
"""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 		a__						(      unittest.TestCase						):
   __lowerCAmelCase            =				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				):
          lowercase    :				Tuple       		=							(3, 32, 128)
          lowercase    :				List[Any]       		=							tempfile.mkdtemp()

          # fmt: off
          lowercase    :				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
          lowercase    :				List[str]       		=							dict(zip(_a	,    range(len(_a				)				)				)				)

          lowercase    :				Optional[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(_a				) + "\n"				)

          lowercase    :				List[str]       		=							{
              "do_normalize": False,
              "do_resize": True,
              "image_processor_type": "ViTImageProcessor",
              "resample": 3,
              "size": {"height": 32, "width": 128},
          }
          lowercase    :				List[str]       		=							os.path.join(self.tmpdirname	,    _a				)
          with open(self.image_processor_file	,    "w"	,    encoding="utf-8"				) as fp:
                 json.dump(_a	,    _a				)
   def 			__magic_name__			(			self	,    **_a				):
          return MgpstrTokenizer.from_pretrained(self.tmpdirname	,    **_a				)
   def 			__magic_name__			(			self	,    **_a				):
          return ViTImageProcessor.from_pretrained(self.tmpdirname	,    **_a				)
   def 			__magic_name__			(			self				):
          shutil.rmtree(self.tmpdirname				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta				)

          lowercase    :				Any       		=							Image.fromarray(np.moveaxis(_a	,    0	,    -1				)				)

          return image_input
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							self.get_tokenizer()
          lowercase    :				Dict       		=							self.get_image_processor()

          lowercase    :				Optional[int]       		=							MgpstrProcessor(tokenizer=_a	,    image_processor=_a				)
          processor.save_pretrained(self.tmpdirname				)
          lowercase    :				List[Any]       		=							MgpstrProcessor.from_pretrained(self.tmpdirname	,    use_fast=_a				)

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

          self.assertEqual(processor.image_processor.to_json_string()	,    image_processor.to_json_string()				)
          self.assertIsInstance(processor.image_processor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							self.get_tokenizer()
          lowercase    :				str       		=							self.get_image_processor()

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

          lowercase    :				Optional[int]       		=							self.get_tokenizer(bos_token="(BOS)"	,    eos_token="(EOS)"				)
          lowercase    :				str       		=							self.get_image_processor(do_normalize=_a	,    padding_value=1.0				)

          lowercase    :				Tuple       		=							MgpstrProcessor.from_pretrained(
              self.tmpdirname	,    bos_token="(BOS)"	,    eos_token="(EOS)"	,    do_normalize=_a	,    padding_value=1.0				)

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

          self.assertEqual(processor.image_processor.to_json_string()	,    image_processor_add_kwargs.to_json_string()				)
          self.assertIsInstance(processor.image_processor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							self.get_image_processor()
          lowercase    :				Tuple       		=							self.get_tokenizer()

          lowercase    :				Dict       		=							MgpstrProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				int       		=							self.prepare_image_inputs()

          lowercase    :				List[Any]       		=							image_processor(_a	,    return_tensors="np"				)
          lowercase    :				List[str]       		=							processor(images=_a	,    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				):
          lowercase    :				Dict       		=							self.get_image_processor()
          lowercase    :				Union[str, Any]       		=							self.get_tokenizer()

          lowercase    :				List[Any]       		=							MgpstrProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				Any       		=							"test"

          lowercase    :				str       		=							processor(text=_a				)

          lowercase    :				int       		=							tokenizer(_a				)
          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]				)
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.get_image_processor()
          lowercase    :				Optional[Any]       		=							self.get_tokenizer()

          lowercase    :				Tuple       		=							MgpstrProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				Any       		=							"test"
          lowercase    :				Optional[int]       		=							self.prepare_image_inputs()

          lowercase    :				Union[str, Any]       		=							processor(text=_a	,    images=_a				)

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

          # test if it raises when no input is passed
          with pytest.raises(_a				):
                 processor()
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.get_image_processor()
          lowercase    :				int       		=							self.get_tokenizer()

          lowercase    :				Any       		=							MgpstrProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				Dict       		=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

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

          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							self.get_image_processor()
          lowercase    :				List[str]       		=							self.get_tokenizer()

          lowercase    :				Tuple       		=							MgpstrProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				List[str]       		=							None
          lowercase    :				Optional[int]       		=							self.prepare_image_inputs()

          lowercase    :				Any       		=							processor(text=_a	,    images=_a				)

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

   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							self.get_image_processor()
          lowercase    :				Tuple       		=							self.get_tokenizer()

          lowercase    :				List[str]       		=							MgpstrProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				List[Any]       		=							torch.randn(1	,    27	,    38				)
          lowercase    :				Any       		=							torch.randn(1	,    27	,    50_257				)
          lowercase    :				Union[str, Any]       		=							torch.randn(1	,    27	,    30_522				)

          lowercase    :				List[str]       		=							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"""




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

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


_A			:       Optional[int]   		=       logging.getLogger(__name__)

class 		a__						(      a_						):
   def __init__(			self	,    _a=-1				):
          # in NER datasets, the last column is usually reserved for NER label
          lowercase    :				List[str]       		=							label_idx
   def 			__magic_name__			(			self	,    _a	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				Optional[Any]       		=							mode.value
          lowercase    :				List[str]       		=							os.path.join(_a	,    f"""{mode}.txt"""				)
          lowercase    :				str       		=							1
          lowercase    :				Optional[int]       		=							[]
          with open(_a	,    encoding="utf-8"				) as f:
                 lowercase    :				List[Any]       		=							[]
                 lowercase    :				Optional[int]       		=							[]
                 for line in f:
                        if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                               if words:
                                      examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
                                      guid_index += 1
                                      lowercase    :				int       		=							[]
                                      lowercase    :				int       		=							[]
                        else:
                               lowercase    :				Optional[Any]       		=							line.split(" "				)
                               words.append(splits[0]				)
                               if len(_a				) > 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=_a	,    labels=_a				)				)
          return examples
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				List[str]       		=							0
          for line in test_input_reader:
                 if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                        writer.write(_a				)
                        if not preds_list[example_id]:
                               example_id += 1
                 elif preds_list[example_id]:
                        lowercase    :				Any       		=							line.split()[0] + " " + preds_list[example_id].pop(0				) + "\n"
                        writer.write(_a				)
                 else:
                        logger.warning("Maximum sequence length exceeded: No prediction for '%s'."	,    line.split()[0]				)

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


class 		a__						(      a_						):
   def __init__(			self				):
          # in CONLL2003 dataset chunk column is second-to-last
          super().__init__(label_idx=-2				)

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        lowercase    :				Tuple       		=							f.read().splitlines()
                 if "O" not in labels:
                        lowercase    :				Optional[int]       		=							["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 		a__						(      a_						):
   def 			__magic_name__			(			self	,    _a	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				List[Any]       		=							mode.value
          lowercase    :				Optional[int]       		=							os.path.join(_a	,    f"""{mode}.txt"""				)
          lowercase    :				Tuple       		=							1
          lowercase    :				List[str]       		=							[]

          with open(_a	,    encoding="utf-8"				) as f:
                 for sentence in parse_incr(_a				):
                        lowercase    :				Optional[Any]       		=							[]
                        lowercase    :				str       		=							[]
                        for token in sentence:
                               words.append(token["form"]				)
                               labels.append(token["upos"]				)
                        assert len(_a				) == len(_a				)
                        if words:
                               examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
                               guid_index += 1
          return examples
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				str       		=							0
          for sentence in parse_incr(_a				):
                 lowercase    :				List[Any]       		=							preds_list[example_id]
                 lowercase    :				List[str]       		=							""
                 for token in sentence:
                        out += f"""{token["form"]} ({token["upos"]}|{s_p.pop(0				)}) """
                 out += "\n"
                 writer.write(_a				)
                 example_id += 1

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "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",
                 ]


