


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

from tabulate import DataRow, TableFormat, tabulate


UpperCAmelCase_      : Union[str, Any]  					=       TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('', '|', '|'),
    datarow=DataRow('', '|', '|'),
    padding=1,
    with_header_hide=None,
)


UpperCAmelCase_      : Optional[Any]  					=       []
UpperCAmelCase_      : int  					=       []

UpperCAmelCase_      : List[Any]  					=       {'type': 'section', 'text': {'type': 'plain_text', 'text': 'No failed tests! 🤗', 'emoji': True}}

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

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

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

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

         UpperCAmelCase_      : List[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) > 3000:
      UpperCAmelCase_      : Optional[Any]  					=       'Too many failed tests, please see the full report in the Action results.'
      UpperCAmelCase_      : int  					=       len(err) + 10
      UpperCAmelCase_      : int  					=       message[: 3000 - offset] + F'\n...\n```\n{err}'
   print(F'### {message}')
else:
   UpperCAmelCase_      : Union[str, Any]  					=       'No failed tests! 🤗'
   print(F'## {message}')
   payload.append(no_error_payload)

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

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

         UpperCAmelCase_      : 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],
         )








"""simple docstring"""




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch






@is_pipeline_test
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				__UpperCamelCase			  =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					TextaTextGenerationPipeline(model=_a		, tokenizer=_a )
							return generator, ["Something to write", "Something else"]





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					generator("""Something there""" )
							self.assertEqual(_a		, [{"""generated_text""": ANY(_a )}] )
							# These are encoder decoder, they don't just append to incoming string
							self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there""" ) )

							lowerCamelCase			      =					generator(["""This is great !""", """Something else"""]		, num_return_sequences=2		, do_sample=_a )
							self.assertEqual(
							    _a		, [
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							    ]		, )

							lowerCamelCase			      =					generator(
							    ["""This is great !""", """Something else"""]		, num_return_sequences=2		, batch_size=2		, do_sample=_a )
							self.assertEqual(
							    _a		, [
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							    ]		, )

							with self.assertRaises(_a ):
										generator(4 )





				@require_torch
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""pt""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )

							lowerCamelCase			      =					3
							lowerCamelCase			      =					generator(
							    """Something there"""		, num_return_sequences=_a		, num_beams=_a		, )
							lowerCamelCase			      =					[
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """"""},
							]
							self.assertEqual(_a		, _a )

							lowerCamelCase			      =					generator("""This is a test"""		, do_sample=_a		, num_return_sequences=2		, return_tensors=_a )
							self.assertEqual(
							    _a		, [
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							    ]		, )
							lowerCamelCase			      =					generator.model.config.eos_token_id
							lowerCamelCase			      =					"""<pad>"""
							lowerCamelCase			      =					generator(
							    ["""This is a test""", """This is a second test"""]		, do_sample=_a		, num_return_sequences=2		, batch_size=2		, return_tensors=_a		, )
							self.assertEqual(
							    _a		, [
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							        ],
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							        ],
							    ]		, )





				@require_tf
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""tf""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )



