'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_	   = {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_	   = ["DetrFeatureExtractor"]
						A_	   = ["DetrImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_	   = [
						    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "DetrForObjectDetection",
						    "DetrForSegmentation",
						    "DetrModel",
						    "DetrPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_detr import DetrFeatureExtractor
												from .image_processing_detr import DetrImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_detr import (
												    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DetrForObjectDetection,
												    DetrForSegmentation,
												    DetrModel,
												    DetrPreTrainedModel,
												)

else:
						import sys

						A_	   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






'''simple docstring'''

import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
						from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
						A_	   = False


if is_vision_available():
						from PIL import Image
else:




						class 		SCREAMING_SNAKE_CASE_					:



									"""simple docstring"""






									@staticmethod
									def   lowerCamelCase (							*snake_case_				,			**snake_case_					):
															pass




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



			"""simple docstring"""






			__magic_name__ :				Optional[int]   							=       MODEL_FOR_VISION_2_SEQ_MAPPING
			__magic_name__ :				Optional[int]   							=       TF_MODEL_FOR_VISION_2_SEQ_MAPPING



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       str				=    pipeline("image-to-text"				,			model=snake_case_				,			tokenizer=snake_case_				,			image_processor=snake_case_					)
									snake_case :       List[Any]				=    [
									    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]
									return pipe, examples



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_					):
									snake_case :       Union[str, Any]				=    pipe(snake_case_					)
									self.assertEqual(
									    snake_case_				,			[
									        [{"generated_text": ANY(snake_case_					)}],
									        [{"generated_text": ANY(snake_case_					)}],
									    ]				,			)



			@require_tf
			def   lowerCamelCase (							self					):
									snake_case :       Tuple				=    pipeline("image-to-text"				,			model="hf-internal-testing/tiny-random-vit-gpt2"				,			framework="tf"					)
									snake_case :       Union[str, Any]				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"

									snake_case :       Dict				=    pipe(snake_case_					)
									self.assertEqual(
									    snake_case_				,			[
									        {
									            "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									        },
									    ]				,			)

									snake_case :       Tuple				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									    ]				,			)

									snake_case :       List[Any]				=    pipe(snake_case_				,			max_new_tokens=1					)
									self.assertEqual(
									    snake_case_				,			[{"generated_text": "growth"}]				,			)



			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    pipeline("image-to-text"				,			model="hf-internal-testing/tiny-random-vit-gpt2"					)
									snake_case :       Tuple				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"

									snake_case :       Any				=    pipe(snake_case_					)
									self.assertEqual(
									    snake_case_				,			[
									        {
									            "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									        },
									    ]				,			)

									snake_case :       str				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									    ]				,			)



			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Tuple				=    pipeline("image-to-text"				,			model="hf-internal-testing/tiny-random-BlipForConditionalGeneration"					)
									snake_case :       Dict				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"
									snake_case :       List[Any]				=    "a photo of"

									snake_case :       List[str]				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertTrue(outputs[0]["generated_text"].startswith(snake_case_					)					)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    pipeline("image-to-text"				,			model="ydshieh/vit-gpt2-coco-en"					)
									snake_case :       Union[str, Any]				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"

									snake_case :       Tuple				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]					)

									snake_case :       Tuple				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
									        [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
									    ]				,			)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    pipeline("image-to-text"				,			model="Salesforce/blip-image-captioning-base"					)
									snake_case :       Any				=    "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
									snake_case :       Union[str, Any]				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Optional[Any]				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a pink pokemon pokemon with a blue shirt and a blue shirt"}]					)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Tuple				=    pipeline("image-to-text"				,			model="microsoft/git-base-coco"					)
									snake_case :       Tuple				=    "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
									snake_case :       Tuple				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Any				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a cartoon of a purple character."}]					)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    pipeline("image-to-text"				,			model="Salesforce/blip-image-captioning-base"					)
									snake_case :       int				=    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
									snake_case :       Tuple				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       str				=    "a photography of"

									snake_case :       int				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a photography of a volcano"}]					)

									with self.assertRaises(snake_case_					):
															snake_case :       List[Any]				=    pipe([image, image]				,			prompt=[prompt, prompt]					)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    pipeline("image-to-text"				,			model="microsoft/git-base-coco"					)
									snake_case :       Optional[Any]				=    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
									snake_case :       List[str]				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Any				=    "a photo of a"

									snake_case :       Dict				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a photo of a tent with a tent and a tent in the background."}]					)

									with self.assertRaises(snake_case_					):
															snake_case :       List[str]				=    pipe([image, image]				,			prompt=[prompt, prompt]					)



			@unittest.skipIf(
			    not is_torch_greater_or_equal_than_1_11				,			reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."					)
			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    pipeline("image-to-text"				,			model="google/pix2struct-ai2d-base"					)
									snake_case :       Optional[int]				=    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
									snake_case :       str				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Any				=    "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"

									snake_case :       str				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "ash cloud"}]					)

									with self.assertRaises(snake_case_					):
															snake_case :       List[str]				=    pipe([image, image]				,			prompt=[prompt, prompt]					)






			@slow
			@require_tf
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    pipeline("image-to-text"				,			model="ydshieh/vit-gpt2-coco-en"				,			framework="tf"					)
									snake_case :       Union[str, Any]				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"

									snake_case :       Any				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]					)

									snake_case :       Optional[int]				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
									        [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
									    ]				,			)





