
"""simple docstring"""




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

from tabulate import DataRow, TableFormat, tabulate


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


UpperCamelCase_									=[]
UpperCamelCase_									=[]

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

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

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

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

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

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

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

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

																		UpperCamelCase_									={
																		    """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
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 			_a						(				unittest.TestCase    ):






				def     snake_case     (			self		:   Tuple							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : int         =   tempfile.mkdtemp()

										_UpperCamelCase      : List[str]         =   [
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''[PAD]''',
										    '''[MASK]''',
										    '''的''',
										    '''价''',
										    '''格''',
										    '''是''',
										    '''15''',
										    '''便''',
										    '''alex''',
										    '''##andra''',
										    '''，''',
										    '''。''',
										    '''-''',
										    '''t''',
										    '''shirt''',
										]
										_UpperCamelCase      : Optional[Any]         =   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]							)							)

										_UpperCamelCase      : Dict         =   {
										    '''do_resize''': True,
										    '''size''': {'''height''': 2_2_4, '''width''': 2_2_4},
										    '''do_center_crop''': True,
										    '''crop_size''': {'''height''': 1_8, '''width''': 1_8},
										    '''do_normalize''': True,
										    '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
										    '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
										    '''do_convert_rgb''': True,
										}
										_UpperCamelCase      : Optional[Any]         =   os.path.join(self.tmpdirname,			lowerCAmelCase__							)
										with open(self.image_processor_file,			'''w''',			encoding='''utf-8'''							) as fp:
																json.dump(lowerCAmelCase__,			lowerCAmelCase__							)






				def     snake_case     (			self		:   str,			**lowerCAmelCase__		:   List[Any]							)     ->				Optional[int]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Union[str, Any],			**lowerCAmelCase__		:   Tuple							)     ->				str:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Any,			**lowerCAmelCase__		:   Optional[int]							)     ->				Optional[Any]:




										'''simple docstring'''



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






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




										'''simple docstring'''



										shutil.rmtree(self.tmpdirname							)






				def     snake_case     (			self		:   Any							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   [np.random.randint(2_5_5,			size=(3, 3_0, 4_0_0),			dtype=np.uinta							)]

										_UpperCamelCase      : List[Any]         =   [Image.fromarray(np.moveaxis(lowerCAmelCase__,			0,			-1							)							) for x in image_inputs]

										return image_inputs






				def     snake_case     (			self		:   str							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.get_tokenizer()
										_UpperCamelCase      : int         =   self.get_rust_tokenizer()
										_UpperCamelCase      : int         =   self.get_image_processor()

										_UpperCamelCase      : Tuple         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)
										processor_slow.save_pretrained(self.tmpdirname							)
										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname,			use_fast=lowerCAmelCase__							)

										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)
										processor_fast.save_pretrained(self.tmpdirname							)
										_UpperCamelCase      : List[Any]         =   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,			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     snake_case     (			self		:   int							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor(tokenizer=self.get_tokenizer(),			image_processor=self.get_image_processor()							)
										processor.save_pretrained(self.tmpdirname							)

										_UpperCamelCase      : Dict         =   self.get_tokenizer(cls_token='''(CLS)''',			sep_token='''(SEP)'''							)
										_UpperCamelCase      : List[str]         =   self.get_image_processor(do_normalize=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor.from_pretrained(
										    self.tmpdirname,			cls_token='''(CLS)''',			sep_token='''(SEP)''',			do_normalize=lowerCAmelCase__							)

										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     snake_case     (			self		:   Optional[int]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.get_image_processor()
										_UpperCamelCase      : str         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : List[str]         =   self.prepare_image_inputs()

										_UpperCamelCase      : Any         =   image_processor(lowerCAmelCase__,			return_tensors='''np'''							)
										_UpperCamelCase      : Any         =   processor(images=lowerCAmelCase__,			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     snake_case     (			self		:   Optional[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



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

										_UpperCamelCase      : Any         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Tuple         =   '''Alexandra，T-shirt的价格是15便士。'''

										_UpperCamelCase      : List[str]         =   processor(text=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   tokenizer(lowerCAmelCase__							)

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






				def     snake_case     (			self		:   Dict							)     ->				Tuple:




										'''simple docstring'''



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

										_UpperCamelCase      : Dict         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   '''Alexandra，T-shirt的价格是15便士。'''
										_UpperCamelCase      : Union[str, Any]         =   self.prepare_image_inputs()

										_UpperCamelCase      : str         =   processor(text=lowerCAmelCase__,			images=lowerCAmelCase__							)

										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(lowerCAmelCase__							):
																processor()






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




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.get_image_processor()
										_UpperCamelCase      : int         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : List[Any]         =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase      : List[Any]         =   processor.batch_decode(lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   tokenizer.batch_decode(lowerCAmelCase__							)

										self.assertListEqual(lowerCAmelCase__,			lowerCAmelCase__							)






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




										'''simple docstring'''



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

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   '''Alexandra，T-shirt的价格是15便士。'''
										_UpperCamelCase      : int         =   self.prepare_image_inputs()

										_UpperCamelCase      : Dict         =   processor(text=lowerCAmelCase__,			images=lowerCAmelCase__							)

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



