


import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
						import torch
						from torch import nn

						from transformers import YolosForObjectDetection, YolosModel
						from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor







class 				snake_case__:







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase=1_3   ,						__lowercase=[3_0, 3_0]   ,						__lowercase=2   ,						__lowercase=3   ,						__lowercase=True   ,						__lowercase=True   ,						__lowercase=3_2   ,						__lowercase=5   ,						__lowercase=4   ,						__lowercase=3_7   ,						__lowercase="gelu"   ,						__lowercase=0.1   ,						__lowercase=0.1   ,						__lowercase=1_0   ,						__lowercase=0.02   ,						__lowercase=3   ,						__lowercase=None   ,						__lowercase=8   ,						__lowercase=1_0   ,						)					->    str:
										lowerCAmelCase_ :      Dict    				=				parent
										lowerCAmelCase_ :      Dict    				=				batch_size
										lowerCAmelCase_ :      Optional[Any]    				=				image_size
										lowerCAmelCase_ :      int    				=				patch_size
										lowerCAmelCase_ :      Optional[Any]    				=				num_channels
										lowerCAmelCase_ :      List[Any]    				=				is_training
										lowerCAmelCase_ :      Union[str, Any]    				=				use_labels
										lowerCAmelCase_ :      Any    				=				hidden_size
										lowerCAmelCase_ :      Tuple    				=				num_hidden_layers
										lowerCAmelCase_ :      str    				=				num_attention_heads
										lowerCAmelCase_ :      Optional[Any]    				=				intermediate_size
										lowerCAmelCase_ :      Tuple    				=				hidden_act
										lowerCAmelCase_ :      Optional[Any]    				=				hidden_dropout_prob
										lowerCAmelCase_ :      Optional[Any]    				=				attention_probs_dropout_prob
										lowerCAmelCase_ :      List[str]    				=				type_sequence_label_size
										lowerCAmelCase_ :      Dict    				=				initializer_range
										lowerCAmelCase_ :      List[Any]    				=				num_labels
										lowerCAmelCase_ :      str    				=				scope
										lowerCAmelCase_ :      Optional[int]    				=				n_targets
										lowerCAmelCase_ :      Optional[int]    				=				num_detection_tokens
										# we set the expected sequence length (which is used in several tests)
										# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
										lowerCAmelCase_ :      Any    				=				(image_size[1] // patch_size) * (image_size[0] // patch_size)
										lowerCAmelCase_ :      Optional[Any]    				=				num_patches + 1 + self.num_detection_tokens




						def 						lowercase_ (				self							)					->    List[Any]:
										lowerCAmelCase_ :      Optional[int]    				=				floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]							)

										lowerCAmelCase_ :      Dict    				=				None
										if self.use_labels:
														# labels is a list of Dict (each Dict being the labels for a given example in the batch)
														lowerCAmelCase_ :      Any    				=				[]
														for i in range(self.batch_size							):
																		lowerCAmelCase_ :      Optional[Any]    				=				{}
																		lowerCAmelCase_ :      Union[str, Any]    				=				torch.randint(
																		    high=self.num_labels   ,						size=(self.n_targets,)   ,						device=__lowercase							)
																		lowerCAmelCase_ :      str    				=				torch.rand(self.n_targets   ,						4   ,						device=__lowercase							)
																		labels.append(__lowercase							)

										lowerCAmelCase_ :      Dict    				=				self.get_config()

										return config, pixel_values, labels




						def 						lowercase_ (				self							)					->    Dict:
										return YolosConfig(
										    image_size=self.image_size   ,						patch_size=self.patch_size   ,						num_channels=self.num_channels   ,						hidden_size=self.hidden_size   ,						num_hidden_layers=self.num_hidden_layers   ,						num_attention_heads=self.num_attention_heads   ,						intermediate_size=self.intermediate_size   ,						hidden_act=self.hidden_act   ,						hidden_dropout_prob=self.hidden_dropout_prob   ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,						is_decoder=__lowercase   ,						initializer_range=self.initializer_range   ,						num_detection_tokens=self.num_detection_tokens   ,						num_labels=self.num_labels   ,						)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    str:
										lowerCAmelCase_ :      Tuple    				=				YolosModel(config=__lowercase							)
										model.to(__lowercase							)
										model.eval()
										lowerCAmelCase_ :      str    				=				model(__lowercase							)
										self.parent.assertEqual(
										    result.last_hidden_state.shape   ,						(self.batch_size, self.expected_seq_len, self.hidden_size)							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    Any:
										lowerCAmelCase_ :      List[str]    				=				YolosForObjectDetection(__lowercase							)
										model.to(__lowercase							)
										model.eval()

										lowerCAmelCase_ :      Optional[int]    				=				model(pixel_values=__lowercase							)
										lowerCAmelCase_ :      str    				=				model(__lowercase							)

										self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
										self.parent.assertEqual(result.pred_boxes.shape   ,						(self.batch_size, self.num_detection_tokens, 4)							)

										lowerCAmelCase_ :      List[Any]    				=				model(pixel_values=__lowercase   ,						labels=__lowercase							)

										self.parent.assertEqual(result.loss.shape   ,						()							)
										self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
										self.parent.assertEqual(result.pred_boxes.shape   ,						(self.batch_size, self.num_detection_tokens, 4)							)




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      Any    				=				self.prepare_config_and_inputs()
										lowerCAmelCase_ :      Optional[int]    				=				config_and_inputs
										lowerCAmelCase_ :      Union[str, Any]    				=				{'''pixel_values''': pixel_values}
										return config, inputs_dict







@require_torch
class 				snake_case__(							UpperCamelCase_,						UpperCamelCase_,						unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
						SCREAMING_SNAKE_CASE__      :  Union[str, Any]					=							(
						    {"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
						)

						SCREAMING_SNAKE_CASE__      :  Dict					=							False
						SCREAMING_SNAKE_CASE__      :  Dict					=							False
						SCREAMING_SNAKE_CASE__      :  Union[str, Any]					=							False
						SCREAMING_SNAKE_CASE__      :  Dict					=							False




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase=False							)					->    str:
										lowerCAmelCase_ :      Optional[int]    				=				super()._prepare_for_class(__lowercase   ,						__lowercase   ,						return_labels=__lowercase							)

										if return_labels:
														if model_class.__name__ == "YolosForObjectDetection":
																		lowerCAmelCase_ :      Any    				=				[]
																		for i in range(self.model_tester.batch_size							):
																						lowerCAmelCase_ :      List[Any]    				=				{}
																						lowerCAmelCase_ :      int    				=				torch.ones(
																						    size=(self.model_tester.n_targets,)   ,						device=__lowercase   ,						dtype=torch.long							)
																						lowerCAmelCase_ :      Union[str, Any]    				=				torch.ones(
																						    self.model_tester.n_targets   ,						4   ,						device=__lowercase   ,						dtype=torch.float							)
																						labels.append(__lowercase							)
																		lowerCAmelCase_ :      Union[str, Any]    				=				labels

										return inputs_dict




						def 						lowercase_ (				self							)					->    Dict:
										lowerCAmelCase_ :      str    				=				YolosModelTester(self							)
										lowerCAmelCase_ :      str    				=				ConfigTester(self   ,						config_class=__lowercase   ,						has_text_modality=__lowercase   ,						hidden_size=3_7							)




						def 						lowercase_ (				self							)					->    Dict:
										self.config_tester.run_common_tests()




						def 						lowercase_ (				self							)					->    int:
										# YOLOS does not use inputs_embeds
										pass




						def 						lowercase_ (				self							)					->    List[Any]:
										lowerCAmelCase_ :      Any    				=				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowerCAmelCase_ :      Dict    				=				model_class(__lowercase							)
														self.assertIsInstance(model.get_input_embeddings()   ,						(nn.Module)							)
														lowerCAmelCase_ :      List[str]    				=				model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(__lowercase   ,						nn.Linear							)							)




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										lowerCAmelCase_ :      Tuple    				=				self.model_tester.prepare_config_and_inputs_for_common()

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

														lowerCAmelCase_ :      List[str]    				=				['''pixel_values''']
														self.assertListEqual(arg_names[:1]   ,						__lowercase							)




						def 						lowercase_ (				self							)					->    Dict:
										lowerCAmelCase_ :      Optional[Any]    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowercase							)




						def 						lowercase_ (				self							)					->    Tuple:
										lowerCAmelCase_ :      Optional[int]    				=				self.model_tester.prepare_config_and_inputs_for_common()
										lowerCAmelCase_ :      Union[str, Any]    				=				True

										# in YOLOS, the seq_len is different
										lowerCAmelCase_ :      List[str]    				=				self.model_tester.expected_seq_len
										for model_class in self.all_model_classes:
														lowerCAmelCase_ :      List[Any]    				=				True
														lowerCAmelCase_ :      Optional[int]    				=				False
														lowerCAmelCase_ :      Any    				=				True
														lowerCAmelCase_ :      int    				=				model_class(__lowercase							)
														model.to(__lowercase							)
														model.eval()
														with torch.no_grad():
																		lowerCAmelCase_ :      int    				=				model(**self._prepare_for_class(__lowercase   ,						__lowercase							)							)
														lowerCAmelCase_ :      Optional[Any]    				=				outputs.attentions
														self.assertEqual(len(__lowercase							)   ,						self.model_tester.num_hidden_layers							)

														# check that output_attentions also work using config
														del inputs_dict["output_attentions"]
														lowerCAmelCase_ :      Optional[int]    				=				True
														lowerCAmelCase_ :      str    				=				model_class(__lowercase							)
														model.to(__lowercase							)
														model.eval()
														with torch.no_grad():
																		lowerCAmelCase_ :      List[Any]    				=				model(**self._prepare_for_class(__lowercase   ,						__lowercase							)							)
														lowerCAmelCase_ :      Any    				=				outputs.attentions
														self.assertEqual(len(__lowercase							)   ,						self.model_tester.num_hidden_layers							)

														self.assertListEqual(
														    list(attentions[0].shape[-3:]							)   ,						[self.model_tester.num_attention_heads, seq_len, seq_len]   ,						)
														lowerCAmelCase_ :      Optional[int]    				=				len(__lowercase							)

														# Check attention is always last and order is fine
														lowerCAmelCase_ :      Tuple    				=				True
														lowerCAmelCase_ :      Union[str, Any]    				=				True
														lowerCAmelCase_ :      Union[str, Any]    				=				model_class(__lowercase							)
														model.to(__lowercase							)
														model.eval()
														with torch.no_grad():
																		lowerCAmelCase_ :      Any    				=				model(**self._prepare_for_class(__lowercase   ,						__lowercase							)							)

														lowerCAmelCase_ :      Dict    				=				1
														self.assertEqual(out_len + added_hidden_states   ,						len(__lowercase							)							)

														lowerCAmelCase_ :      int    				=				outputs.attentions

														self.assertEqual(len(__lowercase							)   ,						self.model_tester.num_hidden_layers							)
														self.assertListEqual(
														    list(self_attentions[0].shape[-3:]							)   ,						[self.model_tester.num_attention_heads, seq_len, seq_len]   ,						)




						def 						lowercase_ (				self							)					->    Tuple:
										def check_hidden_states_output(__lowercase   ,						__lowercase   ,						__lowercase							):
														lowerCAmelCase_ :      List[Any]    				=				model_class(__lowercase							)
														model.to(__lowercase							)
														model.eval()

														with torch.no_grad():
																		lowerCAmelCase_ :      str    				=				model(**self._prepare_for_class(__lowercase   ,						__lowercase							)							)

														lowerCAmelCase_ :      Tuple    				=				outputs.hidden_states

														lowerCAmelCase_ :      Optional[int]    				=				getattr(
														    self.model_tester   ,						'''expected_num_hidden_layers'''   ,						self.model_tester.num_hidden_layers + 1							)
														self.assertEqual(len(__lowercase							)   ,						__lowercase							)

														# YOLOS has a different seq_length
														lowerCAmelCase_ :      List[str]    				=				self.model_tester.expected_seq_len

														self.assertListEqual(
														    list(hidden_states[0].shape[-2:]							)   ,						[seq_length, self.model_tester.hidden_size]   ,						)

										lowerCAmelCase_ :      Tuple    				=				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowerCAmelCase_ :      Union[str, Any]    				=				True
														check_hidden_states_output(__lowercase   ,						__lowercase   ,						__lowercase							)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														lowerCAmelCase_ :      str    				=				True

														check_hidden_states_output(__lowercase   ,						__lowercase   ,						__lowercase							)




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										lowerCAmelCase_ :      Any    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_object_detection(*__lowercase							)




						@slow
						def 						lowercase_ (				self							)					->    int:
										for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase_ :      Tuple    				=				YolosModel.from_pretrained(__lowercase							)
														self.assertIsNotNone(__lowercase							)






def 			lowerCAmelCase (		)-> Dict:
				lowerCAmelCase_ :      List[Any]    				=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
				return image







@require_torch
@require_vision
class 				snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						@cached_property
						def 						lowercase_ (				self							)					->    Any:
										return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''							) if is_vision_available() else None




						@slow
						def 						lowercase_ (				self							)					->    List[Any]:
										lowerCAmelCase_ :      int    				=				YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''							).to(__lowercase							)

										lowerCAmelCase_ :      Dict    				=				self.default_image_processor
										lowerCAmelCase_ :      List[Any]    				=				prepare_img()
										lowerCAmelCase_ :      Union[str, Any]    				=				image_processor(images=__lowercase   ,						return_tensors='''pt'''							).to(__lowercase							)

										# forward pass
										with torch.no_grad():
														lowerCAmelCase_ :      List[str]    				=				model(inputs.pixel_values							)

										# verify outputs
										lowerCAmelCase_ :      Union[str, Any]    				=				torch.Size((1, 1_0_0, 9_2)							)
										self.assertEqual(outputs.logits.shape   ,						__lowercase							)

										lowerCAmelCase_ :      Dict    				=				torch.tensor(
										    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]   ,						device=__lowercase   ,						)
										lowerCAmelCase_ :      Optional[Any]    				=				torch.tensor(
										    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]   ,						device=__lowercase							)
										self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]   ,						__lowercase   ,						atol=1e-4							)							)
										self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]   ,						__lowercase   ,						atol=1e-4							)							)

										# verify postprocessing
										lowerCAmelCase_ :      List[str]    				=				image_processor.post_process_object_detection(
										    __lowercase   ,						threshold=0.3   ,						target_sizes=[image.size[::-1]]							)[0]
										lowerCAmelCase_ :      List[str]    				=				torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61]							).to(__lowercase							)
										lowerCAmelCase_ :      str    				=				[7_5, 7_5, 1_7, 6_3, 1_7]
										lowerCAmelCase_ :      str    				=				torch.tensor([3_35.06_09, 79.38_48, 3_75.42_16, 1_87.24_95]							).to(__lowercase							)

										self.assertEqual(len(results['''scores''']							)   ,						5							)
										self.assertTrue(torch.allclose(results['''scores''']   ,						__lowercase   ,						atol=1e-4							)							)
										self.assertSequenceEqual(results['''labels'''].tolist()   ,						__lowercase							)
										self.assertTrue(torch.allclose(results['''boxes'''][0, :]   ,						__lowercase							)							)



import csv

import tweepy

# Twitter API credentials
_UpperCAmelCase			:  int     =""""""
_UpperCAmelCase			:  Optional[int]     =""""""
_UpperCAmelCase			:  Dict     =""""""
_UpperCAmelCase			:  str     =""""""



def 			lowerCAmelCase (		lowerCAmelCase_ )-> None:
				# authorize twitter, initialize tweepy
				lowerCAmelCase_ :      Optional[int]    				=				tweepy.OAuthHandler(lowerCAmelCase_ ,   lowerCAmelCase_ )
				auth.set_access_token(lowerCAmelCase_ ,   lowerCAmelCase_ )
				lowerCAmelCase_ :      Any    				=				tweepy.API(lowerCAmelCase_ )

				# initialize a list to hold all the tweepy Tweets
				lowerCAmelCase_ :      Dict    				=				[]

				# make initial request for most recent tweets (200 is the maximum allowed count)
				lowerCAmelCase_ :      Optional[int]    				=				api.user_timeline(screen_name=lowerCAmelCase_ ,   count=200 )

				# save most recent tweets
				alltweets.extend(lowerCAmelCase_ )

				# save the id of the oldest tweet less one
				lowerCAmelCase_ :      str    				=				alltweets[-1].id - 1

				# keep grabbing tweets until there are no tweets left to grab
				while len(lowerCAmelCase_ ) > 0:
								print(f"""getting tweets before {oldest}""" )

								# all subsequent requests use the max_id param to prevent duplicates
								lowerCAmelCase_ :      Optional[Any]    				=				api.user_timeline(
								    screen_name=lowerCAmelCase_ ,   count=200 ,   max_id=lowerCAmelCase_ )

								# save most recent tweets
								alltweets.extend(lowerCAmelCase_ )

								# update the id of the oldest tweet less one
								lowerCAmelCase_ :      Optional[Any]    				=				alltweets[-1].id - 1

								print(f"""...{len(lowerCAmelCase_ )} tweets downloaded so far""" )

				# transform the tweepy tweets into a 2D array that will populate the csv
				lowerCAmelCase_ :      Union[str, Any]    				=				[[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

				# write the csv
				with open(f"""new_{screen_name}_tweets.csv""" ,   '''w''' ) as f:
								lowerCAmelCase_ :      Optional[int]    				=				csv.writer(lowerCAmelCase_ )
								writer.writerow(['''id''', '''created_at''', '''text'''] )
								writer.writerows(lowerCAmelCase_ )


if __name__ == "__main__":
						# pass in the username of the account you want to download
						get_all_tweets("""FirePing32""")