import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
		from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor




class    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=13      ,   lowerCAmelCase__=30      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=32      ,   lowerCAmelCase__=5      ,   lowerCAmelCase__=4      ,   lowerCAmelCase__=37      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=10      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=None      ,   lowerCAmelCase__=2      ,   )     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							parent
							__lowercase 			=							batch_size
							__lowercase 			=							image_size
							__lowercase 			=							patch_size
							__lowercase 			=							num_channels
							__lowercase 			=							is_training
							__lowercase 			=							use_labels
							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							num_attention_heads
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_act
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							type_sequence_label_size
							__lowercase 			=							initializer_range
							__lowercase 			=							scope
							__lowercase 			=							encoder_stride

							# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							__lowercase 			=							(image_size // patch_size) ** 2
							__lowercase 			=							num_patches + 1





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


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

							__lowercase 			=							None
							if self.use_labels:
											__lowercase 			=							ids_tensor([self.batch_size]      ,   self.type_sequence_label_size						)

							__lowercase 			=							self.get_config()

							return config, pixel_values, labels





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

							'''simple docstring'''


							return ViTConfig(
							    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=lowerCAmelCase__      ,   initializer_range=self.initializer_range      ,   encoder_stride=self.encoder_stride      ,   )





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

							'''simple docstring'''


							__lowercase 			=							ViTModel(config=lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							__lowercase 			=							model(lowerCAmelCase__						)
							self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)						)





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

							'''simple docstring'''


							__lowercase 			=							ViTForMaskedImageModeling(config=lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							__lowercase 			=							model(lowerCAmelCase__						)
							self.parent.assertEqual(
							    result.reconstruction.shape      ,   (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

							# test greyscale images
							__lowercase 			=							1
							__lowercase 			=							ViTForMaskedImageModeling(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()

							__lowercase 			=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
							__lowercase 			=							model(lowerCAmelCase__						)
							self.parent.assertEqual(result.reconstruction.shape      ,   (self.batch_size, 1, self.image_size, self.image_size)						)





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

							'''simple docstring'''


							__lowercase 			=							self.type_sequence_label_size
							__lowercase 			=							ViTForImageClassification(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							__lowercase 			=							model(lowerCAmelCase__      ,   labels=lowerCAmelCase__						)
							self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size)						)

							# test greyscale images
							__lowercase 			=							1
							__lowercase 			=							ViTForImageClassification(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()

							__lowercase 			=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
							__lowercase 			=							model(lowerCAmelCase__						)
							self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size)						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


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




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


			"""simple docstring"""






			__a  : Union[str, Any]			 =						(
			    (
			        ViTModel,
			        ViTForImageClassification,
			        ViTForMaskedImageModeling,
			    )
			    if is_torch_available()
			    else ()
			)
			__a  : List[Any]			 =						(
			    {'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
			    if is_torch_available()
			    else {}
			)
			__a  : List[Any]			 =						True

			__a  : Any			 =						False
			__a  : Tuple			 =						False
			__a  : str			 =						False





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

							'''simple docstring'''


							__lowercase 			=							ViTModelTester(self						)
							__lowercase 			=							ConfigTester(self      ,   config_class=lowerCAmelCase__      ,   has_text_modality=lowerCAmelCase__      ,   hidden_size=37						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							self.config_tester.run_common_tests()





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

							'''simple docstring'''


							pass





			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__						)
											self.assertIsInstance(model.get_input_embeddings()      ,   (nn.Module)						)
											__lowercase 			=							model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(lowerCAmelCase__      ,   nn.Linear						)						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[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__						)





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

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


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





			@slow
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowercase 			=							ViTModel.from_pretrained(lowerCAmelCase__						)
											self.assertIsNotNone(lowerCAmelCase__						)





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



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




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


			"""simple docstring"""






			@cached_property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''						) if is_vision_available() else None





			@slow
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''						).to(lowerCAmelCase__						)

							__lowercase 			=							self.default_image_processor
							__lowercase 			=							prepare_img()
							__lowercase 			=							image_processor(images=lowerCAmelCase__      ,   return_tensors='''pt'''						).to(lowerCAmelCase__						)

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

							# verify the logits
							__lowercase 			=							torch.Size((1, 10_00)						)
							self.assertEqual(outputs.logits.shape      ,   lowerCAmelCase__						)

							__lowercase 			=							torch.tensor([-0.2744, 0.8215, -0.0836]						).to(lowerCAmelCase__						)

							self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,   lowerCAmelCase__      ,   atol=1E-4						)						)





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

							'''simple docstring'''


							__lowercase 			=							ViTModel.from_pretrained('''facebook/dino-vits8'''						).to(lowerCAmelCase__						)

							__lowercase 			=							ViTImageProcessor.from_pretrained('''facebook/dino-vits8'''      ,   size=4_80						)
							__lowercase 			=							prepare_img()
							__lowercase 			=							image_processor(images=lowerCAmelCase__      ,   return_tensors='''pt'''						)
							__lowercase 			=							inputs.pixel_values.to(lowerCAmelCase__						)

							# forward pass
							with torch.no_grad():
											__lowercase 			=							model(lowerCAmelCase__      ,   interpolate_pos_encoding=lowerCAmelCase__						)

							# verify the logits
							__lowercase 			=							torch.Size((1, 36_01, 3_84)						)
							self.assertEqual(outputs.last_hidden_state.shape      ,   lowerCAmelCase__						)

							__lowercase 			=							torch.tensor(
							    [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]						).to(lowerCAmelCase__						)

							self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]      ,   lowerCAmelCase__      ,   atol=1E-4						)						)





			@slow
			@require_accelerate
			@require_torch_gpu
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							ViTModel.from_pretrained('''facebook/dino-vits8'''      ,   torch_dtype=torch.floataa      ,   device_map='''auto'''						)
							__lowercase 			=							self.default_image_processor

							__lowercase 			=							prepare_img()
							__lowercase 			=							image_processor(images=lowerCAmelCase__      ,   return_tensors='''pt'''						)
							__lowercase 			=							inputs.pixel_values.to(lowerCAmelCase__						)

							# forward pass to make sure inference works in fp16
							with torch.no_grad():
											__lowercase 			=							model(lowerCAmelCase__						)
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""")