



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 ):
     lowerCAmelCase					=			ViTImageProcessor if is_vision_available() else None





     @property
     def 	__snake_case    (       self :							List[str]				)->Optional[int]:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 	__snake_case    (       self :							Optional[int]				)->Any:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	(3, 3_2, 1_2_8)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	tempfile.mkdtemp()

      # fmt: off
      __SCREAMING_SNAKE_CASE       :    str       =	["[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
      __SCREAMING_SNAKE_CASE       :    Any       =	dict(zip(UpperCamelCase_     ,  range(len(UpperCamelCase_				)				)				)				)

      __SCREAMING_SNAKE_CASE       :    Dict       =	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(UpperCamelCase_				) + "\n"				)

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





     def 	__snake_case    (       self :							Optional[int]     ,  **UpperCamelCase :							int				)->Tuple:
      return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,  **UpperCamelCase_				)





     def 	__snake_case    (       self :							Any     ,  **UpperCamelCase :							List[str]				)->Dict:
      return ViTImageProcessor.from_pretrained(self.tmpdirname     ,  **UpperCamelCase_				)





     def 	__snake_case    (       self :							int				)->Optional[int]:
      shutil.rmtree(self.tmpdirname				)





     def 	__snake_case    (       self :							Tuple				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	np.random.randint(2_5_5     ,  size=(3, 3_0, 4_0_0)     ,  dtype=np.uinta				)

      __SCREAMING_SNAKE_CASE       :    Any       =	Image.fromarray(np.moveaxis(UpperCamelCase_     ,  0     ,  -1				)				)

      return image_input





     def 	__snake_case    (       self :							str				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.get_tokenizer()
      __SCREAMING_SNAKE_CASE       :    Any       =	self.get_image_processor()

      __SCREAMING_SNAKE_CASE       :    Tuple       =	MgpstrProcessor(tokenizer=UpperCamelCase_     ,  image_processor=UpperCamelCase_				)
      processor.save_pretrained(self.tmpdirname				)
      __SCREAMING_SNAKE_CASE       :    int       =	MgpstrProcessor.from_pretrained(self.tmpdirname     ,  use_fast=UpperCamelCase_				)

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

      self.assertEqual(processor.image_processor.to_json_string()     ,  image_processor.to_json_string()				)
      self.assertIsInstance(processor.image_processor     ,  UpperCamelCase_				)





     def 	__snake_case    (       self :							List[str]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    int       =	self.get_tokenizer()
      __SCREAMING_SNAKE_CASE       :    str       =	self.get_image_processor()

      __SCREAMING_SNAKE_CASE       :    List[str]       =	MgpstrProcessor(tokenizer=UpperCamelCase_     ,  image_processor=UpperCamelCase_				)
      processor.save_pretrained(self.tmpdirname				)

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.get_tokenizer(bos_token="(BOS)"     ,  eos_token="(EOS)"				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.get_image_processor(do_normalize=UpperCamelCase_     ,  padding_value=1.0				)

      __SCREAMING_SNAKE_CASE       :    str       =	MgpstrProcessor.from_pretrained(
          self.tmpdirname     ,  bos_token="(BOS)"     ,  eos_token="(EOS)"     ,  do_normalize=UpperCamelCase_     ,  padding_value=1.0				)

      self.assertEqual(processor.char_tokenizer.get_vocab()     ,  tokenizer_add_kwargs.get_vocab()				)
      self.assertIsInstance(processor.char_tokenizer     ,  UpperCamelCase_				)

      self.assertEqual(processor.image_processor.to_json_string()     ,  image_processor_add_kwargs.to_json_string()				)
      self.assertIsInstance(processor.image_processor     ,  UpperCamelCase_				)





     def 	__snake_case    (       self :							str				)->str:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.get_image_processor()
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.get_tokenizer()

      __SCREAMING_SNAKE_CASE       :    List[str]       =	MgpstrProcessor(tokenizer=UpperCamelCase_     ,  image_processor=UpperCamelCase_				)

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

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	image_processor(UpperCamelCase_     ,  return_tensors="np"				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	processor(images=UpperCamelCase_     ,  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 	__snake_case    (       self :							Optional[int]				)->Any:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.get_image_processor()
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.get_tokenizer()

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	MgpstrProcessor(tokenizer=UpperCamelCase_     ,  image_processor=UpperCamelCase_				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	"test"

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	processor(text=UpperCamelCase_				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	tokenizer(UpperCamelCase_				)
      for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]     ,  encoded_processor[key]				)





     def 	__snake_case    (       self :							Dict				)->str:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.get_image_processor()
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.get_tokenizer()

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	MgpstrProcessor(tokenizer=UpperCamelCase_     ,  image_processor=UpperCamelCase_				)

      __SCREAMING_SNAKE_CASE       :    int       =	"test"
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	processor(text=UpperCamelCase_     ,  images=UpperCamelCase_				)

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

      # test if it raises when no input is passed
      with pytest.raises(UpperCamelCase_				):
       processor()





     def 	__snake_case    (       self :							Union[str, Any]				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.get_image_processor()
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.get_tokenizer()

      __SCREAMING_SNAKE_CASE       :    List[str]       =	MgpstrProcessor(tokenizer=UpperCamelCase_     ,  image_processor=UpperCamelCase_				)

      __SCREAMING_SNAKE_CASE       :    Tuple       =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

      __SCREAMING_SNAKE_CASE       :    Dict       =	processor.char_decode(UpperCamelCase_				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	tokenizer.batch_decode(UpperCamelCase_				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[seq.replace(" "     ,  ""				) for seq in decoded_tok]

      self.assertListEqual(UpperCamelCase_     ,  UpperCamelCase_				)





     def 	__snake_case    (       self :							List[str]				)->int:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.get_image_processor()
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.get_tokenizer()

      __SCREAMING_SNAKE_CASE       :    List[str]       =	MgpstrProcessor(tokenizer=UpperCamelCase_     ,  image_processor=UpperCamelCase_				)

      __SCREAMING_SNAKE_CASE       :    str       =	None
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE       :    Tuple       =	processor(text=UpperCamelCase_     ,  images=UpperCamelCase_				)

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






     def 	__snake_case    (       self :							Optional[int]				)->Any:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.get_image_processor()
      __SCREAMING_SNAKE_CASE       :    str       =	self.get_tokenizer()

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	MgpstrProcessor(tokenizer=UpperCamelCase_     ,  image_processor=UpperCamelCase_				)

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.randn(1     ,  2_7     ,  3_8				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	torch.randn(1     ,  2_7     ,  5_0_2_5_7				)
      __SCREAMING_SNAKE_CASE       :    str       =	torch.randn(1     ,  2_7     ,  3_0_5_2_2				)

      __SCREAMING_SNAKE_CASE       :    Tuple       =	processor.batch_decode([char_input, bpe_input, wp_input]				)

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










import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_lowerCamelCase						    =  importlib.util.find_spec("""s3fs""") is not None

if _has_safs:
      from .safilesystem import SaFileSystem  # noqa: F401

_lowerCamelCase						    =  [
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
      if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
            warnings.warn(f'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
      fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 if "://" in dataset_path:
  __SCREAMING_SNAKE_CASE       :    Any       =	dataset_path.split("://"     )[1]
 return dataset_path






def 	_lowerCAmelCase						(				__lowerCamelCase   :     fsspec.AbstractFileSystem     ):



 """simple docstring"""





 if fs is not None and fs.protocol != "file":
  return True
 else:
  return False






def 	_lowerCAmelCase						(				__lowerCamelCase   :     fsspec.AbstractFileSystem		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	not is_remote_filesystem(__lowerCamelCase     )
 if is_local:
  # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
  shutil.move(fs._strip_protocol(__lowerCamelCase     )		,	fs._strip_protocol(__lowerCamelCase     )     )
 else:
  fs.mv(__lowerCamelCase		,	__lowerCamelCase		,	recursive=__lowerCamelCase     )






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 if hasattr(fsspec.asyn		,	"reset_lock"     ):
  # for future fsspec>2022.05.0
  fsspec.asyn.reset_lock()
 else:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	None
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	None
  __SCREAMING_SNAKE_CASE       :    Dict       =	threading.Lock()






