





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:
				UpperCamelCase__ :			Tuple       	=			False


if is_vision_available():
				from PIL import Image
else:







				class 	_UpperCamelCase	:







									'''simple docstring'''

									@staticmethod
									def 							SCREAMING_SNAKE_CASE				(    *__lowercase     :	Tuple			,       **__lowercase     :	Dict		):






										'''simple docstring'''







										pass







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







					'''simple docstring'''

					lowerCamelCase  :  List[Any]           =							MODEL_FOR_VISION_2_SEQ_MAPPING
					lowerCamelCase  :  Tuple           =							TF_MODEL_FOR_VISION_2_SEQ_MAPPING





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[int]			,       __lowercase     :	Dict			,       __lowercase     :	Any			,       __lowercase     :	int		):






						'''simple docstring'''







						UpperCAmelCase_		    =  pipeline("""image-to-text"""			,       model=__lowercase			,       tokenizer=__lowercase			,       image_processor=__lowercase		)
						UpperCAmelCase_		    =  [
						    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		),
						    """./tests/fixtures/tests_samples/COCO/000000039769.png""",
						]
						return pipe, examples





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]			,       __lowercase     :	List[str]			,       __lowercase     :	int		):






						'''simple docstring'''







						UpperCAmelCase_		    =  pipe(__lowercase		)
						self.assertEqual(
						    __lowercase			,       [
						        [{"""generated_text""": ANY(__lowercase		)}],
						        [{"""generated_text""": ANY(__lowercase		)}],
						    ]			,       )





					@require_tf
					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  pipeline("""image-to-text"""			,       model="""hf-internal-testing/tiny-random-vit-gpt2"""			,       framework="""tf"""		)
						UpperCAmelCase_		    =  """./tests/fixtures/tests_samples/COCO/000000039769.png"""

						UpperCAmelCase_		    =  pipe(__lowercase		)
						self.assertEqual(
						    __lowercase			,       [
						        {
						            """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
						        },
						    ]			,       )

						UpperCAmelCase_		    =  pipe([image, image]		)
						self.assertEqual(
						    __lowercase			,       [
						        [
						            {
						                """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
						            }
						        ],
						        [
						            {
						                """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
						            }
						        ],
						    ]			,       )

						UpperCAmelCase_		    =  pipe(__lowercase			,       max_new_tokens=1		)
						self.assertEqual(
						    __lowercase			,       [{"""generated_text""": """growth"""}]			,       )





					@require_torch
					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  pipeline("""image-to-text"""			,       model="""hf-internal-testing/tiny-random-vit-gpt2"""		)
						UpperCAmelCase_		    =  """./tests/fixtures/tests_samples/COCO/000000039769.png"""

						UpperCAmelCase_		    =  pipe(__lowercase		)
						self.assertEqual(
						    __lowercase			,       [
						        {
						            """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
						        },
						    ]			,       )

						UpperCAmelCase_		    =  pipe([image, image]		)
						self.assertEqual(
						    __lowercase			,       [
						        [
						            {
						                """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
						            }
						        ],
						        [
						            {
						                """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
						            }
						        ],
						    ]			,       )





					@require_torch
					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  pipeline("""image-to-text"""			,       model="""hf-internal-testing/tiny-random-BlipForConditionalGeneration"""		)
						UpperCAmelCase_		    =  """./tests/fixtures/tests_samples/COCO/000000039769.png"""
						UpperCAmelCase_		    =  """a photo of"""

						UpperCAmelCase_		    =  pipe(__lowercase			,       prompt=__lowercase		)
						self.assertTrue(outputs[0]["""generated_text"""].startswith(__lowercase		)		)





					@slow
					@require_torch
					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  pipeline("""image-to-text"""			,       model="""ydshieh/vit-gpt2-coco-en"""		)
						UpperCAmelCase_		    =  """./tests/fixtures/tests_samples/COCO/000000039769.png"""

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

						UpperCAmelCase_		    =  pipe([image, image]		)
						self.assertEqual(
						    __lowercase			,       [
						        [{"""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 							SCREAMING_SNAKE_CASE				(    self     :	Tuple		):






						'''simple docstring'''







						UpperCAmelCase_		    =  pipeline("""image-to-text"""			,       model="""Salesforce/blip-image-captioning-base"""		)
						UpperCAmelCase_		    =  """https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"""
						UpperCAmelCase_		    =  Image.open(requests.get(__lowercase			,       stream=__lowercase		).raw		)

						UpperCAmelCase_		    =  pipe(__lowercase		)
						self.assertEqual(__lowercase			,       [{"""generated_text""": """a pink pokemon pokemon with a blue shirt and a blue shirt"""}]		)





					@slow
					@require_torch
					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  pipeline("""image-to-text"""			,       model="""microsoft/git-base-coco"""		)
						UpperCAmelCase_		    =  """https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"""
						UpperCAmelCase_		    =  Image.open(requests.get(__lowercase			,       stream=__lowercase		).raw		)

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





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






						'''simple docstring'''







						UpperCAmelCase_		    =  pipeline("""image-to-text"""			,       model="""Salesforce/blip-image-captioning-base"""		)
						UpperCAmelCase_		    =  """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"""
						UpperCAmelCase_		    =  Image.open(requests.get(__lowercase			,       stream=__lowercase		).raw		)

						UpperCAmelCase_		    =  """a photography of"""

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

						with self.assertRaises(__lowercase		):
							UpperCAmelCase_		    =  pipe([image, image]			,       prompt=[prompt, prompt]		)





					@slow
					@require_torch
					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  pipeline("""image-to-text"""			,       model="""microsoft/git-base-coco"""		)
						UpperCAmelCase_		    =  """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"""
						UpperCAmelCase_		    =  Image.open(requests.get(__lowercase			,       stream=__lowercase		).raw		)

						UpperCAmelCase_		    =  """a photo of a"""

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

						with self.assertRaises(__lowercase		):
							UpperCAmelCase_		    =  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 							SCREAMING_SNAKE_CASE				(    self     :	Tuple		):






						'''simple docstring'''







						UpperCAmelCase_		    =  pipeline("""image-to-text"""			,       model="""google/pix2struct-ai2d-base"""		)
						UpperCAmelCase_		    =  """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"""
						UpperCAmelCase_		    =  Image.open(requests.get(__lowercase			,       stream=__lowercase		).raw		)

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

						UpperCAmelCase_		    =  pipe(__lowercase			,       prompt=__lowercase		)
						self.assertEqual(__lowercase			,       [{"""generated_text""": """ash cloud"""}]		)

						with self.assertRaises(__lowercase		):
							UpperCAmelCase_		    =  pipe([image, image]			,       prompt=[prompt, prompt]		)





					@slow
					@require_tf
					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  pipeline("""image-to-text"""			,       model="""ydshieh/vit-gpt2-coco-en"""			,       framework="""tf"""		)
						UpperCAmelCase_		    =  """./tests/fixtures/tests_samples/COCO/000000039769.png"""

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

						UpperCAmelCase_		    =  pipe([image, image]		)
						self.assertEqual(
						    __lowercase			,       [
						        [{"""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 """}],
						    ]			,       )









import matplotlib.pyplot as plt
import numpy as np




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  len(A )  # Number of training samples
	UpperCAmelCase_		    =  np.eye(A )  # Initialize weights as identity matrix
	for j in range(A ):
		UpperCAmelCase_		    =  point - x_train[j]
		UpperCAmelCase_		    =  np.exp(diff @ diff.T / (-2.0 * tau**2) )

	return weights




def  A_(      A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  weight_matrix(A					,   A					,   A )
	UpperCAmelCase_		    =  np.linalg.inv(x_train.T @ weight_mat @ x_train ) @ (
	    x_train.T @ weight_mat @ y_train.T
	)

	return weight




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  np.zeros(len(A ) )  # Initialize array of predictions
	for i, item in enumerate(A ):
		UpperCAmelCase_		    =  item @ local_weight(A					,   A					,   A					,   A )

	return y_pred




def  A_(      A					,   A					,   A ):
	import seaborn as sns

	UpperCAmelCase_		    =  sns.load_dataset(A )
	UpperCAmelCase_		    =  np.array(data[x_name] )
	UpperCAmelCase_		    =  np.array(data[y_name] )

	UpperCAmelCase_		    =  np.ones(len(A ) )

	# pairing elements of one and x_data
	UpperCAmelCase_		    =  np.column_stack((one, x_data) )

	return x_train, x_data, y_data




def  A_(      A					,   A					,   A					,   A					,   A					,   A					,   ):
	UpperCAmelCase_		    =  np.sort(A					,   axis=0 )
	plt.scatter(A					,   A					,   color="""blue""" )
	plt.plot(
	    x_train_sorted[:, 1]					,   preds[x_train[:, 1].argsort(0 )]					,   color="""yellow"""					,   linewidth=5					,   )
	plt.title("""Local Weighted Regression""" )
	plt.xlabel(A )
	plt.ylabel(A )
	plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# Demo with a dataset from the seaborn module
				UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__ :			Tuple       	=			load_data("""tips""", """total_bill""", """tip""")
				UpperCamelCase__ :			Tuple       	=			local_weight_regression(training_data_x, tip, 5)
				plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")


