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

from tabulate import DataRow, TableFormat, tabulate


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


a_    					=       []
a_    					=       []

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

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

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

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

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

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

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

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

         a_    					=       {
             '''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 unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor

class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def __init__(		self				:					List[str] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Any=13 ,  __lowerCamelCase				:					Dict=3 ,  __lowerCamelCase				:					int=224 ,  __lowerCamelCase				:					Any=30 ,  __lowerCamelCase				:					Tuple=400 ,  __lowerCamelCase				:					int=True ,  __lowerCamelCase				:					List[str]=None ,  __lowerCamelCase				:					Any=True ,  __lowerCamelCase				:					Dict=[0.5, 0.5, 0.5] ,  __lowerCamelCase				:					List[Any]=[0.5, 0.5, 0.5] ,  ):
		UpperCamelCase     :List[Any]          =	size if size is not None else {"""height""": 18, """width""": 18}
		UpperCamelCase     :str          =	parent
		UpperCamelCase     :Optional[int]          =	batch_size
		UpperCamelCase     :Dict          =	num_channels
		UpperCamelCase     :str          =	image_size
		UpperCamelCase     :Dict          =	min_resolution
		UpperCamelCase     :str          =	max_resolution
		UpperCamelCase     :Union[str, Any]          =	do_resize
		UpperCamelCase     :Optional[Any]          =	size
		UpperCamelCase     :Any          =	do_normalize
		UpperCamelCase     :Optional[Any]          =	image_mean
		UpperCamelCase     :Tuple          =	image_std





	def 						_A (		self				:					int	):
		return {
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_normalize": self.do_normalize,
		    "do_resize": self.do_resize,
		    "size": self.size,
		}

@require_torch
@require_vision
class     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     List[Any]     	=  ViTImageProcessor if is_vision_available() else None





	def 						_A (		self				:					str	):
		UpperCamelCase     :Tuple          =	EfficientFormerImageProcessorTester(self	)





	@property
	def 						_A (		self				:					List[str]	):
		return self.image_proc_tester.prepare_image_processor_dict()





	def 						_A (		self				:					int	):
		UpperCamelCase     :List[Any]          =	self.image_processing_class(**self.image_processor_dict	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """image_mean"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """image_std"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_normalize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_resize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """size"""	)	)





	def 						_A (		self				:					Optional[int]	):
		pass





	def 						_A (		self				:					str	):
		# Initialize image_processor
		UpperCamelCase     :Union[str, Any]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PIL images
		UpperCamelCase     :Union[str, Any]          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  Image.Image	)

		# Test not batched input
		UpperCamelCase     :List[str]          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :List[Any]          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )





	def 						_A (		self				:					Union[str, Any]	):
		# Initialize image_processor
		UpperCamelCase     :Optional[int]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random numpy tensors
		UpperCamelCase     :List[Any]          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase ,  numpify=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  np.ndarray	)

		# Test not batched input
		UpperCamelCase     :Dict          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :Tuple          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )





	def 						_A (		self				:					List[Any]	):
		# Initialize image_processor
		UpperCamelCase     :List[str]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PyTorch tensors
		UpperCamelCase     :Any          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase ,  torchify=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  torch.Tensor	)

		# Test not batched input
		UpperCamelCase     :List[Any]          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :str          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )
