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   lowerCamelCase					(unittest.TestCase					):
		"""simple docstring"""







		lowerCamelCase__     =						ViTImageProcessor if is_vision_available() else None







		@property
		def    __A     (							self :       Dict   )  ->	str:
								return self.image_processor_tester.prepare_image_processor_dict()







		def    __A     (							self :       List[str]   )  ->	str:
								SCREAMING_SNAKE_CASE_          =  (3, 32, 128)
								SCREAMING_SNAKE_CASE_          =  tempfile.mkdtemp()

								# fmt: off
								SCREAMING_SNAKE_CASE_          =  ["[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_          =  dict(zip(__magic_name__  ,     range(len(__magic_name__   )   )   )   )

								SCREAMING_SNAKE_CASE_          =  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(__magic_name__   ) + "\n"   )

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







		def    __A     (							self :       int  ,     **__magic_name__ :       List[str]   )  ->	Dict:
								return MgpstrTokenizer.from_pretrained(self.tmpdirname  ,     **__magic_name__   )







		def    __A     (							self :       List[str]  ,     **__magic_name__ :       Optional[Any]   )  ->	Dict:
								return ViTImageProcessor.from_pretrained(self.tmpdirname  ,     **__magic_name__   )







		def    __A     (							self :       str   )  ->	Any:
								shutil.rmtree(self.tmpdirname   )







		def    __A     (							self :       Tuple   )  ->	Dict:
								SCREAMING_SNAKE_CASE_          =  np.random.randint(255  ,     size=(3, 30, 400)  ,     dtype=np.uinta   )

								SCREAMING_SNAKE_CASE_          =  Image.fromarray(np.moveaxis(__magic_name__  ,     0  ,     -1   )   )

								return image_input







		def    __A     (							self :       Any   )  ->	str:
								SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()
								SCREAMING_SNAKE_CASE_          =  self.get_image_processor()

								SCREAMING_SNAKE_CASE_          =  MgpstrProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )
								processor.save_pretrained(self.tmpdirname   )
								SCREAMING_SNAKE_CASE_          =  MgpstrProcessor.from_pretrained(self.tmpdirname  ,     use_fast=__magic_name__   )

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

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







		def    __A     (							self :       Dict   )  ->	Tuple:
								SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()
								SCREAMING_SNAKE_CASE_          =  self.get_image_processor()

								SCREAMING_SNAKE_CASE_          =  MgpstrProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )
								processor.save_pretrained(self.tmpdirname   )

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

								SCREAMING_SNAKE_CASE_          =  MgpstrProcessor.from_pretrained(
								    self.tmpdirname  ,     bos_token="(BOS)"  ,     eos_token="(EOS)"  ,     do_normalize=__magic_name__  ,     padding_value=1.0   )

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

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







		def    __A     (							self :       List[str]   )  ->	Union[str, Any]:
								SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
								SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

								SCREAMING_SNAKE_CASE_          =  MgpstrProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

								SCREAMING_SNAKE_CASE_          =  self.prepare_image_inputs()

								SCREAMING_SNAKE_CASE_          =  image_processor(__magic_name__  ,     return_tensors="np"   )
								SCREAMING_SNAKE_CASE_          =  processor(images=__magic_name__  ,     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    __A     (							self :       Optional[Any]   )  ->	Tuple:
								SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
								SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

								SCREAMING_SNAKE_CASE_          =  MgpstrProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

								SCREAMING_SNAKE_CASE_          =  "test"

								SCREAMING_SNAKE_CASE_          =  processor(text=__magic_name__   )

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







		def    __A     (							self :       List[str]   )  ->	Union[str, Any]:
								SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
								SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

								SCREAMING_SNAKE_CASE_          =  MgpstrProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

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

								SCREAMING_SNAKE_CASE_          =  processor(text=__magic_name__  ,     images=__magic_name__   )

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

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







		def    __A     (							self :       List[Any]   )  ->	int:
								SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
								SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

								SCREAMING_SNAKE_CASE_          =  MgpstrProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

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

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

								self.assertListEqual(__magic_name__  ,     __magic_name__   )







		def    __A     (							self :       List[Any]   )  ->	str:
								SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
								SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

								SCREAMING_SNAKE_CASE_          =  MgpstrProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

								SCREAMING_SNAKE_CASE_          =  None
								SCREAMING_SNAKE_CASE_          =  self.prepare_image_inputs()

								SCREAMING_SNAKE_CASE_          =  processor(text=__magic_name__  ,     images=__magic_name__   )

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







		def    __A     (							self :       str   )  ->	Any:
								SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
								SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

								SCREAMING_SNAKE_CASE_          =  MgpstrProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

								SCREAMING_SNAKE_CASE_          =  torch.randn(1  ,     27  ,     38   )
								SCREAMING_SNAKE_CASE_          =  torch.randn(1  ,     27  ,     50_257   )
								SCREAMING_SNAKE_CASE_          =  torch.randn(1  ,     27  ,     30_522   )

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

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



import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


A     :  Any					     =       logging.getLogger(__name__)


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       List[Any]  ,     __magic_name__ :       Optional[Any]=-1   )  ->	Optional[Any]:
        # in NER datasets, the last column is usually reserved for NER label
        SCREAMING_SNAKE_CASE_          =  label_idx







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Union[Split, str]   )  ->	List[InputExample]:
        if isinstance(__magic_name__  ,     __magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  mode.value
        SCREAMING_SNAKE_CASE_          =  os.path.join(__magic_name__  ,     F'''{mode}.txt'''   )
        SCREAMING_SNAKE_CASE_          =  1
        SCREAMING_SNAKE_CASE_          =  []
        with open(__magic_name__  ,     encoding="utf-8"   ) as f:
              SCREAMING_SNAKE_CASE_          =  []
              SCREAMING_SNAKE_CASE_          =  []
              for line in f:
                    if line.startswith("-DOCSTART-"   ) or line == "" or line == "\n":
                          if words:
                                examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,     words=__magic_name__  ,     labels=__magic_name__   )   )
                                guid_index += 1
                                SCREAMING_SNAKE_CASE_          =  []
                                SCREAMING_SNAKE_CASE_          =  []
                    else:
                          SCREAMING_SNAKE_CASE_          =  line.split(" "   )
                          words.append(splits[0]   )
                          if len(__magic_name__   ) > 1:
                                labels.append(splits[self.label_idx].replace("\n"  ,     ""   )   )
                          else:
                                # Examples could have no label for mode = "test"
                                labels.append("O"   )
              if words:
                    examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,     words=__magic_name__  ,     labels=__magic_name__   )   )
        return examples







  def    __A     (							self :       Tuple  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       List   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  0
        for line in test_input_reader:
              if line.startswith("-DOCSTART-"   ) or line == "" or line == "\n":
                    writer.write(__magic_name__   )
                    if not preds_list[example_id]:
                          example_id += 1
              elif preds_list[example_id]:
                    SCREAMING_SNAKE_CASE_          =  line.split()[0] + " " + preds_list[example_id].pop(0   ) + "\n"
                    writer.write(__magic_name__   )
              else:
                    logger.warning("Maximum sequence length exceeded: No prediction for '%s'."  ,     line.split()[0]   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "r"   ) as f:
                    SCREAMING_SNAKE_CASE_          =  f.read().splitlines()
              if "O" not in labels:
                    SCREAMING_SNAKE_CASE_          =  ["O"] + labels
              return labels
        else:
              return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       Optional[int]   )  ->	str:
        # in CONLL2003 dataset chunk column is second-to-last
        super().__init__(label_idx=-2   )







  def    __A     (							self :       Any  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "r"   ) as f:
                    SCREAMING_SNAKE_CASE_          =  f.read().splitlines()
              if "O" not in labels:
                    SCREAMING_SNAKE_CASE_          =  ["O"] + labels
              return labels
        else:
              return [
                  "O",
                  "B-ADVP",
                  "B-INTJ",
                  "B-LST",
                  "B-PRT",
                  "B-NP",
                  "B-SBAR",
                  "B-VP",
                  "B-ADJP",
                  "B-CONJP",
                  "B-PP",
                  "I-ADVP",
                  "I-INTJ",
                  "I-LST",
                  "I-PRT",
                  "I-NP",
                  "I-SBAR",
                  "I-VP",
                  "I-ADJP",
                  "I-CONJP",
                  "I-PP",
              ]



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def    __A     (							self :       str  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Union[Split, str]   )  ->	List[InputExample]:
        if isinstance(__magic_name__  ,     __magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  mode.value
        SCREAMING_SNAKE_CASE_          =  os.path.join(__magic_name__  ,     F'''{mode}.txt'''   )
        SCREAMING_SNAKE_CASE_          =  1
        SCREAMING_SNAKE_CASE_          =  []

        with open(__magic_name__  ,     encoding="utf-8"   ) as f:
              for sentence in parse_incr(__magic_name__   ):
                    SCREAMING_SNAKE_CASE_          =  []
                    SCREAMING_SNAKE_CASE_          =  []
                    for token in sentence:
                          words.append(token["form"]   )
                          labels.append(token["upos"]   )
                    assert len(__magic_name__   ) == len(__magic_name__   )
                    if words:
                          examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,     words=__magic_name__  ,     labels=__magic_name__   )   )
                          guid_index += 1
        return examples







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       List   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  0
        for sentence in parse_incr(__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  preds_list[example_id]
              SCREAMING_SNAKE_CASE_          =  ""
              for token in sentence:
                    out += F'''{token["form"]} ({token["upos"]}|{s_p.pop(0   )}) '''
              out += "\n"
              writer.write(__magic_name__   )
              example_id += 1







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "r"   ) as f:
                    return f.read().splitlines()
        else:
              return [
                  "ADJ",
                  "ADP",
                  "ADV",
                  "AUX",
                  "CCONJ",
                  "DET",
                  "INTJ",
                  "NOUN",
                  "NUM",
                  "PART",
                  "PRON",
                  "PROPN",
                  "PUNCT",
                  "SCONJ",
                  "SYM",
                  "VERB",
                  "X",
              ]


