




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class        __snake_case  (unittest.TestCase		):



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





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      tempfile.mkdtemp()

								_lowerCAmelCase    :    str										=      [
								    """[UNK]""",
								    """[CLS]""",
								    """[SEP]""",
								    """[PAD]""",
								    """[MASK]""",
								    """的""",
								    """价""",
								    """格""",
								    """是""",
								    """15""",
								    """便""",
								    """alex""",
								    """##andra""",
								    """，""",
								    """。""",
								    """-""",
								    """t""",
								    """shirt""",
								]
								_lowerCAmelCase    :    List[str]										=      os.path.join(self.tmpdirname		,	VOCAB_FILES_NAMES["""vocab_file"""]      )
								with open(self.vocab_file		,	"""w"""		,	encoding="""utf-8"""      ) as vocab_writer:
												vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]      )      )

								_lowerCAmelCase    :    Tuple										=      {
								    """do_resize""": True,
								    """size""": {"""height""": 224, """width""": 224},
								    """do_center_crop""": True,
								    """crop_size""": {"""height""": 18, """width""": 18},
								    """do_normalize""": True,
								    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
								    """image_std""": [0.26862954, 0.26130258, 0.27577711],
								    """do_convert_rgb""": True,
								}
								_lowerCAmelCase    :    Any										=      os.path.join(self.tmpdirname		,	_UpperCAmelCase      )
								with open(self.image_processor_file		,	"""w"""		,	encoding="""utf-8"""      ) as fp:
												json.dump(_UpperCAmelCase		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]		,	**_UpperCAmelCase      :		str      )  ->    int:





								'''simple docstring'''




								return BertTokenizer.from_pretrained(self.tmpdirname		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	**_UpperCAmelCase      :		Dict      )  ->    Optional[Any]:





								'''simple docstring'''




								return BertTokenizerFast.from_pretrained(self.tmpdirname		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	**_UpperCAmelCase      :		List[Any]      )  ->    str:





								'''simple docstring'''




								return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    List[Any]:





								'''simple docstring'''




								shutil.rmtree(self.tmpdirname      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      [np.random.randint(255		,	size=(3, 30, 400)		,	dtype=np.uinta      )]

								_lowerCAmelCase    :    int										=      [Image.fromarray(np.moveaxis(_UpperCAmelCase		,	0		,	-1      )      ) for x in image_inputs]

								return image_inputs



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      self.get_tokenizer()
								_lowerCAmelCase    :    List[Any]										=      self.get_rust_tokenizer()
								_lowerCAmelCase    :    int										=      self.get_image_processor()

								_lowerCAmelCase    :    Any										=      ChineseCLIPProcessor(tokenizer=_UpperCAmelCase		,	image_processor=_UpperCAmelCase      )
								processor_slow.save_pretrained(self.tmpdirname      )
								_lowerCAmelCase    :    Optional[int]										=      ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,	use_fast=_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[Any]										=      ChineseCLIPProcessor(tokenizer=_UpperCAmelCase		,	image_processor=_UpperCAmelCase      )
								processor_fast.save_pretrained(self.tmpdirname      )
								_lowerCAmelCase    :    List[str]										=      ChineseCLIPProcessor.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		,	_UpperCAmelCase      )
								self.assertIsInstance(processor_fast.tokenizer		,	_UpperCAmelCase      )

								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		,	_UpperCAmelCase      )
								self.assertIsInstance(processor_fast.image_processor		,	_UpperCAmelCase      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,	image_processor=self.get_image_processor()      )
								processor.save_pretrained(self.tmpdirname      )

								_lowerCAmelCase    :    str										=      self.get_tokenizer(cls_token="""(CLS)"""		,	sep_token="""(SEP)"""      )
								_lowerCAmelCase    :    Optional[int]										=      self.get_image_processor(do_normalize=_UpperCAmelCase      )

								_lowerCAmelCase    :    Union[str, Any]										=      ChineseCLIPProcessor.from_pretrained(
								    self.tmpdirname		,	cls_token="""(CLS)"""		,	sep_token="""(SEP)"""		,	do_normalize=_UpperCAmelCase      )

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

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



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





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      self.get_image_processor()
								_lowerCAmelCase    :    Any										=      self.get_tokenizer()

								_lowerCAmelCase    :    List[Any]										=      ChineseCLIPProcessor(tokenizer=_UpperCAmelCase		,	image_processor=_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[int]										=      self.prepare_image_inputs()

								_lowerCAmelCase    :    List[Any]										=      image_processor(_UpperCAmelCase		,	return_tensors="""np"""      )
								_lowerCAmelCase    :    List[str]										=      processor(images=_UpperCAmelCase		,	return_tensors="""np"""      )

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



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





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      self.get_image_processor()
								_lowerCAmelCase    :    Any										=      self.get_tokenizer()

								_lowerCAmelCase    :    List[Any]										=      ChineseCLIPProcessor(tokenizer=_UpperCAmelCase		,	image_processor=_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[Any]										=      """Alexandra，T-shirt的价格是15便士。"""

								_lowerCAmelCase    :    Optional[Any]										=      processor(text=_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      tokenizer(_UpperCAmelCase      )

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



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      self.get_image_processor()
								_lowerCAmelCase    :    Union[str, Any]										=      self.get_tokenizer()

								_lowerCAmelCase    :    Tuple										=      ChineseCLIPProcessor(tokenizer=_UpperCAmelCase		,	image_processor=_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[int]										=      """Alexandra，T-shirt的价格是15便士。"""
								_lowerCAmelCase    :    Optional[int]										=      self.prepare_image_inputs()

								_lowerCAmelCase    :    Optional[Any]										=      processor(text=_UpperCAmelCase		,	images=_UpperCAmelCase      )

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

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



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      self.get_image_processor()
								_lowerCAmelCase    :    Optional[Any]										=      self.get_tokenizer()

								_lowerCAmelCase    :    str										=      ChineseCLIPProcessor(tokenizer=_UpperCAmelCase		,	image_processor=_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								_lowerCAmelCase    :    int										=      processor.batch_decode(_UpperCAmelCase      )
								_lowerCAmelCase    :    Tuple										=      tokenizer.batch_decode(_UpperCAmelCase      )

								self.assertListEqual(_UpperCAmelCase		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.get_image_processor()
								_lowerCAmelCase    :    Union[str, Any]										=      self.get_tokenizer()

								_lowerCAmelCase    :    int										=      ChineseCLIPProcessor(tokenizer=_UpperCAmelCase		,	image_processor=_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      """Alexandra，T-shirt的价格是15便士。"""
								_lowerCAmelCase    :    Tuple										=      self.prepare_image_inputs()

								_lowerCAmelCase    :    List[str]										=      processor(text=_UpperCAmelCase		,	images=_UpperCAmelCase      )

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







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

from tabulate import DataRow, TableFormat, tabulate


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


_lowerCamelCase      :  Optional[int]      						=						[]
_lowerCamelCase      :  Tuple      						=						[]

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

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

_lowerCamelCase      :  str      						=						0
for log in Path().glob("*.log"):
	_lowerCamelCase      :  str      						=						0
	with open(log, "r") as f:
		for line in f:
			_lowerCamelCase      :  Optional[Any]      						=						json.loads(line)
			if line.get("nodeid", "") != "":
				_lowerCamelCase      :  str      						=						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      :  Optional[int]      						=						[]
	log.unlink()

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

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

			_lowerCamelCase      :  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) > 3_0_0_0:
		_lowerCamelCase      :  List[str]      						=						"Too many failed tests, please see the full report in the Action results."
		_lowerCamelCase      :  Tuple      						=						len(err) + 1_0
		_lowerCamelCase      :  Union[str, Any]      						=						message[: 3_0_0_0 - offset] + F'''\n...\n```\n{err}'''
	print(F'''### {message}''')
else:
	_lowerCamelCase      :  List[str]      						=						"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      :  int      						=						{
		    "type": "section",
		    "text": {
		        "type": "mrkdwn",
		        "text": message,
		    },
		}
		payload.append(md_report)
		_lowerCamelCase      :  List[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      :  Union[str, Any]      						=						{
		    "type": "context",
		    "elements": [
		        {
		            "type": "plain_text",
		            "text": F'''Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}''',
		        }
		    ],
		}
		payload.append(date_report)
	_lowerCamelCase      :  int      						=						client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
	_lowerCamelCase      :  List[Any]      						=						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      :  Dict      						=						""
			for i, row in enumerate(test_failures):
				if row[0] != test_class:
					_lowerCamelCase      :  Optional[int]      						=						row[0]
				else:
					_lowerCamelCase      :  List[str]      						=						""

			_lowerCamelCase      :  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],
			)

