



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_													=      {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_													=      ['''NllbTokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_													=      ['''NllbTokenizerFast''']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_nllb import NllbTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_nllb_fast import NllbTokenizerFast

else:
				import sys

				lowercase_													=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









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     __a			:



					@staticmethod
					def 		UpperCamelCase				(    *snake_case_				:     Any					,						**snake_case_				:     str)->				int:
										pass






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



	@require_torch
	def 		UpperCamelCase				(    self				:     Dict)->				List[str]:
						__lowerCAmelCase						 =pipeline(
						    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""					,						)
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						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(snake_case_)					,						[
						        [{"""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"""}],
						    ]					,						)

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



	@require_tf
	def 		UpperCamelCase				(    self				:     Dict)->				Optional[Any]:
						__lowerCAmelCase						 =pipeline(
						    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""					,						framework="""tf""")
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""a""", """b""", """c"""])

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

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



	@slow
	@require_torch
	def 		UpperCamelCase				(    self				:     Any)->				Dict:
						__lowerCAmelCase						 =pipeline(
						    task="""zero-shot-image-classification"""					,						model="""openai/clip-vit-base-patch32"""					,						)
						# This is an image of 2 cats with remotes and no planes
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""cat""", """plane""", """remote"""])

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

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""cat""", """plane""", """remote"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""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:
						__lowerCAmelCase						 =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
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""cat""", """plane""", """remote"""])
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        {"""score""": 0.5_1_1, """label""": """remote"""},
						        {"""score""": 0.4_8_5, """label""": """cat"""},
						        {"""score""": 0.0_0_4, """label""": """plane"""},
						    ]					,						)

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




