


"""simple docstring"""
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 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      return self.image_processor_tester.prepare_image_processor_dict()





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					List[str]   =		(3, 32, 128)
      _lowerCamelCase	:					List[Any]   =		tempfile.mkdtemp()

      # fmt: off
      _lowerCamelCase	:					Optional[int]   =		["""[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   =		dict(zip(SCREAMING_SNAKE_CASE      ,   range(len(SCREAMING_SNAKE_CASE))))

      _lowerCamelCase	:					int   =		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(SCREAMING_SNAKE_CASE) + """\n""")

      _lowerCamelCase	:					Any   =		{
          """do_normalize""": False,
          """do_resize""": True,
          """image_processor_type""": """ViTImageProcessor""",
          """resample""": 3,
          """size""": {"""height""": 32, """width""": 128},
      }
      _lowerCamelCase	:					Union[str, Any]   =		os.path.join(self.tmpdirname      ,   SCREAMING_SNAKE_CASE)
      with open(self.image_processor_file      ,   """w"""      ,   encoding="""utf-8""") as fp:
         json.dump(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  Union[str, Any]:
      return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  str:
      return ViTImageProcessor.from_pretrained(self.tmpdirname      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      shutil.rmtree(self.tmpdirname)





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Optional[int]   =		np.random.randint(255      ,   size=(3, 30, 400)      ,   dtype=np.uinta)

      _lowerCamelCase	:					Union[str, Any]   =		Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE      ,   0      ,   -1))

      return image_input





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					Optional[Any]   =		self.get_tokenizer()
      _lowerCamelCase	:					Dict   =		self.get_image_processor()

      _lowerCamelCase	:					Tuple   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)
      processor.save_pretrained(self.tmpdirname)
      _lowerCamelCase	:					Dict   =		MgpstrProcessor.from_pretrained(self.tmpdirname      ,   use_fast=SCREAMING_SNAKE_CASE)

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

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





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Optional[int]   =		self.get_tokenizer()
      _lowerCamelCase	:					Any   =		self.get_image_processor()

      _lowerCamelCase	:					List[Any]   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)
      processor.save_pretrained(self.tmpdirname)

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

      _lowerCamelCase	:					Any   =		MgpstrProcessor.from_pretrained(
          self.tmpdirname      ,   bos_token="""(BOS)"""      ,   eos_token="""(EOS)"""      ,   do_normalize=SCREAMING_SNAKE_CASE      ,   padding_value=1.0)

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

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





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Optional[int]   =		self.get_image_processor()
      _lowerCamelCase	:					Union[str, Any]   =		self.get_tokenizer()

      _lowerCamelCase	:					Optional[int]   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[Any]   =		self.prepare_image_inputs()

      _lowerCamelCase	:					Optional[Any]   =		image_processor(SCREAMING_SNAKE_CASE      ,   return_tensors="""np""")
      _lowerCamelCase	:					Tuple   =		processor(images=SCREAMING_SNAKE_CASE      ,   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 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					Dict   =		self.get_image_processor()
      _lowerCamelCase	:					str   =		self.get_tokenizer()

      _lowerCamelCase	:					Dict   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		"""test"""

      _lowerCamelCase	:					Union[str, Any]   =		processor(text=SCREAMING_SNAKE_CASE)

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





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Optional[int]   =		self.get_image_processor()
      _lowerCamelCase	:					Optional[int]   =		self.get_tokenizer()

      _lowerCamelCase	:					Any   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[str]   =		"""test"""
      _lowerCamelCase	:					Dict   =		self.prepare_image_inputs()

      _lowerCamelCase	:					Tuple   =		processor(text=SCREAMING_SNAKE_CASE      ,   images=SCREAMING_SNAKE_CASE)

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

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





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Tuple   =		self.get_image_processor()
      _lowerCamelCase	:					Union[str, Any]   =		self.get_tokenizer()

      _lowerCamelCase	:					Union[str, Any]   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[Any]   =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

      _lowerCamelCase	:					Dict   =		processor.char_decode(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[int]   =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[int]   =		[seq.replace(""" """      ,   """""") for seq in decoded_tok]

      self.assertListEqual(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Optional[int]   =		self.get_image_processor()
      _lowerCamelCase	:					Dict   =		self.get_tokenizer()

      _lowerCamelCase	:					int   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					str   =		None
      _lowerCamelCase	:					List[str]   =		self.prepare_image_inputs()

      _lowerCamelCase	:					List[Any]   =		processor(text=SCREAMING_SNAKE_CASE      ,   images=SCREAMING_SNAKE_CASE)

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





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Optional[Any]   =		self.get_image_processor()
      _lowerCamelCase	:					Optional[Any]   =		self.get_tokenizer()

      _lowerCamelCase	:					int   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		torch.randn(1      ,   27      ,   38)
      _lowerCamelCase	:					int   =		torch.randn(1      ,   27      ,   5_0257)
      _lowerCamelCase	:					Dict   =		torch.randn(1      ,   27      ,   3_0522)

      _lowerCamelCase	:					Union[str, Any]   =		processor.batch_decode([char_input, bpe_input, wp_input])

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





"""simple docstring"""
from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


UpperCAmelCase				=						"""\
@misc{wu2016googles,
      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

UpperCAmelCase				=						"""\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""

UpperCAmelCase				=						"""\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    'google_bleu': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.44

    Example 2:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.61

    Example 3:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results[\"google_bleu\"], 2))
        0.53

    Example 4:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results[\"google_bleu\"], 2))
        0.4
"""




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION				)
class 	lowercase__				(					datasets.Metric				):





   def 		UpperCamelCase_  (							self)  ->  MetricInfo:
      return datasets.MetricInfo(
          description=_DESCRIPTION      ,   citation=_CITATION      ,   inputs_description=_KWARGS_DESCRIPTION      ,   features=datasets.Features(
              {
                  """predictions""": datasets.Sequence(datasets.Value("""string"""      ,   id="""token""")      ,   id="""sequence"""),
                  """references""": datasets.Sequence(
                      datasets.Sequence(datasets.Value("""string"""      ,   id="""token""")      ,   id="""sequence""")      ,   id="""references"""),
              })      ,   )





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = 1      ,   SCREAMING_SNAKE_CASE = 4      ,   )  ->  Dict[str, float]:
      return {
          "google_bleu": gleu_score.corpus_gleu(
              list_of_references=SCREAMING_SNAKE_CASE      ,   hypotheses=SCREAMING_SNAKE_CASE      ,   min_len=SCREAMING_SNAKE_CASE      ,   max_len=SCREAMING_SNAKE_CASE)
      }

