import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwiftFormerForImageClassification, SwiftFormerModel
	from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor

class 	UpperCamelCase    :



				def __init__(self       :   Any			,						_A       :   Union[str, Any]			,						_A       :   Optional[int]=13			,						_A       :   str=3			,						_A       :   Dict=True			,						_A       :   Union[str, Any]=True			,						_A       :   Any=0.1			,						_A       :   Union[str, Any]=0.1			,						_A       :   str=2_24			,						_A       :   Dict=10_00			,						_A       :   Optional[Any]=[3, 3, 6, 4]			,						_A       :   int=[48, 56, 1_12, 2_20]			,						)					->			int:
					__snake_case    :							Tuple   	=				parent
					__snake_case    :							str   	=				batch_size
					__snake_case    :							int   	=				num_channels
					__snake_case    :							Union[str, Any]   	=				is_training
					__snake_case    :							Tuple   	=				use_labels
					__snake_case    :							List[str]   	=				hidden_dropout_prob
					__snake_case    :							Any   	=				attention_probs_dropout_prob
					__snake_case    :							Dict   	=				num_labels
					__snake_case    :							List[Any]   	=				image_size
					__snake_case    :							List[Any]   	=				layer_depths
					__snake_case    :							str   	=				embed_dims



				def       _lowercase  (self       :   Tuple)					->			str:
					__snake_case    :							Tuple   	=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

					__snake_case    :							int   	=				None
					if self.use_labels:
						__snake_case    :							int   	=				ids_tensor([self.batch_size]			,						self.num_labels)

					__snake_case    :							Tuple   	=				self.get_config()

					return config, pixel_values, labels



				def       _lowercase  (self       :   Optional[int])					->			str:
					return SwiftFormerConfig(
					    depths=self.layer_depths			,						embed_dims=self.embed_dims			,						mlp_ratio=4			,						downsamples=[True, True, True, True]			,						hidden_act='gelu'			,						num_labels=self.num_labels			,						down_patch_size=3			,						down_stride=2			,						down_pad=1			,						drop_rate=0.0			,						drop_path_rate=0.0			,						use_layer_scale=A__			,						layer_scale_init_value=1E-5			,						)



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   Any			,						_A       :   Optional[Any]			,						_A       :   List[Any])					->			Union[str, Any]:
					__snake_case    :							List[Any]   	=				SwiftFormerModel(config=A__)
					model.to(A__)
					model.eval()
					__snake_case    :							List[Any]   	=				model(A__)
					self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.embed_dims[-1], 7, 7))



				def       _lowercase  (self       :   int			,						_A       :   List[str]			,						_A       :   Any			,						_A       :   Dict)					->			int:
					__snake_case    :							Tuple   	=				self.num_labels
					__snake_case    :							Tuple   	=				SwiftFormerForImageClassification(A__)
					model.to(A__)
					model.eval()
					__snake_case    :							int   	=				model(A__			,						labels=A__)
					self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels))

					__snake_case    :							List[str]   	=				SwiftFormerForImageClassification(A__)
					model.to(A__)
					model.eval()

					__snake_case    :							Any   	=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
					__snake_case    :							Optional[int]   	=				model(A__)
					self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels))






				def       _lowercase  (self       :   List[str])					->			str:
					(__snake_case)    :							Tuple   	=				self.prepare_config_and_inputs()
					__snake_case    :							Optional[int]   	=				{"""pixel_values""": pixel_values}
					return config, inputs_dict







@require_torch
class 	UpperCamelCase    (					lowercase   ,			lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Dict					=	(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
				UpperCAmelCase       :   List[Any]					=	(
				    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
				    if is_torch_available()
				    else {}
				)

				UpperCAmelCase       :   Dict					=	False
				UpperCAmelCase       :   Union[str, Any]					=	False
				UpperCAmelCase       :   Any					=	False
				UpperCAmelCase       :   int					=	False
				UpperCAmelCase       :   Any					=	False



				def       _lowercase  (self       :   Any)					->			List[Any]:
					__snake_case    :							List[str]   	=				SwiftFormerModelTester(self)
					__snake_case    :							int   	=				ConfigTester(
					    self			,						config_class=A__			,						has_text_modality=A__			,						hidden_size=37			,						num_attention_heads=12			,						num_hidden_layers=12			,						)



				def       _lowercase  (self       :   List[str])					->			Optional[int]:
					self.config_tester.run_common_tests()



				@unittest.skip(reason='SwiftFormer does not use inputs_embeds')
				def       _lowercase  (self       :   List[Any])					->			Optional[Any]:
					pass



				def       _lowercase  (self       :   Any)					->			Optional[Any]:
					__snake_case    :							Dict   	=				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						__snake_case    :							List[str]   	=				model_class(A__)
						__snake_case    :							Dict   	=				model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(A__			,						nn.Linear))



				def       _lowercase  (self       :   Any)					->			Tuple:
					__snake_case    :							Tuple   	=				self.model_tester.prepare_config_and_inputs_for_common()

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

						__snake_case    :							List[str]   	=				["""pixel_values"""]
						self.assertListEqual(arg_names[:1]			,						A__)



				def       _lowercase  (self       :   Any)					->			Tuple:
					__snake_case    :							Dict   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A__)



				def       _lowercase  (self       :   Union[str, Any])					->			Dict:
					__snake_case    :							Any   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*A__)



				@slow
				def       _lowercase  (self       :   List[str])					->			int:
					for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case    :							Tuple   	=				SwiftFormerModel.from_pretrained(A__)
						self.assertIsNotNone(A__)



				@unittest.skip(reason='SwiftFormer does not output attentions')
				def       _lowercase  (self       :   Optional[int])					->			Optional[int]:
					pass



				def       _lowercase  (self       :   int)					->			List[Any]:
					def check_hidden_states_output(_A       :   Optional[Any]			,						_A       :   str			,						_A       :   Optional[Any]):
						__snake_case    :							Any   	=				model_class(A__)
						model.to(A__)
						model.eval()

						with torch.no_grad():
							__snake_case    :							Any   	=				model(**self._prepare_for_class(A__			,						A__))

						__snake_case    :							str   	=				outputs.hidden_states

						__snake_case    :							Tuple   	=				8
						self.assertEqual(len(A__)			,						A__)  # TODO

						# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
						# with the width and height being successively divided by 2, after every 2 blocks
						for i in range(len(A__)):
							self.assertEqual(
							    hidden_states[i].shape			,						torch.Size(
							        [
							            self.model_tester.batch_size,
							            self.model_tester.embed_dims[i // 2],
							            (self.model_tester.image_size // 4) // 2 ** (i // 2),
							            (self.model_tester.image_size // 4) // 2 ** (i // 2),
							        ])			,						)

					__snake_case    :							Dict   	=				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						__snake_case    :							Any   	=				True
						check_hidden_states_output(A__			,						A__			,						A__)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						__snake_case    :							List[Any]   	=				True

						check_hidden_states_output(A__			,						A__			,						A__)



				def       _lowercase  (self       :   Optional[Any])					->			Optional[int]:
					def _config_zero_init(_A       :   int):
						__snake_case    :							str   	=				copy.deepcopy(A__)
						for key in configs_no_init.__dict__.keys():
							if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
								setattr(A__			,						A__			,						1E-10)
							if isinstance(getattr(A__			,						A__			,						A__)			,						A__):
								__snake_case    :							Optional[Any]   	=				_config_zero_init(getattr(A__			,						A__))
								setattr(A__			,						A__			,						A__)
						return configs_no_init

					__snake_case    :							str   	=				self.model_tester.prepare_config_and_inputs_for_common()

					__snake_case    :							List[Any]   	=				_config_zero_init(A__)
					for model_class in self.all_model_classes:
						__snake_case    :							str   	=				model_class(config=A__)
						for name, param in model.named_parameters():
							if param.requires_grad:
								self.assertIn(
								    ((param.data.mean() * 1E9) / 1E9).round().item()			,						[0.0, 1.0]			,						msg=f"Parameter {name} of model {model_class} seems not properly initialized"			,						)






				@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
				def       _lowercase  (self       :   Dict)					->			Union[str, Any]:
					pass







def 						__UpperCAmelCase       (					)							->    Optional[Any]:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
	return image

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



				@cached_property
				def       _lowercase  (self       :   int)					->			int:
					return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs') if is_vision_available() else None






				@slow
				def       _lowercase  (self       :   int)					->			Union[str, Any]:
					__snake_case    :							List[Any]   	=				SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs').to(A__)

					__snake_case    :							Any   	=				self.default_image_processor
					__snake_case    :							List[Any]   	=				prepare_img()
					__snake_case    :							Optional[Any]   	=				image_processor(images=A__			,						return_tensors='pt').to(A__)

					# forward pass
					with torch.no_grad():
						__snake_case    :							Dict   	=				model(**A__)

					# verify the logits
					__snake_case    :							Union[str, Any]   	=				torch.Size((1, 10_00))
					self.assertEqual(outputs.logits.shape			,						A__)

					__snake_case    :							Any   	=				torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]).to(A__)
					self.assertTrue(torch.allclose(outputs.logits[0, :3]			,						A__			,						atol=1E-4))






"""simple docstring"""







import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   List[Any]					=	MvpTokenizer
				UpperCAmelCase       :   int					=	MvpTokenizerFast
				UpperCAmelCase       :   Optional[Any]					=	True
				UpperCAmelCase       :   Tuple					=	filter_roberta_detectors



				def       _lowercase  (self       :   Any)					->			str:
					super().setUp()
					__snake_case    :							Optional[Any]   	=				[
					    'l',
					    'o',
					    'w',
					    'e',
					    'r',
					    's',
					    't',
					    'i',
					    'd',
					    'n',
					    '\u0120',
					    '\u0120l',
					    '\u0120n',
					    '\u0120lo',
					    '\u0120low',
					    'er',
					    '\u0120lowest',
					    '\u0120newer',
					    '\u0120wider',
					    '<unk>',
					]
					__snake_case    :							List[Any]   	=				dict(zip(_A			,						range(len(_A))))
					__snake_case    :							Optional[Any]   	=				['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
					__snake_case    :							Optional[Any]   	=				{'unk_token': '<unk>'}

					__snake_case    :							int   	=				os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['vocab_file'])
					__snake_case    :							str   	=				os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['merges_file'])
					with open(self.vocab_file			,						'w'			,						encoding='utf-8') as fp:
						fp.write(json.dumps(_A) + '\n')
					with open(self.merges_file			,						'w'			,						encoding='utf-8') as fp:
						fp.write('\n'.join(_A))



				def       _lowercase  (self       :   List[str]			,						**_A       :   Any)					->			str:
					kwargs.update(self.special_tokens_map)
					return self.tokenizer_class.from_pretrained(self.tmpdirname			,						**_A)



				def       _lowercase  (self       :   Optional[Any]			,						**_A       :   Tuple)					->			Any:
					kwargs.update(self.special_tokens_map)
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,						**_A)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			Optional[Any]:
					return "lower newer", "lower newer"



				@cached_property
				def       _lowercase  (self       :   Tuple)					->			int:
					return MvpTokenizer.from_pretrained('RUCAIBox/mvp')



				@cached_property
				def       _lowercase  (self       :   int)					->			Tuple:
					return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp')



				@require_torch
				def       _lowercase  (self       :   Optional[int])					->			List[Any]:
					__snake_case    :							Optional[int]   	=				['A long paragraph for summarization.', 'Another paragraph for summarization.']
					__snake_case    :							Any   	=				[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							str   	=				tokenizer(_A			,						max_length=len(_A)			,						padding=_A			,						return_tensors='pt')
						self.assertIsInstance(_A			,						_A)

						self.assertEqual((2, 9)			,						batch.input_ids.shape)
						self.assertEqual((2, 9)			,						batch.attention_mask.shape)
						__snake_case    :							List[str]   	=				batch.input_ids.tolist()[0]
						self.assertListEqual(_A			,						_A)
						# Test that special tokens are reset



				@require_torch
				def       _lowercase  (self       :   List[str])					->			Any:
					__snake_case    :							List[Any]   	=				['A long paragraph for summarization.', 'Another paragraph for summarization.']
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Tuple   	=				tokenizer(_A			,						padding=_A			,						return_tensors='pt')
						# check if input_ids are returned and no labels
						self.assertIn('input_ids'			,						_A)
						self.assertIn('attention_mask'			,						_A)
						self.assertNotIn('labels'			,						_A)
						self.assertNotIn('decoder_attention_mask'			,						_A)



				@require_torch
				def       _lowercase  (self       :   List[Any])					->			Tuple:
					__snake_case    :							List[Any]   	=				[
					    'Summary of the text.',
					    'Another summary.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Any   	=				tokenizer(text_target=_A			,						max_length=32			,						padding='max_length'			,						return_tensors='pt')
						self.assertEqual(32			,						targets['input_ids'].shape[1])



				@require_torch
				def       _lowercase  (self       :   Tuple)					->			Dict:
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Dict   	=				tokenizer(
						    ['I am a small frog' * 10_24, 'I am a small frog']			,						padding=_A			,						truncation=_A			,						return_tensors='pt')
						self.assertIsInstance(_A			,						_A)
						self.assertEqual(batch.input_ids.shape			,						(2, 10_24))



				@require_torch
				def       _lowercase  (self       :   Optional[int])					->			Dict:
					__snake_case    :							List[Any]   	=				['A long paragraph for summarization.']
					__snake_case    :							str   	=				[
					    'Summary of the text.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Any   	=				tokenizer(_A			,						text_target=_A			,						return_tensors='pt')
						__snake_case    :							str   	=				inputs['input_ids']
						__snake_case    :							Optional[Any]   	=				inputs['labels']
						self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
						self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
						self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
						self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



				def       _lowercase  (self       :   str)					->			Tuple:
					pass






				def       _lowercase  (self       :   str)					->			Any:
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
							__snake_case    :							Any   	=				self.rust_tokenizer_class.from_pretrained(_A			,						**_A)
							__snake_case    :							List[Any]   	=				self.tokenizer_class.from_pretrained(_A			,						**_A)
							__snake_case    :							List[Any]   	=				'A, <mask> AllenNLP sentence.'
							__snake_case    :							List[Any]   	=				tokenizer_r.encode_plus(_A			,						add_special_tokens=_A			,						return_token_type_ids=_A)
							__snake_case    :							int   	=				tokenizer_p.encode_plus(_A			,						add_special_tokens=_A			,						return_token_type_ids=_A)

							# token_type_ids should put 0 everywhere
							self.assertEqual(sum(tokens_r['token_type_ids'])			,						sum(tokens_p['token_type_ids']))

							# attention_mask should put 1 everywhere, so sum over length should be 1
							self.assertEqual(
							    sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])			,						sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])			,						)

							__snake_case    :							Tuple   	=				tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
							__snake_case    :							Union[str, Any]   	=				tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])

							# Rust correctly handles the space before the mask while python doesnt
							self.assertSequenceEqual(tokens_p['input_ids']			,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
							self.assertSequenceEqual(tokens_r['input_ids']			,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])

							self.assertSequenceEqual(
							    _A			,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
							self.assertSequenceEqual(
							    _A			,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])





