



import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
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 transformers import (
						    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_MAPPING,
						    EfficientFormerForImageClassification,
						    EfficientFormerForImageClassificationWithTeacher,
						    EfficientFormerModel,
						)
						from transformers.models.efficientformer.modeling_efficientformer import (
						    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						)


if is_vision_available():
						from PIL import Image

						from transformers import EfficientFormerImageProcessor






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self					:			Optional[int]							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			int = 1_3							,					lowerCAmelCase__					:			int = 6_4							,					lowerCAmelCase__					:			int = 2							,					lowerCAmelCase__					:			int = 3							,					lowerCAmelCase__					:			int = 3							,					lowerCAmelCase__					:			bool = True							,					lowerCAmelCase__					:			bool = True							,					lowerCAmelCase__					:			int = 1_2_8							,					lowerCAmelCase__					:			Union[str, Any]=[1_6, 3_2, 6_4, 1_2_8]							,					lowerCAmelCase__					:			int = 7							,					lowerCAmelCase__					:			int = 4							,					lowerCAmelCase__					:			int = 3_7							,					lowerCAmelCase__					:			str = "gelu"							,					lowerCAmelCase__					:			float = 0.1							,					lowerCAmelCase__					:			float = 0.1							,					lowerCAmelCase__					:			int = 1_0							,					lowerCAmelCase__					:			float = 0.02							,					lowerCAmelCase__					:			int = 2							,					lowerCAmelCase__					:			int = 1							,					lowerCAmelCase__					:			int = 1_2_8							,					lowerCAmelCase__					:			List[int] = [2, 2, 2, 2]							,					lowerCAmelCase__					:			int = 2							,					lowerCAmelCase__					:			int = 2							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		parent
					__SCREAMING_SNAKE_CASE	:		Dict   	=		batch_size
					__SCREAMING_SNAKE_CASE	:		int   	=		image_size
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		patch_size
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		num_channels
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		is_training
					__SCREAMING_SNAKE_CASE	:		int   	=		use_labels
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		hidden_size
					__SCREAMING_SNAKE_CASE	:		str   	=		num_hidden_layers
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		num_attention_heads
					__SCREAMING_SNAKE_CASE	:		Dict   	=		intermediate_size
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		hidden_act
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		hidden_dropout_prob
					__SCREAMING_SNAKE_CASE	:		int   	=		attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		type_sequence_label_size
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		initializer_range
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		encoder_stride
					__SCREAMING_SNAKE_CASE	:		Any   	=		num_attention_outputs
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		embed_dim
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		embed_dim + 1
					__SCREAMING_SNAKE_CASE	:		str   	=		resolution
					__SCREAMING_SNAKE_CASE	:		Any   	=		depths
					__SCREAMING_SNAKE_CASE	:		Any   	=		hidden_sizes
					__SCREAMING_SNAKE_CASE	:		Dict   	=		dim
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		mlp_expansion_ratio

			def UpperCamelCase__ (							self					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		None
					if self.use_labels:
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		ids_tensor([self.batch_size]							,					self.type_sequence_label_size						)

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.get_config()
					return config, pixel_values, labels

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					return EfficientFormerConfig(
					    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							,					encoder_stride=self.encoder_stride							,					resolution=self.resolution							,					depths=self.depths							,					hidden_sizes=self.hidden_sizes							,					dim=self.dim							,					mlp_expansion_ratio=self.mlp_expansion_ratio							,					)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			Union[str, Any]							,					lowerCAmelCase__					:			List[str]							,					lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		EfficientFormerModel(config=__lowercase						)
					model.to(__lowercase						)
					model.eval()
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model(__lowercase						)
					self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)						)

			def UpperCamelCase__ (							self					:			Optional[int]							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.type_sequence_label_size
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		EfficientFormerForImageClassification(__lowercase						)
					model.to(__lowercase						)
					model.eval()
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model(__lowercase							,					labels=__lowercase						)
					self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)						)

					# test greyscale images
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		1
					__SCREAMING_SNAKE_CASE	:		Dict   	=		EfficientFormerForImageClassification(__lowercase						)
					model.to(__lowercase						)
					model.eval()

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		model(__lowercase						)
					self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)						)



			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.prepare_config_and_inputs()
					(
					    (
					    __SCREAMING_SNAKE_CASE				
					)	,						(
					    __SCREAMING_SNAKE_CASE				
					)	,						(
					    __SCREAMING_SNAKE_CASE				
					)	,										
					)	:		Tuple   	=		config_and_inputs
					__SCREAMING_SNAKE_CASE	:		Dict   	=		{"""pixel_values""": pixel_values}
					return config, inputs_dict






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


			'''simple docstring'''

			_A				:		Tuple         =		(
			    (
			        EfficientFormerModel,
			        EfficientFormerForImageClassificationWithTeacher,
			        EfficientFormerForImageClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			_A				:		Dict         =		(
			    {
			        '''feature-extraction''': EfficientFormerModel,
			        '''image-classification''': (
			            EfficientFormerForImageClassification,
			            EfficientFormerForImageClassificationWithTeacher,
			        ),
			    }
			    if is_torch_available()
			    else {}
			)
			_A				:		int         =		False

			_A				:		Any         =		False
			_A				:		List[str]         =		False
			_A				:		Tuple         =		False

			def UpperCamelCase__ (							self					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		EfficientFormerModelTester(self						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		ConfigTester(
					    self							,					config_class=__lowercase							,					has_text_modality=__lowercase							,					hidden_size=3_7						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					self.config_tester.run_common_tests()

			@unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""						)
			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					pass

			@unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""						)
			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					pass

			def UpperCamelCase__ (							self					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.model_tester.prepare_config_and_inputs_for_common()

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

							__SCREAMING_SNAKE_CASE	:		int   	=		["""pixel_values"""]
							self.assertListEqual(arg_names[:1]							,					__lowercase						)

			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					def check_hidden_states_output(lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			Optional[int]						):
							__SCREAMING_SNAKE_CASE	:		int   	=		model_class(__lowercase						)
							model.to(__lowercase						)
							model.eval()

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

							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

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

							if hasattr(self.model_tester							,					"""encoder_seq_length"""						):
									__SCREAMING_SNAKE_CASE	:		Any   	=		self.model_tester.encoder_seq_length
									if hasattr(self.model_tester							,					"""chunk_length"""						) and self.model_tester.chunk_length > 1:
											__SCREAMING_SNAKE_CASE	:		str   	=		seq_length * self.model_tester.chunk_length
							else:
									__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.model_tester.seq_length

							self.assertListEqual(
							    list(hidden_states[-1].shape[-2:]						)							,					[seq_length, self.model_tester.hidden_size]							,					)

							if config.is_encoder_decoder:
									__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		outputs.decoder_hidden_states

									self.assertIsInstance(__lowercase							,					(list, tuple)						)
									self.assertEqual(len(__lowercase						)							,					__lowercase						)
									__SCREAMING_SNAKE_CASE	:		List[str]   	=		getattr(self.model_tester							,					"""seq_length"""							,					__lowercase						)
									__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		getattr(self.model_tester							,					"""decoder_seq_length"""							,					__lowercase						)

									self.assertListEqual(
									    list(hidden_states[-1].shape[-2:]						)							,					[decoder_seq_length, self.model_tester.hidden_size]							,					)

					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		Dict   	=		True
							check_hidden_states_output(__lowercase							,					__lowercase							,					__lowercase						)

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

							check_hidden_states_output(__lowercase							,					__lowercase							,					__lowercase						)

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			List[str]							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			List[Any]=False						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		super()._prepare_for_class(__lowercase							,					__lowercase							,					return_labels=__lowercase						)

					if return_labels:
							if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
									del inputs_dict["labels"]

					return inputs_dict

			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowercase						)

			@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""						)
			def UpperCamelCase__ (							self					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*__lowercase						)

			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__lowercase						)

			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					if not self.model_tester.is_training:
							return

					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.model_tester.prepare_config_and_inputs_for_common()
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		True

					for model_class in self.all_model_classes:
							# EfficientFormerForImageClassificationWithTeacher supports inference-only
							if (
							    model_class in get_values(__lowercase						)
							    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
							):
									continue
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model_class(__lowercase						)
							model.to(__lowercase						)
							model.train()
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self._prepare_for_class(__lowercase							,					__lowercase							,					return_labels=__lowercase						)
							__SCREAMING_SNAKE_CASE	:		str   	=		model(**__lowercase						).loss
							loss.backward()

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.model_tester.prepare_config_and_inputs_for_common()

					__SCREAMING_SNAKE_CASE	:		int   	=		[
					    {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
					    {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
					    {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
					]

					for model_class in self.all_model_classes:
							if (
							    model_class
							    not in [
							        *get_values(__lowercase						),
							    ]
							    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
							):
									continue

							for problem_type in problem_types:
									with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"						):
											__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		problem_type["""title"""]
											__SCREAMING_SNAKE_CASE	:		Tuple   	=		problem_type["""num_labels"""]

											__SCREAMING_SNAKE_CASE	:		int   	=		model_class(__lowercase						)
											model.to(__lowercase						)
											model.train()

											__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self._prepare_for_class(__lowercase							,					__lowercase							,					return_labels=__lowercase						)

											if problem_type["num_labels"] > 1:
													__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		inputs["""labels"""].unsqueeze(1						).repeat(1							,					problem_type["""num_labels"""]						)

											__SCREAMING_SNAKE_CASE	:		Any   	=		inputs["""labels"""].to(problem_type["""dtype"""]						)

											# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
											# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
											# they have the same size." which is a symptom something in wrong for the regression problem.
											# See https://github.com/huggingface/transformers/issues/11780
											with warnings.catch_warnings(record=__lowercase						) as warning_list:
													__SCREAMING_SNAKE_CASE	:		int   	=		model(**__lowercase						).loss
											for w in warning_list:
													if "Using a target size that is different to the input size" in str(w.message						):
															raise ValueError(
															    F"Something is going wrong in the regression problem: intercepted {w.message}"						)

											loss.backward()

			@slow
			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__SCREAMING_SNAKE_CASE	:		int   	=		EfficientFormerModel.from_pretrained(__lowercase						)
							self.assertIsNotNone(__lowercase						)



			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		str   	=		self.model_tester.prepare_config_and_inputs_for_common()
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		True

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		getattr(self.model_tester							,					"""seq_length"""							,					__lowercase						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		getattr(self.model_tester							,					"""encoder_seq_length"""							,					__lowercase						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		getattr(self.model_tester							,					"""key_length"""							,					__lowercase						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		getattr(self.model_tester							,					"""chunk_length"""							,					__lowercase						)

					if chunk_length is not None and hasattr(self.model_tester							,					"""num_hashes"""						):
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		encoder_seq_length * self.model_tester.num_hashes

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		True
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		False
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		True
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model_class(__lowercase						)
							model.to(__lowercase						)
							model.eval()
							with torch.no_grad():
									__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model(**self._prepare_for_class(__lowercase							,					__lowercase						)						)
							__SCREAMING_SNAKE_CASE	:		int   	=		outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
							self.assertEqual(len(__lowercase						)							,					self.model_tester.num_attention_outputs						)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		True
							__SCREAMING_SNAKE_CASE	:		Any   	=		model_class(__lowercase						)
							model.to(__lowercase						)
							model.eval()
							with torch.no_grad():
									__SCREAMING_SNAKE_CASE	:		Tuple   	=		model(**self._prepare_for_class(__lowercase							,					__lowercase						)						)
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
							self.assertEqual(len(__lowercase						)							,					self.model_tester.num_attention_outputs						)

							if chunk_length is not None:
									self.assertListEqual(
									    list(attentions[0].shape[-4:]						)							,					[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]							,					)
							else:
									self.assertListEqual(
									    list(attentions[0].shape[-3:]						)							,					[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]							,					)


def     lowerCAmelCase_       (     ):
		__SCREAMING_SNAKE_CASE	:		Dict   	=		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
		return image






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


			'''simple docstring'''

			@cached_property
			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					return (
					    EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""						)
					    if is_vision_available()
					    else None
					)

			@slow
			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		EfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""						).to(
					    __lowercase						)

					__SCREAMING_SNAKE_CASE	:		int   	=		self.default_image_processor
					__SCREAMING_SNAKE_CASE	:		int   	=		prepare_img()
					__SCREAMING_SNAKE_CASE	:		str   	=		image_processor(images=__lowercase							,					return_tensors="""pt"""						).to(__lowercase						)

					# forward pass
					with torch.no_grad():
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		model(**__lowercase						)

					# verify the logits
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		(1, 1_0_0_0)
					self.assertEqual(outputs.logits.shape							,					__lowercase						)

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		torch.tensor([-0.05_55, 0.48_25, -0.08_52]						).to(__lowercase						)
					self.assertTrue(torch.allclose(outputs.logits[0][:3]							,					__lowercase							,					atol=1E-4						)						)



			@slow
			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		EfficientFormerForImageClassificationWithTeacher.from_pretrained(
					    """snap-research/efficientformer-l1-300"""						).to(__lowercase						)

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.default_image_processor
					__SCREAMING_SNAKE_CASE	:		int   	=		prepare_img()
					__SCREAMING_SNAKE_CASE	:		int   	=		image_processor(images=__lowercase							,					return_tensors="""pt"""						).to(__lowercase						)

					# forward pass
					with torch.no_grad():
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model(**__lowercase						)

					# verify the logits
					__SCREAMING_SNAKE_CASE	:		Any   	=		(1, 1_0_0_0)
					self.assertEqual(outputs.logits.shape							,					__lowercase						)

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		torch.tensor([-0.13_12, 0.43_53, -1.04_99]						).to(__lowercase						)
					self.assertTrue(torch.allclose(outputs.logits[0][:3]							,					__lowercase							,					atol=1E-4						)						)




'''simple docstring'''




import argparse
import os
import sys
import urllib.request
import zipfile


UpperCamelCase__	:     str    =			['''CoLA''', '''SST''', '''MRPC''', '''QQP''', '''STS''', '''MNLI''', '''SNLI''', '''QNLI''', '''RTE''', '''WNLI''', '''diagnostic''']
UpperCamelCase__	:     List[Any]    =			{
    '''CoLA''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4''',
    '''SST''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8''',
    '''MRPC''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc''',
    '''QQP''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5''',
    '''STS''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5''',
    '''MNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce''',
    '''SNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df''',
    '''QNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601''',
    '''RTE''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb''',
    '''WNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf''',
    '''diagnostic''': '''https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D''',
}

UpperCamelCase__	:     Optional[int]    =			'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'''
UpperCamelCase__	:     Any    =			'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'''


def     lowerCAmelCase_       (     _lowerCamelCase:       Union[str, Any]	,				_lowerCamelCase:       Dict ):
		print(F"Downloading and extracting {task}..." )
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		F"{task}.zip"
		urllib.request.urlretrieve(TASKaPATH[task]	,				_lowerCamelCase )
		with zipfile.ZipFile(_lowerCamelCase ) as zip_ref:
				zip_ref.extractall(_lowerCamelCase )
		os.remove(_lowerCamelCase )
		print("""\tCompleted!""" )


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       Optional[int] ):
		print("""Processing MRPC...""" )
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		os.path.join(_lowerCamelCase	,				"""MRPC""" )
		if not os.path.isdir(_lowerCamelCase ):
				os.mkdir(_lowerCamelCase )
		if path_to_data:
				__SCREAMING_SNAKE_CASE	:		Dict   	=		os.path.join(_lowerCamelCase	,				"""msr_paraphrase_train.txt""" )
				__SCREAMING_SNAKE_CASE	:		Tuple   	=		os.path.join(_lowerCamelCase	,				"""msr_paraphrase_test.txt""" )
		else:
				print("""Local MRPC data not specified, downloading data from %s""" % MRPC_TRAIN )
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		os.path.join(_lowerCamelCase	,				"""msr_paraphrase_train.txt""" )
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		os.path.join(_lowerCamelCase	,				"""msr_paraphrase_test.txt""" )
				urllib.request.urlretrieve(_lowerCamelCase	,				_lowerCamelCase )
				urllib.request.urlretrieve(_lowerCamelCase	,				_lowerCamelCase )
		if not os.path.isfile(_lowerCamelCase ):
				raise ValueError(F"Train data not found at {mrpc_train_file}" )
		if not os.path.isfile(_lowerCamelCase ):
				raise ValueError(F"Test data not found at {mrpc_test_file}" )
		urllib.request.urlretrieve(TASKaPATH["""MRPC"""]	,				os.path.join(_lowerCamelCase	,				"""dev_ids.tsv""" ) )

		__SCREAMING_SNAKE_CASE	:		Tuple   	=		[]
		with open(os.path.join(_lowerCamelCase	,				"""dev_ids.tsv""" )	,				encoding="""utf8""" ) as ids_fh:
				for row in ids_fh:
						dev_ids.append(row.strip().split("""\t""" ) )

		with open(_lowerCamelCase	,				encoding="""utf8""" ) as data_fh, open(
		    os.path.join(_lowerCamelCase	,				"""train.tsv""" )	,				"""w"""	,				encoding="""utf8""" ) as train_fh, open(os.path.join(_lowerCamelCase	,				"""dev.tsv""" )	,				"""w"""	,				encoding="""utf8""" ) as dev_fh:
				__SCREAMING_SNAKE_CASE	:		Any   	=		data_fh.readline()
				train_fh.write(_lowerCamelCase )
				dev_fh.write(_lowerCamelCase )
				for row in data_fh:
						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		int   	=		row.strip().split("""\t""" )
						if [ida, ida] in dev_ids:
								dev_fh.write("""%s\t%s\t%s\t%s\t%s\n""" % (label, ida, ida, sa, sa) )
						else:
								train_fh.write("""%s\t%s\t%s\t%s\t%s\n""" % (label, ida, ida, sa, sa) )

		with open(_lowerCamelCase	,				encoding="""utf8""" ) as data_fh, open(
		    os.path.join(_lowerCamelCase	,				"""test.tsv""" )	,				"""w"""	,				encoding="""utf8""" ) as test_fh:
				__SCREAMING_SNAKE_CASE	:		Dict   	=		data_fh.readline()
				test_fh.write("""index\t#1 ID\t#2 ID\t#1 String\t#2 String\n""" )
				for idx, row in enumerate(_lowerCamelCase ):
						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Dict   	=		row.strip().split("""\t""" )
						test_fh.write("""%d\t%s\t%s\t%s\t%s\n""" % (idx, ida, ida, sa, sa) )
		print("""\tCompleted!""" )


def     lowerCAmelCase_       (     _lowerCamelCase:       Dict ):
		print("""Downloading and extracting diagnostic...""" )
		if not os.path.isdir(os.path.join(_lowerCamelCase	,				"""diagnostic""" ) ):
				os.mkdir(os.path.join(_lowerCamelCase	,				"""diagnostic""" ) )
		__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		os.path.join(_lowerCamelCase	,				"""diagnostic"""	,				"""diagnostic.tsv""" )
		urllib.request.urlretrieve(TASKaPATH["""diagnostic"""]	,				_lowerCamelCase )
		print("""\tCompleted!""" )
		return


def     lowerCAmelCase_       (     _lowerCamelCase:       int ):
		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		task_names.split(""",""" )
		if "all" in task_names:
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		TASKS
		else:
				__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
				for task_name in task_names:
						if task_name not in TASKS:
								raise ValueError(F"Task {task_name} not found!" )
						tasks.append(_lowerCamelCase )
		return tasks


def     lowerCAmelCase_       (     _lowerCamelCase:       Tuple ):
		__SCREAMING_SNAKE_CASE	:		int   	=		argparse.ArgumentParser()
		parser.add_argument("""--data_dir"""	,				help="""directory to save data to"""	,				type=_lowerCamelCase	,				default="""glue_data""" )
		parser.add_argument(
		    """--tasks"""	,				help="""tasks to download data for as a comma separated string"""	,				type=_lowerCamelCase	,				default="""all""" )
		parser.add_argument(
		    """--path_to_mrpc"""	,				help="""path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt"""	,				type=_lowerCamelCase	,				default=""""""	,				)
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		parser.parse_args(_lowerCamelCase )

		if not os.path.isdir(args.data_dir ):
				os.mkdir(args.data_dir )
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		get_tasks(args.tasks )

		for task in tasks:
				if task == "MRPC":
						format_mrpc(args.data_dir	,				args.path_to_mrpc )
				elif task == "diagnostic":
						download_diagnostic(args.data_dir )
				else:
						download_and_extract(_lowerCamelCase	,				args.data_dir )


if __name__ == "__main__":
						sys.exit(main(sys.argv[1:]))