =			tf.concat([cur_b, self.cluster_bias]						,  0       )

                 snake_case_				:  List[Any]       =			self._logit(lowercase_						,  lowercase_						,  lowercase_						,  self.out_projs[0]       )
                 snake_case_				:  Union[str, Any]       =			tf.nn.log_softmax(lowercase_       )
                 out.append(head_logprob[..., : self.cutoffs[0]]       )
                 if target is not None:
                     snake_case_				:  int       =			tf.boolean_mask(lowercase_						,  lowercase_       )
                     snake_case_				:  str       =			self._gather_logprob(lowercase_						,  lowercase_       )
             else:
                 snake_case_				:  Union[str, Any]       =			self._logit(lowercase_						,  lowercase_						,  lowercase_						,  self.out_projs[i]       )
                 snake_case_				:  List[str]       =			tf.nn.log_softmax(lowercase_       )
                 snake_case_				:  Dict       =			self.cutoffs[0] + i - 1  # No probability for the head cluster
                 snake_case_				:  Union[str, Any]       =			head_logprob[..., cluster_prob_idx, None] + tail_logprob
                 out.append(lowercase_       )
                 if target is not None:
                     snake_case_				:  Optional[int]       =			tf.boolean_mask(lowercase_						,  lowercase_       )
                     snake_case_				:  Dict       =			tf.boolean_mask(lowercase_						,  lowercase_       )
                     snake_case_				:  Union[str, Any]       =			self._gather_logprob(lowercase_						,  lowercase_       )
                     cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
             if target is not None:
                 loss += tf.scatter_nd(lowercase_						,  -cur_logprob						,  shape_list(lowercase_       )       )
         snake_case_				:  Union[str, Any]       =			tf.concat(lowercase_						,  axis=-1       )

     if target is not None:
         if return_mean:
             snake_case_				:  Dict       =			tf.reduce_mean(lowercase_       )
         # Add the training-time loss value to the layer using `self.add_loss()`.
         self.add_loss(lowercase_       )

         # Log the loss as a metric (we could log arbitrary metrics,
         # including different metrics for training and inference.
         self.add_metric(lowercase_						,  name=self.name						,  aggregation='''mean''' if return_mean else ''''''       )

     return out





