

"""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						       =   logging.getLogger(__name__)





class    snake_case     (    a_  ):

				def __init__(     self							:    Any					,  UpperCamelCase__							:    Union[str, Any]=-1)->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      label_idx

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Dict)->       List[InputExample]:



						'''simple docstring'''


						if isinstance(lowercase_					,  lowercase_):
								__lowerCAmelCase:	Optional[int]   				=      mode.value
						__lowerCAmelCase:	List[str]   				=      os.path.join(lowercase_					,  f"{mode}.txt")
						__lowerCAmelCase:	List[str]   				=      1
						__lowerCAmelCase:	List[Any]   				=      []
						with open(lowercase_					,  encoding="utf-8") as f:
								__lowerCAmelCase:	Optional[int]   				=      []
								__lowerCAmelCase:	Any   				=      []
								for line in f:
										if line.startswith("-DOCSTART-") or line == "" or line == "\n":
												if words:
														examples.append(InputExample(guid=f"{mode}-{guid_index}"					,  words=lowercase_					,  labels=lowercase_))
														guid_index += 1
														__lowerCAmelCase:	List[Any]   				=      []
														__lowerCAmelCase:	Optional[Any]   				=      []
										else:
												__lowerCAmelCase:	str   				=      line.split(" ")
												words.append(splits[0])
												if len(lowercase_) > 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=lowercase_					,  labels=lowercase_))
						return examples

				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Optional[Any])->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      0
						for line in test_input_reader:
								if line.startswith("-DOCSTART-") or line == "" or line == "\n":
										writer.write(lowercase_)
										if not preds_list[example_id]:
												example_id += 1
								elif preds_list[example_id]:
										__lowerCAmelCase:	int   				=      line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
										writer.write(lowercase_)
								else:
										logger.warning("Maximum sequence length exceeded: No prediction for \'%s\'."					,  line.split()[0])




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    List[Any])->       List[str]:



						'''simple docstring'''


						if path:
								with open(lowercase_					,  "r") as f:
										__lowerCAmelCase:	List[Any]   				=      f.read().splitlines()
								if "O" not in labels:
										__lowerCAmelCase:	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    snake_case     (    a_  ):

				def __init__(     self							:    str)->       int:



						'''simple docstring'''


						super().__init__(label_idx=-2)




				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    Optional[int])->       List[str]:



						'''simple docstring'''


						if path:
								with open(lowercase_					,  "r") as f:
										__lowerCAmelCase:	Any   				=      f.read().splitlines()
								if "O" not in labels:
										__lowerCAmelCase:	Union[str, Any]   				=      ["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    snake_case     (    a_  ):

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    int)->       List[InputExample]:



						'''simple docstring'''


						if isinstance(lowercase_					,  lowercase_):
								__lowerCAmelCase:	Optional[Any]   				=      mode.value
						__lowerCAmelCase:	Union[str, Any]   				=      os.path.join(lowercase_					,  f"{mode}.txt")
						__lowerCAmelCase:	Tuple   				=      1
						__lowerCAmelCase:	int   				=      []

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

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Tuple)->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      0
						for sentence in parse_incr(lowercase_):
								__lowerCAmelCase:	Dict   				=      preds_list[example_id]
								__lowerCAmelCase:	int   				=      ""
								for token in sentence:
										out += f"{token['form']} ({token['upos']}|{s_p.pop(0)}) "
								out += "\n"
								writer.write(lowercase_)
								example_id += 1




				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    Any)->       List[str]:



						'''simple docstring'''


						if path:
								with open(lowercase_					,  "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",
								]





"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ChineseCLIPImageProcessor





class    snake_case     (    unittest.TestCase  ):

				def __init__(     self							:    Any					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Any=7					,  UpperCamelCase__							:    List[str]=3					,  UpperCamelCase__							:    List[Any]=1_8					,  UpperCamelCase__							:    List[Any]=3_0					,  UpperCamelCase__							:    List[str]=4_0_0					,  UpperCamelCase__							:    Optional[int]=True					,  UpperCamelCase__							:    Optional[Any]=None					,  UpperCamelCase__							:    List[str]=True					,  UpperCamelCase__							:    List[str]=None					,  UpperCamelCase__							:    Optional[int]=True					,  UpperCamelCase__							:    int=[0.48145466, 0.4578275, 0.40821073]					,  UpperCamelCase__							:    str=[0.26862954, 0.26130258, 0.27577711]					,  UpperCamelCase__							:    List[str]=True					,  )->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      size if size is not None else {"height": 2_2_4, "width": 2_2_4}
						__lowerCAmelCase:	Union[str, Any]   				=      crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
						__lowerCAmelCase:	Optional[int]   				=      parent
						__lowerCAmelCase:	List[str]   				=      batch_size
						__lowerCAmelCase:	Union[str, Any]   				=      num_channels
						__lowerCAmelCase:	Optional[Any]   				=      image_size
						__lowerCAmelCase:	Tuple   				=      min_resolution
						__lowerCAmelCase:	List[str]   				=      max_resolution
						__lowerCAmelCase:	List[Any]   				=      do_resize
						__lowerCAmelCase:	Union[str, Any]   				=      size
						__lowerCAmelCase:	List[Any]   				=      do_center_crop
						__lowerCAmelCase:	Optional[int]   				=      crop_size
						__lowerCAmelCase:	Dict   				=      do_normalize
						__lowerCAmelCase:	List[str]   				=      image_mean
						__lowerCAmelCase:	Optional[int]   				=      image_std
						__lowerCAmelCase:	str   				=      do_convert_rgb

				def 							lowercase_					(     self							:    Tuple)->       str:



						'''simple docstring'''


						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_convert_rgb": self.do_convert_rgb,
						}




				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    Tuple=False					,  UpperCamelCase__							:    List[str]=False					,  UpperCamelCase__							:    Dict=False)->       List[str]:



						'''simple docstring'''


						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
								__lowerCAmelCase:	Optional[int]   				=      []
								for i in range(self.batch_size):
										image_inputs.append(
										    np.random.randint(
										        2_5_5					,  size=(self.num_channels, self.max_resolution, self.max_resolution)					,  dtype=np.uinta))
						else:
								__lowerCAmelCase:	List[str]   				=      []
								for i in range(self.batch_size):
										__lowerCAmelCase				,			__lowerCAmelCase:	List[str]   				=      np.random.choice(np.arange(self.min_resolution					,  self.max_resolution)					,  2)
										image_inputs.append(np.random.randint(2_5_5					,  size=(self.num_channels, width, height)					,  dtype=np.uinta))

						if not numpify and not torchify:
								# PIL expects the channel dimension as last dimension
								__lowerCAmelCase:	Union[str, Any]   				=      [Image.fromarray(np.moveaxis(UpperCamelCase__					,  0					,  -1)) for x in image_inputs]

						if torchify:
								__lowerCAmelCase:	str   				=      [torch.from_numpy(UpperCamelCase__) for x in image_inputs]

						return image_inputs





@require_torch
@require_vision
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				str       				=  ChineseCLIPImageProcessor if is_vision_available() else None

				def 							lowercase_					(     self							:    Any)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      ChineseCLIPImageProcessingTester(self					,  do_center_crop=UpperCamelCase__)

				@property
				def 							lowercase_					(     self							:    Any)->       Optional[Any]:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 							lowercase_					(     self							:    Union[str, Any])->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "size"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_convert_rgb"))

				def 							lowercase_					(     self							:    List[Any])->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size					,  {"height": 2_2_4, "width": 2_2_4})
						self.assertEqual(image_processor.crop_size					,  {"height": 1_8, "width": 1_8})

						__lowerCAmelCase:	List[str]   				=      self.image_processing_class.from_dict(self.image_processor_dict					,  size=4_2					,  crop_size=8_4)
						self.assertEqual(image_processor.size					,  {"shortest_edge": 4_2})
						self.assertEqual(image_processor.crop_size					,  {"height": 8_4, "width": 8_4})

				def 							lowercase_					(     self							:    List[str])->       Optional[int]:



						'''simple docstring'''


						pass

				def 							lowercase_					(     self							:    Any)->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  Image.Image)

						# Test not batched input
						__lowerCAmelCase:	Optional[int]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	int   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

				def 							lowercase_					(     self							:    int)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__lowerCAmelCase:	List[Any]   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					,  numpify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  np.ndarray)

						# Test not batched input
						__lowerCAmelCase:	List[Any]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Any   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )




				def 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__lowerCAmelCase:	Dict   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					,  torchify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  torch.Tensor)

						# Test not batched input
						__lowerCAmelCase:	Tuple   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Optional[int]   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )





@require_torch
@require_vision
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  ChineseCLIPImageProcessor if is_vision_available() else None

				def 							lowercase_					(     self							:    int)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      ChineseCLIPImageProcessingTester(self					,  num_channels=4					,  do_center_crop=UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      3

				@property
				def 							lowercase_					(     self							:    Union[str, Any])->       Any:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "size"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_convert_rgb"))

				def 							lowercase_					(     self							:    Tuple)->       Any:



						'''simple docstring'''


						pass




				def 							lowercase_					(     self							:    Tuple)->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase:	int   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  Image.Image)

						# Test not batched input
						__lowerCAmelCase:	List[Any]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Optional[int]   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )


