






import pprint

import requests

a_							=    '''https://zenquotes.io/api'''


def        _a  (     ) ->   list:
						"""simple docstring"""







						return requests.get(API_ENDPOINT_URL + "/today"   ).json()


def        _a  (     ) ->   list:
						"""simple docstring"""







						return requests.get(API_ENDPOINT_URL + "/random"   ).json()


if __name__ == "__main__":
	a_							=    random_quotes()
	pprint.pprint(response)














import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_MAPPING,
	    SegformerForImageClassification,
	    SegformerForSemanticSegmentation,
	    SegformerModel,
	)
	from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import SegformerImageProcessor


class      lowercase__     (			_UpperCAmelCase	):

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.config_class(**self.inputs_dict       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "hidden_sizes"       )       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "num_attention_heads"       )       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "num_encoder_blocks"       )       )





class      lowercase__     :

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=13		,    __UpperCAmelCase=64		,    __UpperCAmelCase=3		,    __UpperCAmelCase=4		,    __UpperCAmelCase=[2, 2, 2, 2]		,    __UpperCAmelCase=[8, 4, 2, 1]		,    __UpperCAmelCase=[16, 32, 64, 128]		,    __UpperCAmelCase=[1, 4, 8, 16]		,    __UpperCAmelCase=[1, 2, 4, 8]		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    __UpperCAmelCase="gelu"		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.02		,    __UpperCAmelCase=3		,    __UpperCAmelCase=None		,    )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						parent
							lowerCAmelCase__       =						batch_size
							lowerCAmelCase__       =						image_size
							lowerCAmelCase__       =						num_channels
							lowerCAmelCase__       =						num_encoder_blocks
							lowerCAmelCase__       =						sr_ratios
							lowerCAmelCase__       =						depths
							lowerCAmelCase__       =						hidden_sizes
							lowerCAmelCase__       =						downsampling_rates
							lowerCAmelCase__       =						num_attention_heads
							lowerCAmelCase__       =						is_training
							lowerCAmelCase__       =						use_labels
							lowerCAmelCase__       =						hidden_act
							lowerCAmelCase__       =						hidden_dropout_prob
							lowerCAmelCase__       =						attention_probs_dropout_prob
							lowerCAmelCase__       =						initializer_range
							lowerCAmelCase__       =						num_labels
							lowerCAmelCase__       =						scope

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

							lowerCAmelCase__       =						None
							if self.use_labels:
													lowerCAmelCase__       =						ids_tensor([self.batch_size, self.image_size, self.image_size]		,    self.num_labels       )

							lowerCAmelCase__       =						self.get_config()
							return config, pixel_values, labels

	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							return SegformerConfig(
							    image_size=self.image_size		,    num_channels=self.num_channels		,    num_encoder_blocks=self.num_encoder_blocks		,    depths=self.depths		,    hidden_sizes=self.hidden_sizes		,    num_attention_heads=self.num_attention_heads		,    hidden_act=self.hidden_act		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    initializer_range=self.initializer_range		,    )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerModel(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__UpperCAmelCase       )
							lowerCAmelCase__       =						lowerCAmelCase__       =						self.image_size // (self.downsampling_rates[-1] * 2)
							self.parent.assertEqual(
							    result.last_hidden_state.shape		,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						self.num_labels
							lowerCAmelCase__       =						SegformerForSemanticSegmentation(__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__UpperCAmelCase       )
							self.parent.assertEqual(
							    result.logits.shape		,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    labels=__UpperCAmelCase       )
							self.parent.assertEqual(
							    result.logits.shape		,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
							self.parent.assertGreater(result.loss		,    0.0       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						1
							lowerCAmelCase__       =						SegformerForSemanticSegmentation(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						torch.randint(0		,    1		,    (self.batch_size, self.image_size, self.image_size)       ).to(__UpperCAmelCase       )
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    labels=__UpperCAmelCase       )
							self.parent.assertGreater(result.loss		,    0.0       )


	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.prepare_config_and_inputs()
							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						config_and_inputs
							lowerCAmelCase__       =						{"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase,					unittest.TestCase	):
	a_         =(
	    (
	        SegformerModel,
	        SegformerForSemanticSegmentation,
	        SegformerForImageClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	a_         =(
	    {
	        """feature-extraction""": SegformerModel,
	        """image-classification""": SegformerForImageClassification,
	        """image-segmentation""": SegformerForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	a_         =True
	a_         =False
	a_         =False
	a_         =False

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerModelTester(self       )
							lowerCAmelCase__       =						SegformerConfigTester(self		,    config_class=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							self.config_tester.run_common_tests()

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_binary_image_segmentation(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_segmentation(*__UpperCAmelCase       )

	@unittest.skip("SegFormer does not use inputs_embeds"       )
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							pass

	@unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods"       )
	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							pass

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													lowerCAmelCase__       =						inspect.signature(model.forward       )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCAmelCase__       =						[*signature.parameters.keys()]

													lowerCAmelCase__       =						["pixel_values"]
													self.assertListEqual(arg_names[:1]		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__       =						True

							for model_class in self.all_model_classes:
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						False
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )
													lowerCAmelCase__       =						outputs.attentions

													lowerCAmelCase__       =						sum(self.model_tester.depths       )
													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )
													lowerCAmelCase__       =						outputs.attentions

													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# verify the first attentions (first block, first layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 4) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(attentions[0].shape[-3:]       )		,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]		,    )

													# verify the last attentions (last block, last layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 32) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
													self.assertListEqual(
													    list(attentions[-1].shape[-3:]       )		,    [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]		,    )
													lowerCAmelCase__       =						len(__UpperCAmelCase       )

													# Check attention is always last and order is fine
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )

													self.assertEqual(out_len + 1		,    len(__UpperCAmelCase       )       )

													lowerCAmelCase__       =						outputs.attentions

													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )
													# verify the first attentions (first block, first layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 4) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]       )		,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]		,    )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							def check_hidden_states_output(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()

													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )

													lowerCAmelCase__       =						outputs.hidden_states

													lowerCAmelCase__       =						self.model_tester.num_encoder_blocks
													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]       )		,    [
													        self.model_tester.hidden_sizes[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ]		,    )

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowerCAmelCase__       =						True
													check_hidden_states_output(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowerCAmelCase__       =						True

													check_hidden_states_output(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							if not self.model_tester.is_training:
													return

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__       =						True

							for model_class in self.all_model_classes:
													if model_class in get_values(__UpperCAmelCase       ):
																			continue

													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.train()
													lowerCAmelCase__       =						self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase		,    return_labels=__UpperCAmelCase       )
													lowerCAmelCase__       =						model(**__UpperCAmelCase       ).loss
													loss.backward()

	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							pass


	@slow
	def 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





							for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCAmelCase__       =						SegformerModel.from_pretrained(__UpperCAmelCase       )
													self.assertIsNotNone(__UpperCAmelCase       )





def        _a  (     ) ->   Any:
						"""simple docstring"""







						lowerCAmelCase__       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
						return image





@require_torch
class      lowercase__     (			unittest.TestCase	):

	@slow
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"       ).to(
							    __UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						torch.Size((1, model.config.num_labels, 128, 128)       )
							self.assertEqual(outputs.logits.shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						torch.tensor(
							    [
							        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
							        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
							        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
							    ]       ).to(__UpperCAmelCase       )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,    __UpperCAmelCase		,    atol=1E-4       )       )

	@slow
	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained(
							    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"       ).to(__UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						torch.Size((1, model.config.num_labels, 128, 128)       )
							self.assertEqual(outputs.logits.shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						torch.tensor(
							    [
							        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
							        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
							        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
							    ]       ).to(__UpperCAmelCase       )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,    __UpperCAmelCase		,    atol=1E-1       )       )


	@slow
	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"       ).to(
							    __UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						outputs.logits.detach().cpu()

							lowerCAmelCase__       =						image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase		,    target_sizes=[(500, 300)]       )
							lowerCAmelCase__       =						torch.Size((500, 300)       )
							self.assertEqual(segmentation[0].shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase       )
							lowerCAmelCase__       =						torch.Size((128, 128)       )
							self.assertEqual(segmentation[0].shape		,    __UpperCAmelCase       )






