





import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:


		class 			lowercase				:



								"""simple docstring"""

								@staticmethod
								def 		__UpperCAmelCase       (    *lowerCamelCase_			:		Any ,				**lowerCamelCase_			:		Tuple					):





												'''simple docstring'''







												pass


@is_pipeline_test
@require_vision
class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						@require_torch
						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			int  						=   pipeline(
										    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification' ,				)
										_snake_case :			List[Any]  						=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
										_snake_case :			Dict  						=   image_classifier(lowerCamelCase_ ,				candidate_labels=['a', 'b', 'c']					)

										# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
										# python and torch versions.
										self.assertIn(
										    nested_simplify(lowerCamelCase_					) ,				[
										        [{'score': 0.333, 'label': 'a'}, {'score': 0.333, 'label': 'b'}, {'score': 0.333, 'label': 'c'}],
										        [{'score': 0.333, 'label': 'a'}, {'score': 0.333, 'label': 'c'}, {'score': 0.333, 'label': 'b'}],
										    ] ,				)

										_snake_case :			Dict  						=   image_classifier([image] * 5 ,				candidate_labels=['A', 'B', 'C'] ,				batch_size=2					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_					) ,				[
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										    ] ,				)







						@require_tf
						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   pipeline(
										    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification' ,				framework='tf'					)
										_snake_case :			Optional[int]  						=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
										_snake_case :			Dict  						=   image_classifier(lowerCamelCase_ ,				candidate_labels=['a', 'b', 'c']					)

										self.assertEqual(
										    nested_simplify(lowerCamelCase_					) ,				[{'score': 0.333, 'label': 'a'}, {'score': 0.333, 'label': 'b'}, {'score': 0.333, 'label': 'c'}] ,				)

										_snake_case :			Optional[int]  						=   image_classifier([image] * 5 ,				candidate_labels=['A', 'B', 'C'] ,				batch_size=2					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_					) ,				[
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										    ] ,				)







						@slow
						@require_torch
						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   pipeline(
										    task='zero-shot-image-classification' ,				model='openai/clip-vit-base-patch32' ,				)
										# This is an image of 2 cats with remotes and no planes
										_snake_case :			Tuple  						=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
										_snake_case :			Any  						=   image_classifier(lowerCamelCase_ ,				candidate_labels=['cat', 'plane', 'remote']					)

										self.assertEqual(
										    nested_simplify(lowerCamelCase_					) ,				[
										        {'score': 0.511, 'label': 'remote'},
										        {'score': 0.485, 'label': 'cat'},
										        {'score': 0.004, 'label': 'plane'},
										    ] ,				)

										_snake_case :			Optional[Any]  						=   image_classifier([image] * 5 ,				candidate_labels=['cat', 'plane', 'remote'] ,				batch_size=2					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_					) ,				[
										        [
										            {'score': 0.511, 'label': 'remote'},
										            {'score': 0.485, 'label': 'cat'},
										            {'score': 0.004, 'label': 'plane'},
										        ],
										    ]
										    * 5 ,				)







						@slow
						@require_tf
						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   pipeline(
										    task='zero-shot-image-classification' ,				model='openai/clip-vit-base-patch32' ,				framework='tf'					)
										# This is an image of 2 cats with remotes and no planes
										_snake_case :			Union[str, Any]  						=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
										_snake_case :			Tuple  						=   image_classifier(lowerCamelCase_ ,				candidate_labels=['cat', 'plane', 'remote']					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_					) ,				[
										        {'score': 0.511, 'label': 'remote'},
										        {'score': 0.485, 'label': 'cat'},
										        {'score': 0.004, 'label': 'plane'},
										    ] ,				)

										_snake_case :			Tuple  						=   image_classifier([image] * 5 ,				candidate_labels=['cat', 'plane', 'remote'] ,				batch_size=2					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_					) ,				[
										        [
										            {'score': 0.511, 'label': 'remote'},
										            {'score': 0.485, 'label': 'cat'},
										            {'score': 0.004, 'label': 'plane'},
										        ],
										    ]
										    * 5 ,				)













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

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


lowercase_		:				Tuple				=						logging.getLogger(__name__)


class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Dict ,				lowerCamelCase_			:		Dict=-1					):





										'''simple docstring'''







										_snake_case :			str  						=   label_idx







						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		Union[Split, str]					):





										'''simple docstring'''







										if isinstance(lowerCamelCase_ ,				lowerCamelCase_					):
														_snake_case :			Union[str, Any]  						=   mode.value
										_snake_case :			Optional[int]  						=   os.path.join(lowerCamelCase_ ,				f'''{mode}.txt'''					)
										_snake_case :			Dict  						=   1
										_snake_case :			List[str]  						=   []
										with open(lowerCamelCase_ ,				encoding='utf-8'					) as f:
														_snake_case :			List[Any]  						=   []
														_snake_case :			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=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
																										guid_index += 1
																										_snake_case :			Optional[Any]  						=   []
																										_snake_case :			Union[str, Any]  						=   []
																		else:
																						_snake_case :			Tuple  						=   line.split(' '					)
																						words.append(splits[0]					)
																						if len(lowerCamelCase_					) > 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=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
										return examples







						def 		__UpperCAmelCase       (    self			:		Union[str, Any] ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		List					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		List[str] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		_snake_case :			Optional[int]  						=   f.read().splitlines()
														if "O" not in labels:
																		_snake_case :			Optional[int]  						=   ['O'] + labels
														return labels
										else:
														return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Optional[int]					):





										'''simple docstring'''







										super().__init__(label_idx=-2					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		_snake_case :			str  						=   f.read().splitlines()
														if "O" not in labels:
																		_snake_case :			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 			lowercase				(							a_     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		Union[Split, str]					):





										'''simple docstring'''







										if isinstance(lowerCamelCase_ ,				lowerCamelCase_					):
														_snake_case :			str  						=   mode.value
										_snake_case :			List[str]  						=   os.path.join(lowerCamelCase_ ,				f'''{mode}.txt'''					)
										_snake_case :			Tuple  						=   1
										_snake_case :			List[str]  						=   []

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







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		List					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'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",
														]






