




'''simple docstring'''







import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
      import torch

if is_soundfile_availble():
      import soundfile as sf

if is_vision_available():
      from PIL import Image



def        lowerCamelCase__				(      __lowercase=""				):
     snake_case			:      Union[str, Any]        =			tempfile.mkdtemp()
     return os.path.join(__lowercase     ,   str(uuid.uuida()				) + suffix				)







@require_soundfile
@require_torch
class 					_a (unittest.TestCase	):





     '''simple docstring'''



     def 			snake_case_      (				self							)      ->				int:
          snake_case			:      Optional[Any]        =			torch.rand(12							,dtype=torch.floataa							) - 0.5
          snake_case			:      Tuple        =			AgentAudio(__a							)
          snake_case			:      int        =			str(agent_type.to_string()							)

          # Ensure that the tensor and the agent_type's tensor are the same
          self.assertTrue(torch.allclose(__a							,agent_type.to_raw()							,atol=1E-4							)							)

          del agent_type

          # Ensure the path remains even after the object deletion
          self.assertTrue(os.path.exists(__a							)							)

          # Ensure that the file contains the same value as the original tensor
          snake_case ,		snake_case			:      str        =			sf.read(__a							)
          self.assertTrue(torch.allclose(__a							,torch.tensor(__a							)							,atol=1E-4							)							)







     def 			snake_case_      (				self							)      ->				Dict:
          snake_case			:      Dict        =			torch.rand(12							,dtype=torch.floataa							) - 0.5
          snake_case			:      int        =			get_new_path(suffix=""".wav"""							)
          sf.write(__a							,__a							,16_000							)

          snake_case			:      Any        =			AgentAudio(__a							)

          self.assertTrue(torch.allclose(__a							,agent_type.to_raw()							,atol=1E-4							)							)
          self.assertEqual(agent_type.to_string()							,__a							)







@require_vision
@require_torch
class 					_a (unittest.TestCase	):





     '''simple docstring'''



     def 			snake_case_      (				self							)      ->				Dict:
          snake_case			:      List[Any]        =			torch.randint(0							,256							,(64, 64, 3)							)
          snake_case			:      List[Any]        =			AgentImage(__a							)
          snake_case			:      List[str]        =			str(agent_type.to_string()							)

          # Ensure that the tensor and the agent_type's tensor are the same
          self.assertTrue(torch.allclose(__a							,agent_type._tensor							,atol=1E-4							)							)

          self.assertIsInstance(agent_type.to_raw()							,Image.Image							)

          # Ensure the path remains even after the object deletion
          del agent_type
          self.assertTrue(os.path.exists(__a							)							)







     def 			snake_case_      (				self							)      ->				Tuple:
          snake_case			:      Any        =			Path(get_tests_dir("""fixtures/tests_samples/COCO"""							)							) / """000000039769.png"""
          snake_case			:      Union[str, Any]        =			Image.open(__a							)
          snake_case			:      Any        =			AgentImage(__a							)

          self.assertTrue(path.samefile(agent_type.to_string()							)							)
          self.assertTrue(image == agent_type.to_raw()							)

          # Ensure the path remains even after the object deletion
          del agent_type
          self.assertTrue(os.path.exists(__a							)							)







     def 			snake_case_      (				self							)      ->				List[Any]:
          snake_case			:      Optional[int]        =			Path(get_tests_dir("""fixtures/tests_samples/COCO"""							)							) / """000000039769.png"""
          snake_case			:      Union[str, Any]        =			Image.open(__a							)
          snake_case			:      List[Any]        =			AgentImage(__a							)

          self.assertFalse(path.samefile(agent_type.to_string()							)							)
          self.assertTrue(image == agent_type.to_raw()							)

          # Ensure the path remains even after the object deletion
          del agent_type
          self.assertTrue(os.path.exists(__a							)							)







class 					_a (unittest.TestCase	):





     '''simple docstring'''



     def 			snake_case_      (				self							)      ->				Any:
          snake_case			:      str        =			"""Hey!"""
          snake_case			:      Union[str, Any]        =			AgentText(__a							)

          self.assertEqual(__a							,agent_type.to_string()							)
          self.assertEqual(__a							,agent_type.to_raw()							)
          self.assertEqual(__a							,__a							)









'''simple docstring'''







import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase      :   Any      =       logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase      :   List[Any]      =       argparse.ArgumentParser(
          description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
      )
      parser.add_argument(
          """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
      )
      parser.add_argument(
          """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
      )
      parser.add_argument("""--vocab_size""", default=3_0522, type=int)
      lowercase      :   str      =       parser.parse_args()

      logger.info(F"""Loading data from {args.data_file}""")
      with open(args.data_file, """rb""") as fp:
            lowercase      :   int      =       pickle.load(fp)

      logger.info("""Counting occurrences for MLM.""")
      lowercase      :   List[Any]      =       Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase      :   int      =       [0] * args.vocab_size
      for k, v in counter.items():
            lowercase      :   List[Any]      =       v

      logger.info(F"""Dump to {args.token_counts_dump}""")
      with open(args.token_counts_dump, """wb""") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)



