






import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


__lowerCamelCase :      Any										=				logging.get_logger(__name__)

# General docstring
__lowerCamelCase :      Dict										=				"""PoolFormerConfig"""

# Base docstring
__lowerCamelCase :      Dict										=				"""sail/poolformer_s12"""
__lowerCamelCase :      Any										=				[1, 512, 7, 7]

# Image classification docstring
__lowerCamelCase :      Optional[int]										=				"""sail/poolformer_s12"""
__lowerCamelCase :      Dict										=				"""tabby, tabby cat"""

__lowerCamelCase :      Dict										=				[
    """sail/poolformer_s12""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str					,  snake_case_ :							float = 0.0					,  snake_case_ :							bool = False       ):
	if drop_prob == 0.0 or not training:
		return input
	snake_case__			:				str       =  1 - drop_prob
	snake_case__			:				Dict       =  (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
	snake_case__			:				List[Any]       =  keep_prob + torch.rand(snake_case_					,  dtype=input.dtype					,  device=input.device       )
	random_tensor.floor_()  # binarize
	snake_case__			:				Tuple       =  input.div(snake_case_       ) * random_tensor
	return output



class 							SCREAMING_SNAKE_CASE__						(    nn.Module						):



					def __init__( self					:  str							,		__A					:  Optional[float] = None	):
						super().__init__()
						snake_case__			:				Union[str, Any]       =  drop_prob



					def 	_lowercase					( self					:  List[Any]							,		__A					:  torch.Tensor	):
						return drop_path(__A							,		self.drop_prob							,		self.training	)






					def 	_lowercase					( self					:  Tuple	):
						return "p={}".format(self.drop_prob	)



class 							SCREAMING_SNAKE_CASE__						(    nn.Module						):



					def __init__( self					:  Optional[Any]							,		__A					:  List[Any]							,		__A					:  str							,		__A					:  List[Any]							,		__A					:  List[str]							,		__A					:  List[Any]							,		__A					:  Optional[int]=None	):
						super().__init__()
						snake_case__			:				str       =  patch_size if isinstance(__A							,		collections.abc.Iterable	) else (patch_size, patch_size)
						snake_case__			:				int       =  stride if isinstance(__A							,		collections.abc.Iterable	) else (stride, stride)
						snake_case__			:				str       =  padding if isinstance(__A							,		collections.abc.Iterable	) else (padding, padding)

						snake_case__			:				Union[str, Any]       =  nn.Convad(__A							,		__A							,		kernel_size=__A							,		stride=__A							,		padding=__A	)
						snake_case__			:				Tuple       =  norm_layer(__A	) if norm_layer else nn.Identity()






					def 	_lowercase					( self					:  Any							,		__A					:  List[str]	):
						snake_case__			:				str       =  self.projection(__A	)
						snake_case__			:				List[str]       =  self.norm(__A	)
						return embeddings



class 							SCREAMING_SNAKE_CASE__						(    nn.GroupNorm						):



					def __init__( self					:  Dict							,		__A					:  Tuple							,		**__A					:  Optional[Any]	):
						super().__init__(1							,		__A							,		**__A	)



class 							SCREAMING_SNAKE_CASE__						(    nn.Module						):



					def __init__( self					:  List[Any]							,		__A					:  List[Any]	):
						super().__init__()
						snake_case__			:				Union[str, Any]       =  nn.AvgPoolad(__A							,		stride=1							,		padding=pool_size // 2							,		count_include_pad=__A	)






					def 	_lowercase					( self					:  Dict							,		__A					:  str	):
						return self.pool(__A	) - hidden_states



class 							SCREAMING_SNAKE_CASE__						(    nn.Module						):



					def __init__( self					:  Optional[int]							,		__A					:  Optional[int]							,		__A					:  str							,		__A					:  Dict							,		__A					:  Dict	):
						super().__init__()
						snake_case__			:				int       =  nn.Convad(__A							,		__A							,		1	)
						snake_case__			:				Optional[Any]       =  nn.Convad(__A							,		__A							,		1	)
						snake_case__			:				Union[str, Any]       =  PoolFormerDropPath(__A	)
						if isinstance(config.hidden_act							,		__A	):
							snake_case__			:				Any       =  ACTaFN[config.hidden_act]
						else:
							snake_case__			:				Union[str, Any]       =  config.hidden_act






					def 	_lowercase					( self					:  Dict							,		__A					:  List[str]	):
						snake_case__			:				str       =  self.conva(__A	)
						snake_case__			:				Tuple       =  self.act_fn(__A	)
						snake_case__			:				Tuple       =  self.drop(__A	)
						snake_case__			:				List[str]       =  self.conva(__A	)
						snake_case__			:				Tuple       =  self.drop(__A	)

						return hidden_states



class 							SCREAMING_SNAKE_CASE__						(    nn.Module						):



					def __init__( self					:  List[Any]							,		__A					:  int							,		__A					:  Tuple							,		__A					:  Tuple							,		__A					:  str							,		__A					:  Dict							,		__A					:  Union[str, Any]	):
						super().__init__()
						snake_case__			:				List[Any]       =  PoolFormerPooling(__A	)
						snake_case__			:				Optional[Any]       =  PoolFormerOutput(__A							,		__A							,		__A							,		__A	)
						snake_case__			:				Optional[Any]       =  PoolFormerGroupNorm(__A	)
						snake_case__			:				int       =  PoolFormerGroupNorm(__A	)

						# Useful for training neural nets
						snake_case__			:				Union[str, Any]       =  PoolFormerDropPath(__A	) if drop_path > 0.0 else nn.Identity()
						snake_case__			:				Any       =  config.use_layer_scale
						if config.use_layer_scale:
							snake_case__			:				Optional[Any]       =  nn.Parameter(
							    config.layer_scale_init_value * torch.ones((__A)	)							,		requires_grad=__A	)
							snake_case__			:				Any       =  nn.Parameter(
							    config.layer_scale_init_value * torch.ones((__A)	)							,		requires_grad=__A	)






					def 	_lowercase					( self					:  Any							,		__A					:  Any	):
						if self.use_layer_scale:
							snake_case__			:				Optional[Any]       =  self.pooling(self.before_norm(__A	)	)
							snake_case__			:				Tuple       =  self.layer_scale_a.unsqueeze(-1	).unsqueeze(-1	) * pooling_output
							# First residual connection
							snake_case__			:				Dict       =  hidden_states + self.drop_path(__A	)
							snake_case__			:				Optional[Any]       =  ()

							snake_case__			:				Optional[Any]       =  self.output(self.after_norm(__A	)	)
							snake_case__			:				Tuple       =  self.layer_scale_a.unsqueeze(-1	).unsqueeze(-1	) * layer_output
							# Second residual connection
							snake_case__			:				int       =  hidden_states + self.drop_path(__A	)

							snake_case__			:				Any       =  (output,) + outputs
							return outputs

						else:
							snake_case__			:				str       =  self.drop_path(self.pooling(self.before_norm(__A	)	)	)
							# First residual connection
							snake_case__			:				Tuple       =  pooling_output + hidden_states
							snake_case__			:				Union[str, Any]       =  ()

							# Second residual connection inside the PoolFormerOutput block
							snake_case__			:				Optional[int]       =  self.drop_path(self.output(self.after_norm(__A	)	)	)
							snake_case__			:				Tuple       =  hidden_states + layer_output

							snake_case__			:				str       =  (output,) + outputs
							return outputs



class 							SCREAMING_SNAKE_CASE__						(    nn.Module						):



					def __init__( self					:  List[Any]							,		__A					:  str	):
						super().__init__()
						snake_case__			:				Optional[int]       =  config
						# stochastic depth decay rule
						snake_case__			:				List[Any]       =  [x.item() for x in torch.linspace(0							,		config.drop_path_rate							,		sum(config.depths	)	)]

						# patch embeddings
						snake_case__			:				Any       =  []
						for i in range(config.num_encoder_blocks	):
							embeddings.append(
							    PoolFormerEmbeddings(
							        patch_size=config.patch_sizes[i]							,		stride=config.strides[i]							,		padding=config.padding[i]							,		num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]							,		hidden_size=config.hidden_sizes[i]							,		)	)
						snake_case__			:				List[str]       =  nn.ModuleList(__A	)

						# Transformer blocks
						snake_case__			:				Dict       =  []
						snake_case__			:				List[str]       =  0
						for i in range(config.num_encoder_blocks	):
							# each block consists of layers
							snake_case__			:				List[str]       =  []
							if i != 0:
								cur += config.depths[i - 1]
							for j in range(config.depths[i]	):
								layers.append(
								    PoolFormerLayer(
								        __A							,		num_channels=config.hidden_sizes[i]							,		pool_size=config.pool_size							,		hidden_size=config.hidden_sizes[i]							,		intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio	)							,		drop_path=dpr[cur + j]							,		)	)
							blocks.append(nn.ModuleList(__A	)	)

						snake_case__			:				Optional[int]       =  nn.ModuleList(__A	)






					def 	_lowercase					( self					:  Optional[Any]							,		__A					:  Tuple							,		__A					:  List[str]=False							,		__A					:  int=True	):
						snake_case__			:				Dict       =  () if output_hidden_states else None

						snake_case__			:				str       =  pixel_values
						for idx, layers in enumerate(zip(self.patch_embeddings							,		self.block	)	):
							snake_case__			:				Tuple       =  layers
							# Get patch embeddings from hidden_states
							snake_case__			:				str       =  embedding_layer(__A	)
							# Send the embeddings through the blocks
							for _, blk in enumerate(__A	):
								snake_case__			:				int       =  blk(__A	)
								snake_case__			:				List[Any]       =  layer_outputs[0]

							if output_hidden_states:
								snake_case__			:				Optional[int]       =  all_hidden_states + (hidden_states,)

						if not return_dict:
							return tuple(v for v in [hidden_states, all_hidden_states] if v is not None	)

						return BaseModelOutputWithNoAttention(last_hidden_state=__A							,		hidden_states=__A	)



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):
					a_     =		PoolFormerConfig
					a_     =		"poolformer"
					a_     =		"pixel_values"
					a_     =		True



					def 	_lowercase					( self					:  Dict							,		__A					:  Any	):
						if isinstance(__A							,		(nn.Linear, nn.Convad)	):
							module.weight.data.normal_(mean=0.0							,		std=self.config.initializer_range	)
							if module.bias is not None:
								module.bias.data.zero_()
						elif isinstance(__A							,		nn.LayerNorm	):
							module.bias.data.zero_()
							module.weight.data.fill_(1.0	)






					def 	_lowercase					( self					:  Tuple							,		__A					:  List[Any]							,		__A					:  Dict=False	):
						if isinstance(__A							,		__A	):
							snake_case__			:				str       =  value
__lowerCamelCase :      Optional[int]										=				R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

__lowerCamelCase :      Dict										=				R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PoolFormerImageProcessor.__call__`] for details.
"""



@add_start_docstrings(
    "The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top."			,       UpperCamelCase_			,       )
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):



					def __init__( self					:  str							,		__A					:  Optional[int]	):
						super().__init__(__A	)
						snake_case__			:				str       =  config

						snake_case__			:				Union[str, Any]       =  PoolFormerEncoder(__A	)

						# Initialize weights and apply final processing
						self.post_init()



					def 	_lowercase					( self					:  Union[str, Any]	):
						return self.embeddings.patch_embeddings






					@add_start_docstrings_to_model_forward(__A	)
					@add_code_sample_docstrings(
					    checkpoint=_CHECKPOINT_FOR_DOC							,		output_type=__A							,		config_class=_CONFIG_FOR_DOC							,		modality="vision"							,		expected_output=_EXPECTED_OUTPUT_SHAPE							,		)
					def 	_lowercase					( self					:  Any							,		__A					:  Optional[torch.FloatTensor] = None							,		__A					:  Optional[bool] = None							,		__A					:  Optional[bool] = None							,		):
						snake_case__			:				List[str]       =  (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						snake_case__			:				Tuple       =  return_dict if return_dict is not None else self.config.use_return_dict

						if pixel_values is None:
							raise ValueError("You have to specify pixel_values"	)

						snake_case__			:				Any       =  self.encoder(
						    __A							,		output_hidden_states=__A							,		return_dict=__A							,		)
						snake_case__			:				List[str]       =  encoder_outputs[0]

						if not return_dict:
							return (sequence_output, None) + encoder_outputs[1:]

						return BaseModelOutputWithNoAttention(
						    last_hidden_state=__A							,		hidden_states=encoder_outputs.hidden_states							,		)



class 							SCREAMING_SNAKE_CASE__						(    nn.Module						):



					def __init__( self					:  List[Any]							,		__A					:  Optional[Any]	):
						super().__init__()
						snake_case__			:				Dict       =  nn.Linear(config.hidden_size							,		config.hidden_size	)






					def 	_lowercase					( self					:  Dict							,		__A					:  List[Any]	):
						snake_case__			:				int       =  self.dense(__A	)
						return output



@add_start_docstrings(
    "\n    PoolFormer Model transformer with an image classification head on top\n    "			,       UpperCamelCase_			,       )
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):



					def __init__( self					:  Any							,		__A					:  Tuple	):
						super().__init__(__A	)
						snake_case__			:				Optional[Any]       =  config.num_labels
						snake_case__			:				int       =  PoolFormerModel(__A	)

						# Final norm
						snake_case__			:				Any       =  PoolFormerGroupNorm(config.hidden_sizes[-1]	)
						# Classifier head
						snake_case__			:				int       =  (
						    nn.Linear(config.hidden_sizes[-1]							,		config.num_labels	) if config.num_labels > 0 else nn.Identity()
						)

						# Initialize weights and apply final processing
						self.post_init()






					@add_start_docstrings_to_model_forward(__A	)
					@add_code_sample_docstrings(
					    checkpoint=_IMAGE_CLASS_CHECKPOINT							,		output_type=__A							,		config_class=_CONFIG_FOR_DOC							,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,		)
					def 	_lowercase					( self					:  int							,		__A					:  Optional[torch.FloatTensor] = None							,		__A					:  Optional[torch.LongTensor] = None							,		__A					:  Optional[bool] = None							,		__A					:  Optional[bool] = None							,		):
						snake_case__			:				str       =  return_dict if return_dict is not None else self.config.use_return_dict

						snake_case__			:				List[str]       =  self.poolformer(
						    __A							,		output_hidden_states=__A							,		return_dict=__A							,		)

						snake_case__			:				Optional[Any]       =  outputs[0]

						snake_case__			:				str       =  self.classifier(self.norm(__A	).mean([-2, -1]	)	)

						snake_case__			:				Any       =  None
						if labels is not None:
							if self.config.problem_type is None:
								if self.num_labels == 1:
									snake_case__			:				List[str]       =  "regression"
								elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
									snake_case__			:				str       =  "single_label_classification"
								else:
									snake_case__			:				Any       =  "multi_label_classification"

							if self.config.problem_type == "regression":
								snake_case__			:				Tuple       =  MSELoss()
								if self.num_labels == 1:
									snake_case__			:				Optional[Any]       =  loss_fct(logits.squeeze()							,		labels.squeeze()	)
								else:
									snake_case__			:				List[Any]       =  loss_fct(__A							,		__A	)
							elif self.config.problem_type == "single_label_classification":
								snake_case__			:				Optional[int]       =  CrossEntropyLoss()
								snake_case__			:				Any       =  loss_fct(logits.view(-1							,		self.num_labels	)							,		labels.view(-1	)	)
							elif self.config.problem_type == "multi_label_classification":
								snake_case__			:				int       =  BCEWithLogitsLoss()
								snake_case__			:				Union[str, Any]       =  loss_fct(__A							,		__A	)

						if not return_dict:
							snake_case__			:				str       =  (logits,) + outputs[2:]
							return ((loss,) + output) if loss is not None else output

						return ImageClassifierOutputWithNoAttention(loss=__A							,		logits=__A							,		hidden_states=outputs.hidden_states	)











import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import DeformableDetrImageProcessor



class 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






					def __init__( self					:  List[str]							,		__A					:  int							,		__A					:  str=7							,		__A					:  Union[str, Any]=3							,		__A					:  Union[str, Any]=3_0							,		__A					:  Optional[int]=4_0_0							,		__A					:  Optional[Any]=True							,		__A					:  Optional[int]=None							,		__A					:  Union[str, Any]=True							,		__A					:  Optional[int]=[0.5, 0.5, 0.5]							,		__A					:  Any=[0.5, 0.5, 0.5]							,		__A					:  Optional[int]=True							,		__A					:  Optional[Any]=1 / 2_5_5							,		__A					:  Union[str, Any]=True							,		):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						snake_case__			:				Optional[Any]       =  size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
						snake_case__			:				List[Any]       =  parent
						snake_case__			:				Union[str, Any]       =  batch_size
						snake_case__			:				Tuple       =  num_channels
						snake_case__			:				List[Any]       =  min_resolution
						snake_case__			:				Optional[Any]       =  max_resolution
						snake_case__			:				str       =  do_resize
						snake_case__			:				List[str]       =  size
						snake_case__			:				List[Any]       =  do_normalize
						snake_case__			:				Dict       =  image_mean
						snake_case__			:				List[Any]       =  image_std
						snake_case__			:				int       =  do_rescale
						snake_case__			:				Tuple       =  rescale_factor
						snake_case__			:				str       =  do_pad



					def 	_lowercase					( self					:  List[str]	):
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}






					def 	_lowercase					( self					:  Optional[Any]							,		__A					:  Dict							,		__A					:  Union[str, Any]=False	):
						if not batched:
							snake_case__			:				List[str]       =  image_inputs[0]
							if isinstance(__A							,		Image.Image	):
								snake_case__,						snake_case__			:				Any       =  image.size
							else:
								snake_case__,						snake_case__			:				List[str]       =  image.shape[1], image.shape[2]
							if w < h:
								snake_case__			:				List[str]       =  int(self.size["shortest_edge"] * h / w	)
								snake_case__			:				Tuple       =  self.size["shortest_edge"]
							elif w > h:
								snake_case__			:				Optional[int]       =  self.size["shortest_edge"]
								snake_case__			:				Union[str, Any]       =  int(self.size["shortest_edge"] * w / h	)
							else:
								snake_case__			:				Optional[Any]       =  self.size["shortest_edge"]
								snake_case__			:				List[Any]       =  self.size["shortest_edge"]

						else:
							snake_case__			:				Union[str, Any]       =  []
							for image in image_inputs:
								snake_case__,						snake_case__			:				int       =  self.get_expected_values([image]	)
								expected_values.append((expected_height, expected_width)	)
							snake_case__			:				Any       =  max(__A							,		key=lambda __A	: item[0]	)[0]
							snake_case__			:				Optional[int]       =  max(__A							,		key=lambda __A	: item[1]	)[1]

						return expected_height, expected_width



@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		DeformableDetrImageProcessor if is_vision_available() else None



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				str       =  DeformableDetrImageProcessingTester(self	)



					@property
					def 	_lowercase					( self					:  List[Any]	):
						return self.image_processor_tester.prepare_image_processor_dict()



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				int       =  self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(__A							,		"image_mean"	)	)
						self.assertTrue(hasattr(__A							,		"image_std"	)	)
						self.assertTrue(hasattr(__A							,		"do_normalize"	)	)
						self.assertTrue(hasattr(__A							,		"do_resize"	)	)
						self.assertTrue(hasattr(__A							,		"do_rescale"	)	)
						self.assertTrue(hasattr(__A							,		"do_pad"	)	)
						self.assertTrue(hasattr(__A							,		"size"	)	)



					def 	_lowercase					( self					:  Tuple	):
						snake_case__			:				int       =  self.image_processing_class.from_dict(self.image_processor_dict	)
						self.assertEqual(image_processor.size							,		{"shortest_edge": 1_8, "longest_edge": 1_3_3_3}	)
						self.assertEqual(image_processor.do_pad							,		__A	)

						snake_case__			:				List[Any]       =  self.image_processing_class.from_dict(
						    self.image_processor_dict							,		size=4_2							,		max_size=8_4							,		pad_and_return_pixel_mask=__A	)
						self.assertEqual(image_processor.size							,		{"shortest_edge": 4_2, "longest_edge": 8_4}	)
						self.assertEqual(image_processor.do_pad							,		__A	)



					def 	_lowercase					( self					:  Any	):
						pass



					def 	_lowercase					( self					:  Optional[int]	):
						# Initialize image_processing
						snake_case__			:				List[str]       =  self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						snake_case__			:				Tuple       =  prepare_image_inputs(self.image_processor_tester							,		equal_resolution=__A	)
						for image in image_inputs:
							self.assertIsInstance(__A							,		Image.Image	)

						# Test not batched input
						snake_case__			:				Tuple       =  image_processing(image_inputs[0]							,		return_tensors="pt"	).pixel_values

						snake_case__,						snake_case__			:				Dict       =  self.image_processor_tester.get_expected_values(__A	)

						self.assertEqual(
						    encoded_images.shape							,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,		)

						# Test batched
						snake_case__,						snake_case__			:				Optional[Any]       =  self.image_processor_tester.get_expected_values(__A							,		batched=__A	)

						snake_case__			:				Union[str, Any]       =  image_processing(__A							,		return_tensors="pt"	).pixel_values
						self.assertEqual(
						    encoded_images.shape							,		(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,		)



					def 	_lowercase					( self					:  Any	):
						# Initialize image_processing
						snake_case__			:				Optional[Any]       =  self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						snake_case__			:				List[Any]       =  prepare_image_inputs(self.image_processor_tester							,		equal_resolution=__A							,		numpify=__A	)
						for image in image_inputs:
							self.assertIsInstance(__A							,		np.ndarray	)

						# Test not batched input
						snake_case__			:				Union[str, Any]       =  image_processing(image_inputs[0]							,		return_tensors="pt"	).pixel_values

						snake_case__,						snake_case__			:				Union[str, Any]       =  self.image_processor_tester.get_expected_values(__A	)

						self.assertEqual(
						    encoded_images.shape							,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,		)

						# Test batched
						snake_case__			:				Union[str, Any]       =  image_processing(__A							,		return_tensors="pt"	).pixel_values

						snake_case__,						snake_case__			:				Any       =  self.image_processor_tester.get_expected_values(__A							,		batched=__A	)

						self.assertEqual(
						    encoded_images.shape							,		(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,		)



					def 	_lowercase					( self					:  Union[str, Any]	):
						# Initialize image_processing
						snake_case__			:				str       =  self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						snake_case__			:				List[Any]       =  prepare_image_inputs(self.image_processor_tester							,		equal_resolution=__A							,		torchify=__A	)
						for image in image_inputs:
							self.assertIsInstance(__A							,		torch.Tensor	)

						# Test not batched input
						snake_case__			:				Tuple       =  image_processing(image_inputs[0]							,		return_tensors="pt"	).pixel_values

						snake_case__,						snake_case__			:				List[Any]       =  self.image_processor_tester.get_expected_values(__A	)

						self.assertEqual(
						    encoded_images.shape							,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,		)

						# Test batched
						snake_case__			:				Optional[Any]       =  image_processing(__A							,		return_tensors="pt"	).pixel_values

						snake_case__,						snake_case__			:				int       =  self.image_processor_tester.get_expected_values(__A							,		batched=__A	)

						self.assertEqual(
						    encoded_images.shape							,		(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,		)



					@slow
					def 	_lowercase					( self					:  Optional[int]	):
						# prepare image and target
						snake_case__			:				List[Any]       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
						with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"							,		"r"	) as f:
							snake_case__			:				Any       =  json.loads(f.read()	)

						snake_case__			:				List[str]       =  {"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						snake_case__			:				Optional[Any]       =  DeformableDetrImageProcessor()
						snake_case__			:				Tuple       =  image_processing(images=__A							,		annotations=__A							,		return_tensors="pt"	)

						# verify pixel values
						snake_case__			:				str       =  torch.Size([1, 3, 8_0_0, 1_0_6_6]	)
						self.assertEqual(encoding["pixel_values"].shape							,		__A	)

						snake_case__			:				int       =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]	)
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,		__A							,		atol=1e-4	)	)

						# verify area
						snake_case__			:				int       =  torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,		__A	)	)
						# verify boxes
						snake_case__			:				str       =  torch.Size([6, 4]	)
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,		__A	)
						snake_case__			:				List[Any]       =  torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,		__A							,		atol=1e-3	)	)
						# verify image_id
						snake_case__			:				int       =  torch.tensor([3_9_7_6_9]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,		__A	)	)
						# verify is_crowd
						snake_case__			:				Dict       =  torch.tensor([0, 0, 0, 0, 0, 0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,		__A	)	)
						# verify class_labels
						snake_case__			:				Optional[int]       =  torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,		__A	)	)
						# verify orig_size
						snake_case__			:				Union[str, Any]       =  torch.tensor([4_8_0, 6_4_0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,		__A	)	)
						# verify size
						snake_case__			:				List[str]       =  torch.tensor([8_0_0, 1_0_6_6]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,		__A	)	)






					@slow
					def 	_lowercase					( self					:  Union[str, Any]	):
						# prepare image, target and masks_path
						snake_case__			:				str       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
						with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"							,		"r"	) as f:
							snake_case__			:				Optional[int]       =  json.loads(f.read()	)

						snake_case__			:				Any       =  {"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

						snake_case__			:				List[Any]       =  pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"	)

						# encode them
						snake_case__			:				Dict       =  DeformableDetrImageProcessor(format="coco_panoptic"	)
						snake_case__			:				List[Any]       =  image_processing(images=__A							,		annotations=__A							,		masks_path=__A							,		return_tensors="pt"	)

						# verify pixel values
						snake_case__			:				Optional[int]       =  torch.Size([1, 3, 8_0_0, 1_0_6_6]	)
						self.assertEqual(encoding["pixel_values"].shape							,		__A	)

						snake_case__			:				Tuple       =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]	)
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,		__A							,		atol=1e-4	)	)

						# verify area
						snake_case__			:				Any       =  torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,		__A	)	)
						# verify boxes
						snake_case__			:				Union[str, Any]       =  torch.Size([6, 4]	)
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,		__A	)
						snake_case__			:				Tuple       =  torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,		__A							,		atol=1e-3	)	)
						# verify image_id
						snake_case__			:				Union[str, Any]       =  torch.tensor([3_9_7_6_9]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,		__A	)	)
						# verify is_crowd
						snake_case__			:				List[Any]       =  torch.tensor([0, 0, 0, 0, 0, 0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,		__A	)	)
						# verify class_labels
						snake_case__			:				List[str]       =  torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,		__A	)	)
						# verify masks
						snake_case__			:				Optional[Any]       =  8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,		__A	)
						# verify orig_size
						snake_case__			:				Dict       =  torch.tensor([4_8_0, 6_4_0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,		__A	)	)
						# verify size
						snake_case__			:				Dict       =  torch.tensor([8_0_0, 1_0_6_6]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,		__A	)	)



