


import gc
import threading
import time

import psutil
import torch






class   __lowercase				:





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






       """simple docstring"""







       UpperCAmelCase							=     psutil.Process()
       UpperCAmelCase							=     False





    def        _lowercase				(		self						:					Dict       )    ->   Any:






       """simple docstring"""







       UpperCAmelCase							=     -1

       while True:
          UpperCAmelCase							=     max(self.process.memory_info().rss					,       self.cpu_memory_peak       )

          # can't sleep or will not catch the peak right (this comment is here on purpose)
          if not self.peak_monitoring:
             break





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






       """simple docstring"""







       UpperCAmelCase							=     True
       UpperCAmelCase							=     threading.Thread(target=self.peak_monitor       )
       UpperCAmelCase							=     True
       self.thread.start()





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






       """simple docstring"""







       UpperCAmelCase							=     False
       self.thread.join()
       return self.cpu_memory_peak

__a     =			PeakCPUMemory()
def   _UpperCamelCase				(						)						->Any:
   # Time
   UpperCAmelCase							=     {"""time""": time.time()}

   gc.collect()
   torch.cuda.empty_cache()

   # CPU mem
   UpperCAmelCase							=     psutil.Process().memory_info().rss
   cpu_peak_tracker.start()

   # GPU mem
   for i in range(torch.cuda.device_count()   ):
      UpperCAmelCase							=     torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_   )
   torch.cuda.reset_peak_memory_stats()

   return measures
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[int]:
   # Time
   UpperCAmelCase							=     {"""time""": time.time() - start_measures["""time"""]}

   gc.collect()
   torch.cuda.empty_cache()

   # CPU mem
   UpperCAmelCase							=     (psutil.Process().memory_info().rss - start_measures["""cpu"""]) / 2**2_0
   UpperCAmelCase							=     (cpu_peak_tracker.stop() - start_measures["""cpu"""]) / 2**2_0

   # GPU mem
   for i in range(torch.cuda.device_count()   ):
      UpperCAmelCase							=     (torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_   ) - start_measures[str(SCREAMING_SNAKE_CASE_   )]) / 2**2_0
      UpperCAmelCase							=     (torch.cuda.max_memory_allocated(SCREAMING_SNAKE_CASE_   ) - start_measures[str(SCREAMING_SNAKE_CASE_   )]) / 2**2_0

   return measures







def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Tuple:
   print(F"""{description}:"""   )
   print(F"""- Time: {measures['time']:.2f}s"""   )
   for i in range(torch.cuda.device_count()   ):
      print(F"""- GPU {i} allocated: {measures[str(SCREAMING_SNAKE_CASE_   )]:.2f}MiB"""   )
      UpperCAmelCase							=     measures[F"""{i}-peak"""]
      print(F"""- GPU {i} peak: {peak:.2f}MiB"""   )
   print(F"""- CPU RAM allocated: {measures['cpu']:.2f}MiB"""   )
   print(F"""- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"""   )







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   __lowercase				(  unittest.TestCase  ):
      UpperCamelCase								=       ViTImageProcessor if is_vision_available() else None





      @property
      def        _lowercase				(		self						:					Tuple       )    ->   int:






         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





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






         """simple docstring"""







         UpperCAmelCase							=     (3, 3_2, 1_2_8)
         UpperCAmelCase							=     tempfile.mkdtemp()

         # fmt: off
         UpperCAmelCase							=     ["""[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
         UpperCAmelCase							=     dict(zip(__lowerCamelCase					,       range(len(__lowerCamelCase       )       )       )       )

         UpperCAmelCase							=     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"""       )

         UpperCAmelCase							=     {
             """do_normalize""": False,
             """do_resize""": True,
             """image_processor_type""": """ViTImageProcessor""",
             """resample""": 3,
             """size""": {"""height""": 3_2, """width""": 1_2_8},
         }
         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       __lowerCamelCase       )
         with open(self.image_processor_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            json.dump(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]					,       **__lowerCamelCase						:					Union[str, Any]       )    ->   int:






         """simple docstring"""







         return MgpstrTokenizer.from_pretrained(self.tmpdirname					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					int					,       **__lowerCamelCase						:					Optional[int]       )    ->   Optional[int]:






         """simple docstring"""







         return ViTImageProcessor.from_pretrained(self.tmpdirname					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Dict:






         """simple docstring"""







         shutil.rmtree(self.tmpdirname       )





      def        _lowercase				(		self						:					Any       )    ->   str:






         """simple docstring"""







         UpperCAmelCase							=     np.random.randint(2_5_5					,       size=(3, 3_0, 4_0_0)					,       dtype=np.uinta       )

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

         return image_input





      def        _lowercase				(		self						:					Tuple       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_tokenizer()
         UpperCAmelCase							=     self.get_image_processor()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor.save_pretrained(self.tmpdirname       )
         UpperCAmelCase							=     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        _lowercase				(		self						:					int       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_tokenizer()
         UpperCAmelCase							=     self.get_image_processor()

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

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

         UpperCAmelCase							=     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        _lowercase				(		self						:					int       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

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

         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     image_processor(__lowerCamelCase					,       return_tensors="""np"""       )
         UpperCAmelCase							=     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        _lowercase				(		self						:					str       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

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

         UpperCAmelCase							=     """test"""

         UpperCAmelCase							=     processor(text=__lowerCamelCase       )

         UpperCAmelCase							=     tokenizer(__lowerCamelCase       )
         for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]					,       encoded_processor[key]       )





      def        _lowercase				(		self						:					Optional[int]       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

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

         UpperCAmelCase							=     """test"""
         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     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        _lowercase				(		self						:					str       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

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

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

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

         self.assertListEqual(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

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

         UpperCAmelCase							=     None
         UpperCAmelCase							=     self.prepare_image_inputs()

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

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





      def        _lowercase				(		self						:					Dict       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

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

         UpperCAmelCase							=     torch.randn(1					,       2_7					,       3_8       )
         UpperCAmelCase							=     torch.randn(1					,       2_7					,       5_0_2_5_7       )
         UpperCAmelCase							=     torch.randn(1					,       2_7					,       3_0_5_2_2       )

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

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



