


'''simple docstring'''


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

from tabulate import DataRow, TableFormat, tabulate


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


__magic_name__			=      []
__magic_name__			=      []

__magic_name__			=      {'type': 'section', 'text': {'type': 'plain_text', 'text': 'No failed tests! 🤗', 'emoji': True}}

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

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

__magic_name__			=      ''
__magic_name__			=      []
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"
                  __magic_name__			=      []
                  __magic_name__			=      {}
                  for test in failed_tests:
                        __magic_name__			=      test[0].split('::')
                        __magic_name__			=      data[0].split('/')[-1]
                        if data[0] not in filesafailed:
                              __magic_name__			=      [data[1:]]
                        else:
                              filesafailed[data[0]] += [data[1:]]
                        failed_table.append(data)

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

                  __magic_name__			=      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:
            __magic_name__			=      'Too many failed tests, please see the full report in the Action results.'
            __magic_name__			=      len(err) + 10
            __magic_name__			=      message[: 3_000 - offset] + f"""\n...\n```\n{err}"""
      print(f"""### {message}""")
else:
      __magic_name__			=      'No failed tests! 🤗'
      print(f"""## {message}""")
      payload.append(no_error_payload)

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

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

                  __magic_name__			=      {
                      '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 os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


__magic_name__			=      {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}



class 	__lowerCAmelCase		(							nn.Module    ):


  '''simple docstring'''







  def __init__(							self :       Dict	,_a :       Dict ):

        '''simple docstring'''




        super().__init__()
        A_						:  List[str]          =				torchvision.models.resnetaaa(pretrained=_a )
        A_						:  int          =				list(model.children() )[:-2]
        A_						:  int          =				nn.Sequential(*_a )
        A_						:  Optional[int]          =				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )






  def        _a							(							self :       str	,_a :       Optional[int] ):

        '''simple docstring'''




        A_						:  Tuple          =				self.pool(self.model(_a ) )
        A_						:  Any          =				torch.flatten(_a	,start_dim=2 )
        A_						:  str          =				out.transpose(1	,2 ).contiguous()
        return out  # BxNx2048







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  def __init__(							self :       int	,_a :       Optional[Any]	,_a :       Optional[Any]	,_a :       Dict	,_a :       Dict	,_a :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Dict          =				[json.loads(_a ) for l in open(_a )]
        A_						:  Optional[int]          =				os.path.dirname(_a )
        A_						:  Optional[Any]          =				tokenizer
        A_						:  Optional[Any]          =				labels
        A_						:  List[Any]          =				len(_a )
        A_						:  str          =				max_seq_length

        A_						:  str          =				transforms






  def __len__(							self :       str ):

        '''simple docstring'''




        return len(self.data )






  def __getitem__(							self :       Tuple	,_a :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]	,add_special_tokens=_a ) )
        A_   ,					A_   ,					A_						:  Dict          =				sentence[0], sentence[1:-1], sentence[-1]
        A_						:  Optional[int]          =				sentence[: self.max_seq_length]

        A_						:  Any          =				torch.zeros(self.n_classes )
        A_						:  Tuple          =				1

        A_						:  Optional[Any]          =				Image.open(os.path.join(self.data_dir	,self.data[index]["""img"""] ) ).convert("""RGB""" )
        A_						:  Union[str, Any]          =				self.transforms(_a )

        return {
            "image_start_token": start_token,
            "image_end_token": end_token,
            "sentence": sentence,
            "image": image,
            "label": label,
        }






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  str          =				Counter()
        for row in self.data:
              label_freqs.update(row["""label"""] )
        return label_freqs







def      lowerCamelCase					(      lowerCamelCase  :      str):
      A_						:  List[Any]          =				[len(row["""sentence"""]) for row in batch]
      A_   ,					A_						:  Dict          =				len(lowerCamelCase), max(lowerCamelCase)

      A_						:  Optional[int]          =				torch.zeros(lowerCamelCase							,						lowerCamelCase							,						dtype=torch.long)
      A_						:  Tuple          =				torch.zeros(lowerCamelCase							,						lowerCamelCase							,						dtype=torch.long)

      for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase							,						lowerCamelCase)):
            A_						:  str          =				input_row["""sentence"""]
            A_						:  Tuple          =				1

      A_						:  int          =				torch.stack([row["""image"""] for row in batch])
      A_						:  str          =				torch.stack([row["""label"""] for row in batch])
      A_						:  List[Any]          =				torch.stack([row["""image_start_token"""] for row in batch])
      A_						:  Tuple          =				torch.stack([row["""image_end_token"""] for row in batch])

      return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor




def      lowerCamelCase					(      ):
      return [
          "Crime",
          "Drama",
          "Thriller",
          "Action",
          "Comedy",
          "Romance",
          "Documentary",
          "Short",
          "Mystery",
          "History",
          "Family",
          "Adventure",
          "Fantasy",
          "Sci-Fi",
          "Western",
          "Horror",
          "Sport",
          "War",
          "Music",
          "Musical",
          "Animation",
          "Biography",
          "Film-Noir",
      ]






def      lowerCamelCase					(      ):
      return transforms.Compose(
          [
              transforms.Resize(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]							,						std=[0.1222_1994, 0.1214_5835, 0.1438_0469]							,						),
          ])
