


'''simple docstring'''

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

from tabulate import DataRow, TableFormat, tabulate


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


lowerCamelCase							:   int  						=	[]
lowerCamelCase							:   Optional[int]  						=	[]

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

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

   lowerCamelCase							:   Any  						=	[test[0] for test in failed_table]
   lowerCamelCase							:   List[Any]  						=	list(set(files))
   # Count number of instances in failed_tests
   lowerCamelCase							:   List[str]  						=	[]
   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_000:
  lowerCamelCase							:   Dict  						=	'Too many failed tests, please see the full report in the Action results.'
  lowerCamelCase							:   List[Any]  						=	len(err) + 10
  lowerCamelCase							:   List[Any]  						=	message[: 3_000 - offset] + f"""\n...\n```\n{err}"""
 print(f"""### {message}""")
else:
 lowerCamelCase							:   Optional[int]  						=	'No failed tests! 🤗'
 print(f"""## {message}""")
 payload.append(no_error_payload)

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

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

   lowerCamelCase							:   Optional[int]  						=	{
       '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 json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import CLIPImageProcessor, CLIPProcessor


@require_vision
class   A			(unittest.TestCase    ):
			'''simple docstring'''




			def    a_       (			self   :    Union[str, Any]   )	->	Dict:







					"""simple docstring"""






					A__        =   tempfile.mkdtemp()

					# fmt: off
					A__        =   ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
					# fmt: on
					A__        =   dict(zip(__lowerCAmelCase							,	range(len(__lowerCAmelCase   )   )   )   )
					A__        =   ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
					A__        =   {"""unk_token""": """<unk>"""}

					A__        =   os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES["""vocab_file"""]   )
					A__        =   os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES["""merges_file"""]   )
					with open(self.vocab_file							,	"""w"""							,	encoding="""utf-8"""   ) as fp:
							fp.write(json.dumps(__lowerCAmelCase   ) + """\n"""   )
					with open(self.merges_file							,	"""w"""							,	encoding="""utf-8"""   ) as fp:
							fp.write("""\n""".join(__lowerCAmelCase   )   )

					A__        =   {
					    """do_resize""": True,
					    """size""": 20,
					    """do_center_crop""": True,
					    """crop_size""": 18,
					    """do_normalize""": True,
					    """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
					    """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
					}
					A__        =   os.path.join(self.tmpdirname							,	__lowerCAmelCase   )
					with open(self.image_processor_file							,	"""w"""							,	encoding="""utf-8"""   ) as fp:
							json.dump(__lowerCAmelCase							,	__lowerCAmelCase   )






			def    a_       (			self   :    Tuple							,	**__lowerCAmelCase   :    Dict   )	->	str:







					"""simple docstring"""






					return CLIPTokenizer.from_pretrained(self.tmpdirname							,	**__lowerCAmelCase   )






			def    a_       (			self   :    Union[str, Any]							,	**__lowerCAmelCase   :    Dict   )	->	List[str]:







					"""simple docstring"""






					return CLIPTokenizerFast.from_pretrained(self.tmpdirname							,	**__lowerCAmelCase   )






			def    a_       (			self   :    List[str]							,	**__lowerCAmelCase   :    Optional[Any]   )	->	Dict:







					"""simple docstring"""






					return CLIPImageProcessor.from_pretrained(self.tmpdirname							,	**__lowerCAmelCase   )






			def    a_       (			self   :    str   )	->	Dict:







					"""simple docstring"""






					shutil.rmtree(self.tmpdirname   )






			def    a_       (			self   :    str   )	->	Any:







					"""simple docstring"""






					A__        =   [np.random.randint(2_55							,	size=(3, 30, 4_00)							,	dtype=np.uinta   )]

					A__        =   [Image.fromarray(np.moveaxis(__lowerCAmelCase							,	0							,	-1   )   ) for x in image_inputs]

					return image_inputs






			def    a_       (			self   :    Optional[int]   )	->	Tuple:







					"""simple docstring"""






					A__        =   self.get_tokenizer()
					A__        =   self.get_rust_tokenizer()
					A__        =   self.get_image_processor()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )
					processor_slow.save_pretrained(self.tmpdirname   )
					A__        =   CLIPProcessor.from_pretrained(self.tmpdirname							,	use_fast=__lowerCAmelCase   )

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )
					processor_fast.save_pretrained(self.tmpdirname   )
					A__        =   CLIPProcessor.from_pretrained(self.tmpdirname   )

					self.assertEqual(processor_slow.tokenizer.get_vocab()							,	tokenizer_slow.get_vocab()   )
					self.assertEqual(processor_fast.tokenizer.get_vocab()							,	tokenizer_fast.get_vocab()   )
					self.assertEqual(tokenizer_slow.get_vocab()							,	tokenizer_fast.get_vocab()   )
					self.assertIsInstance(processor_slow.tokenizer							,	__lowerCAmelCase   )
					self.assertIsInstance(processor_fast.tokenizer							,	__lowerCAmelCase   )

					self.assertEqual(processor_slow.image_processor.to_json_string()							,	image_processor.to_json_string()   )
					self.assertEqual(processor_fast.image_processor.to_json_string()							,	image_processor.to_json_string()   )
					self.assertIsInstance(processor_slow.image_processor							,	__lowerCAmelCase   )
					self.assertIsInstance(processor_fast.image_processor							,	__lowerCAmelCase   )






			def    a_       (			self   :    Optional[Any]   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   CLIPProcessor(tokenizer=self.get_tokenizer()							,	image_processor=self.get_image_processor()   )
					processor.save_pretrained(self.tmpdirname   )

					A__        =   self.get_tokenizer(bos_token="""(BOS)"""							,	eos_token="""(EOS)"""   )
					A__        =   self.get_image_processor(do_normalize=__lowerCAmelCase							,	padding_value=1.0   )

					A__        =   CLIPProcessor.from_pretrained(
					    self.tmpdirname							,	bos_token="""(BOS)"""							,	eos_token="""(EOS)"""							,	do_normalize=__lowerCAmelCase							,	padding_value=1.0   )

					self.assertEqual(processor.tokenizer.get_vocab()							,	tokenizer_add_kwargs.get_vocab()   )
					self.assertIsInstance(processor.tokenizer							,	__lowerCAmelCase   )

					self.assertEqual(processor.image_processor.to_json_string()							,	image_processor_add_kwargs.to_json_string()   )
					self.assertIsInstance(processor.image_processor							,	__lowerCAmelCase   )






			def    a_       (			self   :    List[Any]   )	->	Dict:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   self.prepare_image_inputs()

					A__        =   image_processor(__lowerCAmelCase							,	return_tensors="""np"""   )
					A__        =   processor(images=__lowerCAmelCase							,	return_tensors="""np"""   )

					for key in input_image_proc.keys():
							self.assertAlmostEqual(input_image_proc[key].sum()							,	input_processor[key].sum()							,	delta=1e-2   )






			def    a_       (			self   :    Optional[Any]   )	->	Any:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   """lower newer"""

					A__        =   processor(text=__lowerCAmelCase   )

					A__        =   tokenizer(__lowerCAmelCase   )

					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]							,	encoded_processor[key]   )






			def    a_       (			self   :    Union[str, Any]   )	->	Dict:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   """lower newer"""
					A__        =   self.prepare_image_inputs()

					A__        =   processor(text=__lowerCAmelCase							,	images=__lowerCAmelCase   )

					self.assertListEqual(list(inputs.keys()   )							,	["""input_ids""", """attention_mask""", """pixel_values"""]   )

					# test if it raises when no input is passed
					with pytest.raises(__lowerCAmelCase   ):
							processor()






			def    a_       (			self   :    Tuple   )	->	str:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					A__        =   processor.batch_decode(__lowerCAmelCase   )
					A__        =   tokenizer.batch_decode(__lowerCAmelCase   )

					self.assertListEqual(__lowerCAmelCase							,	__lowerCAmelCase   )







			def    a_       (			self   :    Optional[int]   )	->	str:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   """lower newer"""
					A__        =   self.prepare_image_inputs()

					A__        =   processor(text=__lowerCAmelCase							,	images=__lowerCAmelCase   )

					self.assertListEqual(list(inputs.keys()   )							,	processor.model_input_names   )



