




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
      import torch


if is_vision_available():
      from PIL import Image

      from transformers import MgpstrProcessor, ViTImageProcessor





@require_torch
@require_vision
class     SCREAMING_SNAKE_CASE__		(unittest.TestCase			):
     lowercase_ :      Optional[Any]				= ViTImageProcessor if is_vision_available() else None
     @property
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()
     def        A__      (						self	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      (3, 32, 1_28)
       lowerCAmelCase__										=      tempfile.mkdtemp()

       # fmt: off
       lowerCAmelCase__										=      ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
       # fmt: on
       lowerCAmelCase__										=      dict(zip(__lowerCamelCase       ,						range(len(__lowerCamelCase						)						)						)						)

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

       lowerCAmelCase__										=      {
           '''do_normalize''': False,
           '''do_resize''': True,
           '''image_processor_type''': '''ViTImageProcessor''',
           '''resample''': 3,
           '''size''': {'''height''': 32, '''width''': 1_28},
       }
       lowerCAmelCase__										=      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	:		int       ,						**__lowerCamelCase	:		List[str]						):



       """simple docstring"""
       return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,						**__lowerCamelCase						)
     def        A__      (						self	:		Optional[Any]       ,						**__lowerCamelCase	:		Optional[int]						):



       """simple docstring"""
       return ViTImageProcessor.from_pretrained(self.tmpdirname       ,						**__lowerCamelCase						)
     def        A__      (						self	:		Any						):



       """simple docstring"""
       shutil.rmtree(self.tmpdirname						)
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       lowerCAmelCase__										=      np.random.randint(2_55       ,						size=(3, 30, 4_00)       ,						dtype=np.uinta						)

       lowerCAmelCase__										=      Image.fromarray(np.moveaxis(__lowerCamelCase       ,						0       ,						-1						)						)

       return image_input
     def        A__      (						self	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      self.get_tokenizer()
       lowerCAmelCase__										=      self.get_image_processor()

       lowerCAmelCase__										=      MgpstrProcessor(tokenizer=__lowerCamelCase       ,						image_processor=__lowerCamelCase						)
       processor.save_pretrained(self.tmpdirname						)
       lowerCAmelCase__										=      MgpstrProcessor.from_pretrained(self.tmpdirname       ,						use_fast=__lowerCamelCase						)

       self.assertEqual(processor.char_tokenizer.get_vocab()       ,						tokenizer.get_vocab()						)
       self.assertIsInstance(processor.char_tokenizer       ,						__lowerCamelCase						)

       self.assertEqual(processor.image_processor.to_json_string()       ,						image_processor.to_json_string()						)
       self.assertIsInstance(processor.image_processor       ,						__lowerCamelCase						)
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.get_tokenizer()
       lowerCAmelCase__										=      self.get_image_processor()

       lowerCAmelCase__										=      MgpstrProcessor(tokenizer=__lowerCamelCase       ,						image_processor=__lowerCamelCase						)
       processor.save_pretrained(self.tmpdirname						)

       lowerCAmelCase__										=      self.get_tokenizer(bos_token='''(BOS)'''       ,						eos_token='''(EOS)'''						)
       lowerCAmelCase__										=      self.get_image_processor(do_normalize=__lowerCamelCase       ,						padding_value=1.0						)

       lowerCAmelCase__										=      MgpstrProcessor.from_pretrained(
           self.tmpdirname       ,						bos_token='''(BOS)'''       ,						eos_token='''(EOS)'''       ,						do_normalize=__lowerCamelCase       ,						padding_value=1.0						)

       self.assertEqual(processor.char_tokenizer.get_vocab()       ,						tokenizer_add_kwargs.get_vocab()						)
       self.assertIsInstance(processor.char_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	:		Optional[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.get_image_processor()
       lowerCAmelCase__										=      self.get_tokenizer()

       lowerCAmelCase__										=      MgpstrProcessor(tokenizer=__lowerCamelCase       ,						image_processor=__lowerCamelCase						)

       lowerCAmelCase__										=      self.prepare_image_inputs()

       lowerCAmelCase__										=      image_processor(__lowerCamelCase       ,						return_tensors='''np'''						)
       lowerCAmelCase__										=      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	:		Tuple						):



       """simple docstring"""
       lowerCAmelCase__										=      self.get_image_processor()
       lowerCAmelCase__										=      self.get_tokenizer()

       lowerCAmelCase__										=      MgpstrProcessor(tokenizer=__lowerCamelCase       ,						image_processor=__lowerCamelCase						)

       lowerCAmelCase__										=      '''test'''

       lowerCAmelCase__										=      processor(text=__lowerCamelCase						)

       lowerCAmelCase__										=      tokenizer(__lowerCamelCase						)
       for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]       ,						encoded_processor[key]						)
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       lowerCAmelCase__										=      self.get_image_processor()
       lowerCAmelCase__										=      self.get_tokenizer()

       lowerCAmelCase__										=      MgpstrProcessor(tokenizer=__lowerCamelCase       ,						image_processor=__lowerCamelCase						)

       lowerCAmelCase__										=      '''test'''
       lowerCAmelCase__										=      self.prepare_image_inputs()

       lowerCAmelCase__										=      processor(text=__lowerCamelCase       ,						images=__lowerCamelCase						)

       self.assertListEqual(list(inputs.keys()						)       ,						['''pixel_values''', '''labels''']						)

       # test if it raises when no input is passed
       with pytest.raises(__lowerCamelCase						):
         processor()
     def        A__      (						self	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      self.get_image_processor()
       lowerCAmelCase__										=      self.get_tokenizer()

       lowerCAmelCase__										=      MgpstrProcessor(tokenizer=__lowerCamelCase       ,						image_processor=__lowerCamelCase						)

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

       lowerCAmelCase__										=      processor.char_decode(__lowerCamelCase						)
       lowerCAmelCase__										=      tokenizer.batch_decode(__lowerCamelCase						)
       lowerCAmelCase__										=      [seq.replace(''' '''       ,						''''''						) for seq in decoded_tok]

       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       lowerCAmelCase__										=      self.get_image_processor()
       lowerCAmelCase__										=      self.get_tokenizer()

       lowerCAmelCase__										=      MgpstrProcessor(tokenizer=__lowerCamelCase       ,						image_processor=__lowerCamelCase						)

       lowerCAmelCase__										=      None
       lowerCAmelCase__										=      self.prepare_image_inputs()

       lowerCAmelCase__										=      processor(text=__lowerCamelCase       ,						images=__lowerCamelCase						)

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



     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.get_image_processor()
       lowerCAmelCase__										=      self.get_tokenizer()

       lowerCAmelCase__										=      MgpstrProcessor(tokenizer=__lowerCamelCase       ,						image_processor=__lowerCamelCase						)

       lowerCAmelCase__										=      torch.randn(1       ,						27       ,						38						)
       lowerCAmelCase__										=      torch.randn(1       ,						27       ,						5_02_57						)
       lowerCAmelCase__										=      torch.randn(1       ,						27       ,						3_05_22						)

       lowerCAmelCase__										=      processor.batch_decode([char_input, bpe_input, wp_input]						)

       self.assertListEqual(list(results.keys()						)       ,						['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']						)







import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__magic_name__  :  List[Any]					=       logging.get_logger(__name__)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      set()
  lowerCAmelCase__										=      []

  def parse_line(__lowerCAmelCase				):
    for line in fp:
      if isinstance(__lowerCAmelCase   , __lowerCAmelCase				):
        lowerCAmelCase__										=      line.decode('''UTF-8'''				)
      if "warnings summary (final)" in line:
        continue
      # This means we are outside the body of a warning
      elif not line.startswith(''' '''				):
        # process a single warning and move it to `selected_warnings`.
        if len(__lowerCAmelCase				) > 0:
          lowerCAmelCase__										=      '''\n'''.join(__lowerCAmelCase				)
          # Only keep the warnings specified in `targets`
          if any(F""": {x}: """ in warning for x in targets				):
            selected_warnings.add(__lowerCAmelCase				)
          buffer.clear()
        continue
      else:
        lowerCAmelCase__										=      line.strip()
        buffer.append(__lowerCAmelCase				)

  if from_gh:
    for filename in os.listdir(__lowerCAmelCase				):
      lowerCAmelCase__										=      os.path.join(__lowerCAmelCase   , __lowerCAmelCase				)
      if not os.path.isdir(__lowerCAmelCase				):
        # read the file
        if filename != "warnings.txt":
          continue
        with open(__lowerCAmelCase				) as fp:
          parse_line(__lowerCAmelCase				)
  else:
    try:
      with zipfile.ZipFile(__lowerCAmelCase				) as z:
        for filename in z.namelist():
          if not os.path.isdir(__lowerCAmelCase				):
            # read the file
            if filename != "warnings.txt":
              continue
            with z.open(__lowerCAmelCase				) as fp:
              parse_line(__lowerCAmelCase				)
    except Exception:
      logger.warning(
          F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""				)

  return selected_warnings
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      set()

  lowerCAmelCase__										=      [os.path.join(__lowerCAmelCase   , __lowerCAmelCase				) for p in os.listdir(__lowerCAmelCase				) if (p.endswith('''.zip'''				) or from_gh)]
  for p in paths:
    selected_warnings.update(extract_warnings_from_single_artifact(__lowerCAmelCase   , __lowerCAmelCase				)				)

  return selected_warnings


if __name__ == "__main__":
      def 		a_							( __lowerCAmelCase				):
        return values.split(''','''				)

      __magic_name__  :  Optional[int]					=       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
      parser.add_argument(
          """--output_dir""",
          type=str,
          required=True,
          help="""Where to store the downloaded artifacts and other result files.""",
      )
      parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
      # optional parameters
      parser.add_argument(
          """--targets""",
          default="""DeprecationWarning,UserWarning,FutureWarning""",
          type=list_str,
          help="""Comma-separated list of target warning(s) which we want to extract.""",
      )
      parser.add_argument(
          """--from_gh""",
          action="""store_true""",
          help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
      )

      __magic_name__  :  Optional[Any]					=       parser.parse_args()

      __magic_name__  :  Optional[int]					=       args.from_gh
      if from_gh:
            # The artifacts have to be downloaded using `actions/download-artifact@v3`
            pass
      else:
            os.makedirs(args.output_dir, exist_ok=True)

            # get download links
            __magic_name__  :  Optional[Any]					=       get_artifacts_links(args.workflow_run_id, token=args.token)
            with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
                  json.dump(artifacts, fp, ensure_ascii=False, indent=4)

            # download artifacts
            for idx, (name, url) in enumerate(artifacts.items()):
                  print(name)
                  print(url)
                  print("""=""" * 80)
                  download_artifact(name, url, args.output_dir, args.token)
                  # Be gentle to GitHub
                  time.sleep(1)

    # extract warnings from artifacts
      __magic_name__  :  Optional[Any]					=       extract_warnings(args.output_dir, args.targets)
      __magic_name__  :  Any					=       sorted(selected_warnings)
      with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
            json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)

