import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
		import torch

		from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

		if is_vision_available():
				from transformers import MaskaFormerImageProcessor

if is_vision_available():
		from PIL import Image




class    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=10      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=32 * 8      ,   lowerCAmelCase__=32 * 8      ,   lowerCAmelCase__=4      ,   lowerCAmelCase__=64      ,   )     ->					Any:

							'''simple docstring'''


							__lowercase 			=							parent
							__lowercase 			=							batch_size
							__lowercase 			=							is_training
							__lowercase 			=							use_auxiliary_loss
							__lowercase 			=							num_queries
							__lowercase 			=							num_channels
							__lowercase 			=							min_size
							__lowercase 			=							max_size
							__lowercase 			=							num_labels
							__lowercase 			=							hidden_dim
							__lowercase 			=							hidden_dim





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
							    lowerCAmelCase__						)

							__lowercase 			=							torch.ones([self.batch_size, self.min_size, self.max_size]      ,   device=lowerCAmelCase__						)

							__lowercase 			=							(
							    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]      ,   device=lowerCAmelCase__						) > 0.5
							).float()
							__lowercase 			=							(torch.rand((self.batch_size, self.num_labels)      ,   device=lowerCAmelCase__						) > 0.5).long()

							__lowercase 			=							self.get_config()
							return config, pixel_values, pixel_mask, mask_labels, class_labels





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

							'''simple docstring'''


							__lowercase 			=							MaskaFormerConfig(
							    hidden_size=self.hidden_dim      ,   )
							__lowercase 			=							self.num_queries
							__lowercase 			=							self.num_labels

							__lowercase 			=							[1, 1, 1, 1]
							__lowercase 			=							self.num_channels

							__lowercase 			=							64
							__lowercase 			=							1_28
							__lowercase 			=							self.hidden_dim
							__lowercase 			=							self.hidden_dim
							__lowercase 			=							self.hidden_dim
							return config





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							__lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase 			=							self.prepare_config_and_inputs()
							__lowercase 			=							{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
							return config, inputs_dict





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					List[Any]:

							'''simple docstring'''


							__lowercase 			=							output.encoder_hidden_states
							__lowercase 			=							output.pixel_decoder_hidden_states
							__lowercase 			=							output.transformer_decoder_hidden_states

							self.parent.assertTrue(len(lowerCAmelCase__						)      ,   len(config.backbone_config.depths						)						)
							self.parent.assertTrue(len(lowerCAmelCase__						)      ,   len(config.backbone_config.depths						)						)
							self.parent.assertTrue(len(lowerCAmelCase__						)      ,   config.decoder_layers						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__=False						)     ->					Dict:

							'''simple docstring'''


							with torch.no_grad():
											__lowercase 			=							MaskaFormerModel(config=lowerCAmelCase__						)
											model.to(lowerCAmelCase__						)
											model.eval()

											__lowercase 			=							model(pixel_values=lowerCAmelCase__      ,   pixel_mask=lowerCAmelCase__						)
											__lowercase 			=							model(lowerCAmelCase__      ,   output_hidden_states=lowerCAmelCase__						)

							self.parent.assertEqual(
							    output.transformer_decoder_last_hidden_state.shape      ,   (self.batch_size, self.num_queries, self.hidden_dim)      ,   )
							# let's ensure the other two hidden state exists
							self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None						)
							self.parent.assertTrue(output.encoder_last_hidden_state is not None						)

							if output_hidden_states:
											self.check_output_hidden_state(lowerCAmelCase__      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							MaskaFormerForUniversalSegmentation(config=lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()

							def comm_check_on_output(lowerCAmelCase__						):
											# let's still check that all the required stuff is there
											self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None						)
											self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None						)
											self.parent.assertTrue(result.encoder_last_hidden_state is not None						)
											# okay, now we need to check the logits shape
											# due to the encoder compression, masks have a //4 spatial size
											self.parent.assertEqual(
											    result.masks_queries_logits.shape      ,   (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)      ,   )
											# + 1 for null class
											self.parent.assertEqual(
											    result.class_queries_logits.shape      ,   (self.batch_size, self.num_queries, self.num_labels + 1)						)

							with torch.no_grad():
											__lowercase 			=							model(pixel_values=lowerCAmelCase__      ,   pixel_mask=lowerCAmelCase__						)
											__lowercase 			=							model(lowerCAmelCase__						)

											comm_check_on_output(lowerCAmelCase__						)

											__lowercase 			=							model(
											    pixel_values=lowerCAmelCase__      ,   pixel_mask=lowerCAmelCase__      ,   mask_labels=lowerCAmelCase__      ,   class_labels=lowerCAmelCase__						)

							comm_check_on_output(lowerCAmelCase__						)

							self.parent.assertTrue(result.loss is not None						)
							self.parent.assertEqual(result.loss.shape      ,   torch.Size([1]						)						)




@require_torch
class    _UpperCamelCase      (						_UpperCAmelCase						,_UpperCAmelCase						,unittest.TestCase ):


			"""simple docstring"""






			__a  : Union[str, Any]			 =						(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
			__a  : Tuple			 =						{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

			__a  : Dict			 =						False
			__a  : Optional[Any]			 =						False
			__a  : Optional[Any]			 =						False
			__a  : Dict			 =						False





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

							'''simple docstring'''


							__lowercase 			=							MaskaFormerModelTester(self						)
							__lowercase 			=							ConfigTester(self      ,   config_class=lowerCAmelCase__      ,   has_text_modality=lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							self.config_tester.run_common_tests()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__      ,   **lowerCAmelCase__      ,   output_hidden_states=lowerCAmelCase__						)





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

							'''simple docstring'''


							__lowercase 			=							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCAmelCase__						)





			@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							pass





			@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							pass





			@unittest.skip(reason='''Mask2Former is not a generative model'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							pass





			@unittest.skip(reason='''Mask2Former does not use token embeddings'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							pass





			@require_torch_multi_gpu
			@unittest.skip(
			    reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							pass





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

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()

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

											__lowercase 			=							['''pixel_values''']
											self.assertListEqual(arg_names[:1]      ,   lowerCAmelCase__						)





			@slow
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
											__lowercase 			=							MaskaFormerModel.from_pretrained(lowerCAmelCase__						)
											self.assertIsNotNone(lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							(self.model_tester.min_size,) * 2
							__lowercase 			=							{
							    '''pixel_values''': torch.randn((2, 3, *size)      ,   device=lowerCAmelCase__						),
							    '''mask_labels''': torch.randn((2, 10, *size)      ,   device=lowerCAmelCase__						),
							    '''class_labels''': torch.zeros(2      ,   10      ,   device=lowerCAmelCase__						).long(),
							}
							__lowercase 			=							self.model_tester.get_config()

							__lowercase 			=							MaskaFormerForUniversalSegmentation(lowerCAmelCase__						).to(lowerCAmelCase__						)
							__lowercase 			=							model(**lowerCAmelCase__						)
							self.assertTrue(outputs.loss is not None						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__      ,   **lowerCAmelCase__      ,   output_hidden_states=lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowercase 			=							model_class(lowerCAmelCase__						).to(lowerCAmelCase__						)
											__lowercase 			=							model(**lowerCAmelCase__      ,   output_attentions=lowerCAmelCase__						)
											self.assertTrue(outputs.attentions is not None						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							if not self.model_tester.is_training:
											return

							__lowercase 			=							self.all_model_classes[1]
							__lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs()

							__lowercase 			=							model_class(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.train()

							__lowercase 			=							model(lowerCAmelCase__      ,   mask_labels=lowerCAmelCase__      ,   class_labels=lowerCAmelCase__						).loss
							loss.backward()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							self.all_model_classes[1]
							__lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs()
							__lowercase 			=							True
							__lowercase 			=							True

							__lowercase 			=							model_class(lowerCAmelCase__						).to(lowerCAmelCase__						)
							model.train()

							__lowercase 			=							model(lowerCAmelCase__      ,   mask_labels=lowerCAmelCase__      ,   class_labels=lowerCAmelCase__						)

							__lowercase 			=							outputs.encoder_hidden_states[0]
							encoder_hidden_states.retain_grad()

							__lowercase 			=							outputs.pixel_decoder_hidden_states[0]
							pixel_decoder_hidden_states.retain_grad()

							__lowercase 			=							outputs.transformer_decoder_hidden_states[0]
							transformer_decoder_hidden_states.retain_grad()

							__lowercase 			=							outputs.attentions[0]
							attentions.retain_grad()

							outputs.loss.backward(retain_graph=lowerCAmelCase__						)

							self.assertIsNotNone(encoder_hidden_states.grad						)
							self.assertIsNotNone(pixel_decoder_hidden_states.grad						)
							self.assertIsNotNone(transformer_decoder_hidden_states.grad						)
							self.assertIsNotNone(attentions.grad						)


__a  :     Union[str, Any]       =  1e-4





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
				return image




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


			"""simple docstring"""






			@cached_property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[int]:

							'''simple docstring'''


							return "facebook/mask2former-swin-small-coco-instance"





			@cached_property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints						) if is_vision_available() else None





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							MaskaFormerModel.from_pretrained(self.model_checkpoints						).to(lowerCAmelCase__						)
							__lowercase 			=							self.default_image_processor
							__lowercase 			=							prepare_img()
							__lowercase 			=							image_processor(lowerCAmelCase__      ,   return_tensors='''pt'''						).to(lowerCAmelCase__						)
							__lowercase 			=							inputs['''pixel_values'''].shape
							# check size is divisible by 32
							self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
							# check size
							self.assertEqual(lowerCAmelCase__      ,   (1, 3, 3_84, 3_84)						)

							with torch.no_grad():
											__lowercase 			=							model(**lowerCAmelCase__						)

							__lowercase 			=							torch.tensor(
							    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]						).to(lowerCAmelCase__						)
							self.assertTrue(
							    torch.allclose(
							        outputs.encoder_last_hidden_state[0, 0, :3, :3]      ,   lowerCAmelCase__      ,   atol=lowerCAmelCase__						)						)

							__lowercase 			=							torch.tensor(
							    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]						).to(lowerCAmelCase__						)
							self.assertTrue(
							    torch.allclose(
							        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]      ,   lowerCAmelCase__      ,   atol=lowerCAmelCase__						)						)

							__lowercase 			=							torch.tensor(
							    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]						).to(lowerCAmelCase__						)
							self.assertTrue(
							    torch.allclose(
							        outputs.transformer_decoder_last_hidden_state[0, :3, :3]      ,   lowerCAmelCase__      ,   atol=lowerCAmelCase__						)						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(lowerCAmelCase__						).eval()
							__lowercase 			=							self.default_image_processor
							__lowercase 			=							prepare_img()
							__lowercase 			=							image_processor(lowerCAmelCase__      ,   return_tensors='''pt'''						).to(lowerCAmelCase__						)
							__lowercase 			=							inputs['''pixel_values'''].shape
							# check size is divisible by 32
							self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
							# check size
							self.assertEqual(lowerCAmelCase__      ,   (1, 3, 3_84, 3_84)						)

							with torch.no_grad():
											__lowercase 			=							model(**lowerCAmelCase__						)
							# masks_queries_logits
							__lowercase 			=							outputs.masks_queries_logits
							self.assertEqual(
							    masks_queries_logits.shape      ,   (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						)
							__lowercase 			=							[
							    [-8.7839, -9.0056, -8.8121],
							    [-7.4104, -7.0313, -6.5401],
							    [-6.6105, -6.3427, -6.4675],
							]
							__lowercase 			=							torch.tensor(lowerCAmelCase__						).to(lowerCAmelCase__						)
							self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,   lowerCAmelCase__      ,   atol=lowerCAmelCase__						)						)
							# class_queries_logits
							__lowercase 			=							outputs.class_queries_logits
							self.assertEqual(class_queries_logits.shape      ,   (1, model.config.num_queries, model.config.num_labels + 1)						)
							__lowercase 			=							torch.tensor(
							    [
							        [1.8324, -8.0835, -4.1922],
							        [0.8450, -9.0050, -3.6053],
							        [0.3045, -7.7293, -3.0275],
							    ]						).to(lowerCAmelCase__						)
							self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,   lowerCAmelCase__      ,   atol=lowerCAmelCase__						)						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(lowerCAmelCase__						).eval()
							__lowercase 			=							self.default_image_processor

							__lowercase 			=							image_processor(
							    [np.zeros((3, 8_00, 13_33)						), np.zeros((3, 8_00, 13_33)						)]      ,   segmentation_maps=[np.zeros((3_84, 3_84)						).astype(np.floataa						), np.zeros((3_84, 3_84)						).astype(np.floataa						)]      ,   return_tensors='''pt'''      ,   )

							__lowercase 			=							inputs['''pixel_values'''].to(lowerCAmelCase__						)
							__lowercase 			=							[el.to(lowerCAmelCase__						) for el in inputs['''mask_labels''']]
							__lowercase 			=							[el.to(lowerCAmelCase__						) for el in inputs['''class_labels''']]

							with torch.no_grad():
											__lowercase 			=							model(**lowerCAmelCase__						)

							self.assertTrue(outputs.loss is not None						)
import csv

import tweepy

# Twitter API credentials
__a  :     Union[str, Any]       =  """"""
__a  :     Union[str, Any]       =  """"""
__a  :     Union[str, Any]       =  """"""
__a  :     List[Any]       =  """"""





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							tweepy.OAuthHandler(lowercase							,			lowercase							)
				auth.set_access_token(lowercase							,			lowercase							)
				__lowercase 			=							tweepy.API(lowercase							)

				# initialize a list to hold all the tweepy Tweets
				__lowercase 			=							[]

				# make initial request for most recent tweets (200 is the maximum allowed count)
				__lowercase 			=							api.user_timeline(screen_name=lowercase							,			count=200							)

				# save most recent tweets
				alltweets.extend(lowercase							)

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

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

								# all subsequent requests use the max_id param to prevent duplicates
								__lowercase 			=							api.user_timeline(
								    screen_name=lowercase							,			count=200							,			max_id=lowercase							)

								# save most recent tweets
								alltweets.extend(lowercase							)

								# update the id of the oldest tweet less one
								__lowercase 			=							alltweets[-1].id - 1

								print(F"...{len(lowercase							)} tweets downloaded so far"							)

				# transform the tweepy tweets into a 2D array that will populate the csv
				__lowercase 			=							[[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:
								__lowercase 			=							csv.writer(lowercase							)
								writer.writerow(['''id''', '''created_at''', '''text''']							)
								writer.writerows(lowercase							)


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