





import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


A_												=     '''\
    Text data.
    Second line of data.'''

A_												=     '''file'''





@pytest.fixture(scope="session")
def    __UpperCamelCase      (    a) ->Union[str, Any]:
     lowerCamelCase__       =       tmp_path_factory.mktemp("data") / (FILE_PATH + ".zstd")
     lowerCamelCase__       =       bytes(lowerCAmelCase__,     "utf-8")
     with zstd.open(lowerCAmelCase__,     "wb") as f:
          f.write(lowerCAmelCase__)
     return path





@pytest.fixture
def    __UpperCamelCase      (    a) ->str:
     with open(os.path.join(tmpfs.local_root_dir,     lowerCAmelCase__),     "w") as f:
          f.write(lowerCAmelCase__)
     return FILE_PATH





@pytest.mark.parametrize("compression_format",     ["gzip", "xz", "zstd"])
def    __UpperCamelCase      (    a,     a,     a,     a,     a,     a) ->Dict:
     lowerCamelCase__       =       {"gzip": gz_file, "xz": xz_file, "zstd": zstd_path}
     lowerCamelCase__       =       input_paths[compression_format]
     lowerCamelCase__       =       tmp_path / "cache"
     lowerCamelCase__       =       DownloadConfig(cache_dir=lowerCAmelCase__,     extract_compressed_file=lowerCAmelCase__)
     lowerCamelCase__       =       cached_path(lowerCAmelCase__,     download_config=lowerCAmelCase__)
     with open(lowerCAmelCase__) as f:
          lowerCamelCase__       =       f.read()
     with open(lowerCAmelCase__) as f:
          lowerCamelCase__       =       f.read()
     assert extracted_file_content == expected_file_content





@pytest.mark.parametrize("default_extracted",     [True, False])
@pytest.mark.parametrize("default_cache_dir",     [True, False])
def    __UpperCamelCase      (    a,     a,     a,     a,     a) ->Tuple:
     lowerCamelCase__       =       "custom_cache"
     lowerCamelCase__       =       "custom_extracted_dir"
     lowerCamelCase__       =       tmp_path / "custom_extracted_path"
     if default_extracted:
          lowerCamelCase__       =       ("downloads" if default_cache_dir else custom_cache_dir, "extracted")
     else:
          monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_DIR",     lowerCAmelCase__)
          monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH",     str(lowerCAmelCase__))
          lowerCamelCase__       =       custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

     lowerCamelCase__       =       xz_file
     lowerCamelCase__       =       (
         DownloadConfig(extract_compressed_file=lowerCAmelCase__)
         if default_cache_dir
         else DownloadConfig(cache_dir=tmp_path / custom_cache_dir,     extract_compressed_file=lowerCAmelCase__)
     )
     lowerCamelCase__       =       cached_path(lowerCAmelCase__,     download_config=lowerCAmelCase__)
     assert Path(lowerCAmelCase__).parent.parts[-2:] == expected





def    __UpperCamelCase      (    a) ->Any:
     # absolute path
     lowerCamelCase__       =       str(Path(lowerCAmelCase__).resolve())
     assert cached_path(lowerCAmelCase__) == text_file
     # relative path
     lowerCamelCase__       =       str(Path(lowerCAmelCase__).resolve().relative_to(Path(os.getcwd())))
     assert cached_path(lowerCAmelCase__) == text_file





def    __UpperCamelCase      (    a) ->Tuple:
     # absolute path
     lowerCamelCase__       =       str(tmp_path.resolve() / "__missing_file__.txt")
     with pytest.raises(lowerCAmelCase__):
          cached_path(lowerCAmelCase__)
     # relative path
     lowerCamelCase__       =       "./__missing_file__.txt"
     with pytest.raises(lowerCAmelCase__):
          cached_path(lowerCAmelCase__)





def    __UpperCamelCase      (    a) ->Tuple:
     lowerCamelCase__       =       get_from_cache(f"tmp://{tmpfs_file}")
     with open(lowerCAmelCase__) as f:
          lowerCamelCase__       =       f.read()
     assert output_file_content == FILE_CONTENT





@patch("datasets.config.HF_DATASETS_OFFLINE",     lowerCAmelCase__)
def    __UpperCamelCase      (    ) ->Any:
     with pytest.raises(lowerCAmelCase__):
          cached_path("https://huggingface.co")





@patch("datasets.config.HF_DATASETS_OFFLINE",     lowerCAmelCase__)
def    __UpperCamelCase      (    a) ->List[Any]:
     lowerCamelCase__       =       tmp_path_factory.mktemp("data") / "file.html"
     with pytest.raises(lowerCAmelCase__):
          http_get("https://huggingface.co",     temp_file=lowerCAmelCase__)
     with pytest.raises(lowerCAmelCase__):
          http_head("https://huggingface.co")





@patch("datasets.config.HF_DATASETS_OFFLINE",     lowerCAmelCase__)
def    __UpperCamelCase      (    a) ->List[str]:
     lowerCamelCase__       =       tmp_path_factory.mktemp("data") / "file.html"
     with pytest.raises(lowerCAmelCase__):
          ftp_get("ftp://huggingface.co",     temp_file=lowerCAmelCase__)
     with pytest.raises(lowerCAmelCase__):
          ftp_head("ftp://huggingface.co")







@patch("datasets.config.HF_DATASETS_OFFLINE",     lowerCAmelCase__)
def    __UpperCamelCase      (    a) ->Union[str, Any]:
     lowerCamelCase__       =       tmp_path_factory.mktemp("data") / "file.html"
     with pytest.raises(lowerCAmelCase__):
          fsspec_get("s3://huggingface.co",     temp_file=lowerCAmelCase__)
     with pytest.raises(lowerCAmelCase__):
          fsspec_head("s3://huggingface.co")








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   ):





					"""simple docstring"""


					A__				 =    ViTImageProcessor if is_vision_available() else None





					@property
					def 					__magic_name__ ( self	):
										return self.image_processor_tester.prepare_image_processor_dict()





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       (3, 32, 128)
										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": 128},
										}
										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  _lowerCAmelCase	)
										with open(self.image_processor_file	,  "w"	,  encoding="utf-8"	) as fp:
															json.dump(_lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return MgpstrTokenizer.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return ViTImageProcessor.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										shutil.rmtree(self.tmpdirname	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       np.random.randint(255	,  size=(3, 30, 400)	,  dtype=np.uinta	)

										lowerCamelCase__       =       Image.fromarray(np.moveaxis(_lowerCAmelCase	,  0	,  -1	)	)

										return image_input





					def 					__magic_name__ ( self	):
										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 					__magic_name__ ( self	):
										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 					__magic_name__ ( self	):
										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 					__magic_name__ ( self	):
										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 					__magic_name__ ( self	):
										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 					__magic_name__ ( self	):
										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 					__magic_name__ ( self	):
										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 					__magic_name__ ( self	):
										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_0257	)
										lowerCamelCase__       =       torch.randn(1	,  27	,  3_0522	)

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

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

