
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 	A_       :




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case = 13		,   snake_case = 64		,   snake_case = 2		,   snake_case = 3		,   snake_case = 3		,   snake_case = True		,   snake_case = True		,   snake_case = 128		,   snake_case=[16, 32, 64, 128]		,   snake_case = 7		,   snake_case = 4		,   snake_case = 37		,   snake_case = "gelu"		,   snake_case = 0.1		,   snake_case = 0.1		,   snake_case = 10		,   snake_case = 0.02		,   snake_case = 2		,   snake_case = 1		,   snake_case = 128		,   snake_case = [2, 2, 2, 2]		,   snake_case = 2		,   snake_case = 2		,   ):
		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					  =   encoder_stride
		lowercase					  =   num_attention_outputs
		lowercase					  =   embed_dim
		lowercase					  =   embed_dim + 1
		lowercase					  =   resolution
		lowercase					  =   depths
		lowercase					  =   hidden_sizes
		lowercase					  =   dim
		lowercase					  =   mlp_expansion_ratio





	def      SCREAMING_SNAKE_CASE__						(	self	):
		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	):
		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=snake_case		,   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      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   EfficientFormerModel(config=snake_case	)
		model.to(snake_case	)
		model.eval()
		lowercase					  =   model(snake_case	)
		self.parent.assertEqual(result.last_hidden_state.shape		,   (self.batch_size, self.seq_length, self.hidden_size)	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   self.type_sequence_label_size
		lowercase					  =   EfficientFormerForImageClassification(snake_case	)
		model.to(snake_case	)
		model.eval()
		lowercase					  =   model(snake_case		,   labels=snake_case	)
		self.parent.assertEqual(result.logits.shape		,   (self.batch_size, self.type_sequence_label_size)	)

		# test greyscale images
		lowercase					  =   1
		lowercase					  =   EfficientFormerForImageClassification(snake_case	)
		model.to(snake_case	)
		model.eval()

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





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







@require_torch
class 	A_       (      __lowerCamelCase				,     __lowerCamelCase				,     unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					List[str]										=	(
	    (
	        EfficientFormerModel,
	        EfficientFormerForImageClassificationWithTeacher,
	        EfficientFormerForImageClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	_UpperCamelCase					:					Optional[Any]										=	(
	    {
	        """feature-extraction""": EfficientFormerModel,
	        """image-classification""": (
	            EfficientFormerForImageClassification,
	            EfficientFormerForImageClassificationWithTeacher,
	        ),
	    }
	    if is_torch_available()
	    else {}
	)
	_UpperCamelCase					:					List[Any]										=	False

	_UpperCamelCase					:					Union[str, Any]										=	False
	_UpperCamelCase					:					Optional[int]										=	False
	_UpperCamelCase					:					Tuple										=	False





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   EfficientFormerModelTester(self	)
		lowercase					  =   ConfigTester(
		    self		,   config_class=snake_case		,   has_text_modality=snake_case		,   hidden_size=37	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		self.config_tester.run_common_tests()





	@unittest.skip(reason='EfficientFormer does not use inputs_embeds'	)
	def      SCREAMING_SNAKE_CASE__						(	self	):
		pass





	@unittest.skip(reason='EfficientFormer does not support input and output embeddings'	)
	def      SCREAMING_SNAKE_CASE__						(	self	):
		pass





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase  , lowercase					  =   self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowercase					  =   model_class(snake_case	)
			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]		,   snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		def check_hidden_states_output(snake_case		,   snake_case		,   snake_case	):
			lowercase					  =   model_class(snake_case	)
			model.to(snake_case	)
			model.eval()

			with torch.no_grad():
				lowercase					  =   model(**self._prepare_for_class(snake_case		,   snake_case	)	)

			lowercase					  =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

			lowercase					  =   getattr(
			    self.model_tester		,   'expected_num_hidden_layers'		,   self.model_tester.num_hidden_layers + 1	)
			self.assertEqual(len(snake_case	)		,   snake_case	)

			if hasattr(self.model_tester		,   'encoder_seq_length'	):
				lowercase					  =   self.model_tester.encoder_seq_length
				if hasattr(self.model_tester		,   'chunk_length'	) and self.model_tester.chunk_length > 1:
					lowercase					  =   seq_length * self.model_tester.chunk_length
			else:
				lowercase					  =   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:
				lowercase					  =   outputs.decoder_hidden_states

				self.assertIsInstance(snake_case		,   (list, tuple)	)
				self.assertEqual(len(snake_case	)		,   snake_case	)
				lowercase					  =   getattr(self.model_tester		,   'seq_length'		,   snake_case	)
				lowercase					  =   getattr(self.model_tester		,   'decoder_seq_length'		,   snake_case	)

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

		lowercase  , lowercase					  =   self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowercase					  =   True
			check_hidden_states_output(snake_case		,   snake_case		,   snake_case	)

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			lowercase					  =   True

			check_hidden_states_output(snake_case		,   snake_case		,   snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case=False	):
		lowercase					  =   super()._prepare_for_class(snake_case		,   snake_case		,   return_labels=snake_case	)

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

		return inputs_dict





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*snake_case	)





	@unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'	)
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_image_modeling(*snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		if not self.model_tester.is_training:
			return

		lowercase  , lowercase					  =   self.model_tester.prepare_config_and_inputs_for_common()
		lowercase					  =   True

		for model_class in self.all_model_classes:
			# EfficientFormerForImageClassificationWithTeacher supports inference-only
			if (
			    model_class in get_values(snake_case	)
			    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
			):
				continue
			lowercase					  =   model_class(snake_case	)
			model.to(snake_case	)
			model.train()
			lowercase					  =   self._prepare_for_class(snake_case		,   snake_case		,   return_labels=snake_case	)
			lowercase					  =   model(**snake_case	).loss
			loss.backward()





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase  , lowercase					  =   self.model_tester.prepare_config_and_inputs_for_common()

		lowercase					  =   [
		    {'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(snake_case	),
			    ]
			    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
			):
				continue

			for problem_type in problem_types:
				with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''	):
					lowercase					  =   problem_type['title']
					lowercase					  =   problem_type['num_labels']

					lowercase					  =   model_class(snake_case	)
					model.to(snake_case	)
					model.train()

					lowercase					  =   self._prepare_for_class(snake_case		,   snake_case		,   return_labels=snake_case	)

					if problem_type["num_labels"] > 1:
						lowercase					  =   inputs['labels'].unsqueeze(1	).repeat(1		,   problem_type['num_labels']	)

					lowercase					  =   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=snake_case	) as warning_list:
						lowercase					  =   model(**snake_case	).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      SCREAMING_SNAKE_CASE__						(	self	):
		for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowercase					  =   EfficientFormerModel.from_pretrained(snake_case	)
			self.assertIsNotNone(snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase  , lowercase					  =   self.model_tester.prepare_config_and_inputs_for_common()
		lowercase					  =   True

		lowercase					  =   getattr(self.model_tester		,   'seq_length'		,   snake_case	)
		lowercase					  =   getattr(self.model_tester		,   'encoder_seq_length'		,   snake_case	)
		lowercase					  =   getattr(self.model_tester		,   'key_length'		,   snake_case	)
		lowercase					  =   getattr(self.model_tester		,   'chunk_length'		,   snake_case	)

		if chunk_length is not None and hasattr(self.model_tester		,   'num_hashes'	):
			lowercase					  =   encoder_seq_length * self.model_tester.num_hashes

		for model_class in self.all_model_classes:
			lowercase					  =   True
			lowercase					  =   False
			lowercase					  =   True
			lowercase					  =   model_class(snake_case	)
			model.to(snake_case	)
			model.eval()
			with torch.no_grad():
				lowercase					  =   model(**self._prepare_for_class(snake_case		,   snake_case	)	)
			lowercase					  =   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
			self.assertEqual(len(snake_case	)		,   self.model_tester.num_attention_outputs	)

			# check that output_attentions also work using config
			del inputs_dict["output_attentions"]
			lowercase					  =   True
			lowercase					  =   model_class(snake_case	)
			model.to(snake_case	)
			model.eval()
			with torch.no_grad():
				lowercase					  =   model(**self._prepare_for_class(snake_case		,   snake_case	)	)
			lowercase					  =   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
			self.assertEqual(len(snake_case	)		,   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 							UpperCAmelCase_		(							):
	lowercase					  =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
	return image







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




	'''simple docstring'''


	@cached_property
	def      SCREAMING_SNAKE_CASE__						(	self	):
		return (
		    EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'	)
		    if is_vision_available()
		    else None
		)





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   EfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'	).to(
		    snake_case	)

		lowercase					  =   self.default_image_processor
		lowercase					  =   prepare_img()
		lowercase					  =   image_processor(images=snake_case		,   return_tensors='pt'	).to(snake_case	)

		# forward pass
		with torch.no_grad():
			lowercase					  =   model(**snake_case	)

		# verify the logits
		lowercase					  =   (1, 1000)
		self.assertEqual(outputs.logits.shape		,   snake_case	)

		lowercase					  =   torch.tensor([-0.0_555, 0.4_825, -0.0_852]	).to(snake_case	)
		self.assertTrue(torch.allclose(outputs.logits[0][:3]		,   snake_case		,   atol=1E-4	)	)





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   EfficientFormerForImageClassificationWithTeacher.from_pretrained(
		    'snap-research/efficientformer-l1-300'	).to(snake_case	)

		lowercase					  =   self.default_image_processor
		lowercase					  =   prepare_img()
		lowercase					  =   image_processor(images=snake_case		,   return_tensors='pt'	).to(snake_case	)

		# forward pass
		with torch.no_grad():
			lowercase					  =   model(**snake_case	)

		# verify the logits
		lowercase					  =   (1, 1000)
		self.assertEqual(outputs.logits.shape		,   snake_case	)

		lowercase					  =   torch.tensor([-0.1_312, 0.4_353, -1.0_499]	).to(snake_case	)
		self.assertTrue(torch.allclose(outputs.logits[0][:3]		,   snake_case		,   atol=1E-4	)	)



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   copy.deepcopy(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   sorted(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   [bisect.bisect_right(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) for y in x]
	return quantized






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=0							):
	lowercase					  =   np.arange(start=3			,							stop=__SCREAMING_SNAKE_CASE			,							step=4							).tolist() if k > 0 else [10]
	lowercase					  =   _quantize(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)
	# count number of elements per group
	lowercase					  =   np.unique(__SCREAMING_SNAKE_CASE			,							return_counts=__SCREAMING_SNAKE_CASE							)[1]
	lowercase					  =   [0] + bins + [np.inf]
	logger.info('Using {} as bins for aspect lengths quantization'.format(__SCREAMING_SNAKE_CASE							)							)
	logger.info('Count of instances per bin: {}'.format(__SCREAMING_SNAKE_CASE							)							)
	return groups







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case		,   snake_case	):
		if not isinstance(snake_case		,   snake_case	):
			raise ValueError(
			    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(snake_case	)	)
		lowercase					  =   sampler
		lowercase					  =   group_ids
		lowercase					  =   batch_size





	def __iter__(	self	):
		lowercase					  =   defaultdict(snake_case	)
		lowercase					  =   defaultdict(snake_case	)

		lowercase					  =   0
		for idx in self.sampler:
			lowercase					  =   self.group_ids[idx]
			buffer_per_group[group_id].append(snake_case	)
			samples_per_group[group_id].append(snake_case	)
			if len(buffer_per_group[group_id]	) == self.batch_size:
				yield buffer_per_group[group_id]  # TODO
				num_batches += 1
				del buffer_per_group[group_id]
			assert len(buffer_per_group[group_id]	) < self.batch_size

		# now we have run out of elements that satisfy
		# the group criteria, let's return the remaining
		# elements so that the size of the sampler is
		# deterministic
		lowercase					  =   len(self	)
		lowercase					  =   expected_num_batches - num_batches
		if num_remaining > 0:
			# for the remaining batches, group the batches by similar lengths
			lowercase					  =   []
			for group_id, idxs in sorted(buffer_per_group.items()		,   key=lambda snake_case	: x[0]	):
				batch_idx.extend(snake_case	)
				if len(snake_case	) >= self.batch_size:
					yield batch_idx[: self.batch_size]
					lowercase					  =   batch_idx[self.batch_size :]
					num_remaining -= 1
			if len(snake_case	) > 0:
				yield batch_idx
				num_remaining -= 1
		assert num_remaining == 0





	def __len__(	self	):
		return (len(self.sampler	) + self.batch_size - 1) // self.batch_size

