




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    lowerCamelCase    :

				@staticmethod
				def 							UpperCAmelCase__       ( *lowerCamelCase_			:   Tuple       ,						**lowerCamelCase_			:   int  )						->   int:
									pass


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

			@require_torch
			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   List[Any]:
								__magic_name__							:	int    							=							pipeline(
								    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''       ,						)
								__magic_name__							:	int    							=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								__magic_name__							:	Tuple    							=							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''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}],
								        [{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}],
								    ]       ,						)

								__magic_name__							:	Optional[Any]    							=							image_classifier([image] * 5       ,						candidate_labels=['''A''', '''B''', '''C''']       ,						batch_size=2  )
								self.assertEqual(
								    nested_simplify(lowerCamelCase_  )       ,						[
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								        ],
								    ]       ,						)

			@require_tf
			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Optional[Any]:
								__magic_name__							:	List[str]    							=							pipeline(
								    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''       ,						framework='''tf'''  )
								__magic_name__							:	Tuple    							=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								__magic_name__							:	List[str]    							=							image_classifier(lowerCamelCase_       ,						candidate_labels=['''a''', '''b''', '''c''']  )

								self.assertEqual(
								    nested_simplify(lowerCamelCase_  )       ,						[{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}]       ,						)

								__magic_name__							:	str    							=							image_classifier([image] * 5       ,						candidate_labels=['''A''', '''B''', '''C''']       ,						batch_size=2  )
								self.assertEqual(
								    nested_simplify(lowerCamelCase_  )       ,						[
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(lowerCamelCase_  )},
								        ],
								    ]       ,						)

			@slow
			@require_torch
			def 							UpperCAmelCase__       ( self			:   str  )						->   List[str]:
								__magic_name__							:	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
								__magic_name__							:	int    							=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								__magic_name__							:	Union[str, Any]    							=							image_classifier(lowerCamelCase_       ,						candidate_labels=['''cat''', '''plane''', '''remote''']  )

								self.assertEqual(
								    nested_simplify(lowerCamelCase_  )       ,						[
								        {'''score''': 0.5_1_1, '''label''': '''remote'''},
								        {'''score''': 0.4_8_5, '''label''': '''cat'''},
								        {'''score''': 0.0_0_4, '''label''': '''plane'''},
								    ]       ,						)

								__magic_name__							:	List[str]    							=							image_classifier([image] * 5       ,						candidate_labels=['''cat''', '''plane''', '''remote''']       ,						batch_size=2  )
								self.assertEqual(
								    nested_simplify(lowerCamelCase_  )       ,						[
								        [
								            {'''score''': 0.5_1_1, '''label''': '''remote'''},
								            {'''score''': 0.4_8_5, '''label''': '''cat'''},
								            {'''score''': 0.0_0_4, '''label''': '''plane'''},
								        ],
								    ]
								    * 5       ,						)


			@slow
			@require_tf
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   int:
								__magic_name__							:	List[str]    							=							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
								__magic_name__							:	Dict    							=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								__magic_name__							:	Optional[Any]    							=							image_classifier(lowerCamelCase_       ,						candidate_labels=['''cat''', '''plane''', '''remote''']  )
								self.assertEqual(
								    nested_simplify(lowerCamelCase_  )       ,						[
								        {'''score''': 0.5_1_1, '''label''': '''remote'''},
								        {'''score''': 0.4_8_5, '''label''': '''cat'''},
								        {'''score''': 0.0_0_4, '''label''': '''plane'''},
								    ]       ,						)

								__magic_name__							:	str    							=							image_classifier([image] * 5       ,						candidate_labels=['''cat''', '''plane''', '''remote''']       ,						batch_size=2  )
								self.assertEqual(
								    nested_simplify(lowerCamelCase_  )       ,						[
								        [
								            {'''score''': 0.5_1_1, '''label''': '''remote'''},
								            {'''score''': 0.4_8_5, '''label''': '''cat'''},
								            {'''score''': 0.0_0_4, '''label''': '''plane'''},
								        ],
								    ]
								    * 5       ,						)









# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__lowerCamelCase   :						int				=			logging.get_logger(__name__)

__lowerCamelCase   :						Dict[Optional[str], Type[Formatter]]				=			{}
__lowerCamelCase   :						Dict[Optional[str], str]				=			{}
__lowerCamelCase   :						Dict[Optional[str], Exception]				=			{}







def 		lowercase__   (		__A:	type							,__A:	Optional[str]							,__A:	Optional[List[str]] = None							,):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							aliases if aliases is not None else []
					if format_type in _FORMAT_TYPES:
										logger.warning(
										    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''				)
					__magic_name__							:	Tuple    							=							formatter_cls
					for alias in set(aliases + [format_type]				):
										if alias in _FORMAT_TYPES_ALIASES:
															logger.warning(
															    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''				)
										__magic_name__							:	Optional[int]    							=							format_type







def 		lowercase__   (		__A:	Exception							,__A:	Optional[str]							,__A:	Optional[List[str]] = None				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							aliases if aliases is not None else []
					for alias in set(aliases + [format_type]				):
										__magic_name__							:	List[str]    							=							unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
	__lowerCamelCase   :						str				=			ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
	_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
	__lowerCamelCase   :						int				=			ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
	_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
	__lowerCamelCase   :						str				=			ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])







def 		lowercase__   (		__A:	Optional[str]				):





					'''simple docstring'''






					if format_type in _FORMAT_TYPES_ALIASES:
										return _FORMAT_TYPES_ALIASES[format_type]
					else:
										return format_type







def 		lowercase__   (		__A:	Optional[str]							,**__A:	Any				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							get_format_type_from_alias(__A				)
					if format_type in _FORMAT_TYPES:
										return _FORMAT_TYPES[format_type](**__A				)
					if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
										raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
					else:
										raise ValueError(
										    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None				)}, but got \'{format_type}\''''				)



