




import inspect
import unittest

from transformers import ResNetConfig
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_backbone_common import BackboneTesterMixin
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 ResNetBackbone, ResNetForImageClassification, ResNetModel
				from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor
class    __lowerCAmelCase			:







			def __init__(	self   :      Any   ,   snake_case__   :      List[str]   ,   snake_case__   :      Union[str, Any]=3   ,   snake_case__   :      Optional[Any]=32   ,   snake_case__   :      Dict=3   ,   snake_case__   :      int=10   ,   snake_case__   :      Optional[int]=[10, 20, 30, 40]   ,   snake_case__   :      Optional[Any]=[1, 1, 2, 1]   ,   snake_case__   :      Union[str, Any]=True   ,   snake_case__   :      Union[str, Any]=True   ,   snake_case__   :      Any="relu"   ,   snake_case__   :      Any=3   ,   snake_case__   :      Any=None   ,   snake_case__   :      Tuple=["stage2", "stage3", "stage4"]   ,   snake_case__   :      Optional[int]=[2, 3, 4]   ,   ):




									"""simple docstring"""


									_UpperCAmelCase													=					parent
									_UpperCAmelCase													=					batch_size
									_UpperCAmelCase													=					image_size
									_UpperCAmelCase													=					num_channels
									_UpperCAmelCase													=					embeddings_size
									_UpperCAmelCase													=					hidden_sizes
									_UpperCAmelCase													=					depths
									_UpperCAmelCase													=					is_training
									_UpperCAmelCase													=					use_labels
									_UpperCAmelCase													=					hidden_act
									_UpperCAmelCase													=					num_labels
									_UpperCAmelCase													=					scope
									_UpperCAmelCase													=					len(snake_case__				)
									_UpperCAmelCase													=					out_features
									_UpperCAmelCase													=					out_indices







			def UpperCamelCase      (	self   :      Dict				):




									"""simple docstring"""


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

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

									_UpperCAmelCase													=					self.get_config()

									return config, pixel_values, labels







			def UpperCamelCase      (	self   :      Optional[int]				):




									"""simple docstring"""


									return ResNetConfig(
									    num_channels=self.num_channels   ,   embeddings_size=self.embeddings_size   ,   hidden_sizes=self.hidden_sizes   ,   depths=self.depths   ,   hidden_act=self.hidden_act   ,   num_labels=self.num_labels   ,   out_features=self.out_features   ,   out_indices=self.out_indices   ,   )







			def UpperCamelCase      (	self   :      Union[str, Any]   ,   snake_case__   :      Dict   ,   snake_case__   :      Optional[int]   ,   snake_case__   :      List[str]				):




									"""simple docstring"""


									_UpperCAmelCase													=					ResNetModel(config=snake_case__				)
									model.to(snake_case__				)
									model.eval()
									_UpperCAmelCase													=					model(snake_case__				)
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape   ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,   )







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      List[str]   ,   snake_case__   :      Optional[Any]   ,   snake_case__   :      Optional[Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.num_labels
									_UpperCAmelCase													=					ResNetForImageClassification(snake_case__				)
									model.to(snake_case__				)
									model.eval()
									_UpperCAmelCase													=					model(snake_case__   ,   labels=snake_case__				)
									self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_labels)				)







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      Tuple   ,   snake_case__   :      Union[str, Any]   ,   snake_case__   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					ResNetBackbone(config=snake_case__				)
									model.to(snake_case__				)
									model.eval()
									_UpperCAmelCase													=					model(snake_case__				)

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps				)   ,   len(config.out_features				)				)
									self.parent.assertListEqual(list(result.feature_maps[0].shape				)   ,   [self.batch_size, self.hidden_sizes[1], 4, 4]				)

									# verify channels
									self.parent.assertEqual(len(model.channels				)   ,   len(config.out_features				)				)
									self.parent.assertListEqual(model.channels   ,   config.hidden_sizes[1:]				)

									# verify backbone works with out_features=None
									_UpperCAmelCase													=					None
									_UpperCAmelCase													=					ResNetBackbone(config=snake_case__				)
									model.to(snake_case__				)
									model.eval()
									_UpperCAmelCase													=					model(snake_case__				)

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps				)   ,   1				)
									self.parent.assertListEqual(list(result.feature_maps[0].shape				)   ,   [self.batch_size, self.hidden_sizes[-1], 1, 1]				)

									# verify channels
									self.parent.assertEqual(len(model.channels				)   ,   1				)
									self.parent.assertListEqual(model.channels   ,   [config.hidden_sizes[-1]]				)







			def UpperCamelCase      (	self   :      Any				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.prepare_config_and_inputs()
									_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase													=					config_and_inputs
									_UpperCAmelCase													=					{"pixel_values": pixel_values}
									return config, inputs_dict




@require_torch
class    __lowerCAmelCase			(	UpperCAmelCase__ ,		UpperCAmelCase__ ,		unittest.TestCase			):
			snake_case_				:			Dict								=					(
			    (
			        ResNetModel,
			        ResNetForImageClassification,
			        ResNetBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			snake_case_				:			Tuple								=					(
			    {"feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
			    if is_torch_available()
			    else {}
			)

			snake_case_				:			Optional[int]								=					True
			snake_case_				:			List[str]								=					False
			snake_case_				:			int								=					False
			snake_case_				:			Any								=					False
			snake_case_				:			Any								=					False







			def UpperCamelCase      (	self   :      List[Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					ResNetModelTester(self				)
									_UpperCAmelCase													=					ConfigTester(self   ,   config_class=snake_case__   ,   has_text_modality=snake_case__				)







			def UpperCamelCase      (	self   :      str				):




									"""simple docstring"""


									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()







			def UpperCamelCase      (	self   :      int				):




									"""simple docstring"""


									return







			@unittest.skip(reason="ResNet does not use inputs_embeds"				)
			def UpperCamelCase      (	self   :      str				):




									"""simple docstring"""


									pass







			@unittest.skip(reason="ResNet does not support input and output embeddings"				)
			def UpperCamelCase      (	self   :      Dict				):




									"""simple docstring"""


									pass







			def UpperCamelCase      (	self   :      str				):




									"""simple docstring"""


									_UpperCAmelCase							,			_UpperCAmelCase													=					self.model_tester.prepare_config_and_inputs_for_common()

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

															_UpperCAmelCase													=					["pixel_values"]
															self.assertListEqual(arg_names[:1]   ,   snake_case__				)







			def UpperCamelCase      (	self   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case__				)







			def UpperCamelCase      (	self   :      Optional[Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*snake_case__				)







			def UpperCamelCase      (	self   :      str				):




									"""simple docstring"""


									_UpperCAmelCase							,			_UpperCAmelCase													=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															_UpperCAmelCase													=					model_class(config=snake_case__				)
															for name, module in model.named_modules():
																					if isinstance(snake_case__   ,   (nn.BatchNormad, nn.GroupNorm)				):
																											self.assertTrue(
																											    torch.all(module.weight == 1				)   ,   msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""   ,   )
																											self.assertTrue(
																											    torch.all(module.bias == 0				)   ,   msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""   ,   )







			def UpperCamelCase      (	self   :      str				):




									"""simple docstring"""


									def check_hidden_states_output(snake_case__   :      str   ,   snake_case__   :      Dict   ,   snake_case__   :      List[Any]				):
															_UpperCAmelCase													=					model_class(snake_case__				)
															model.to(snake_case__				)
															model.eval()

															with torch.no_grad():
																					_UpperCAmelCase													=					model(**self._prepare_for_class(snake_case__   ,   snake_case__				)				)

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

															_UpperCAmelCase													=					self.model_tester.num_stages
															self.assertEqual(len(snake_case__				)   ,   expected_num_stages + 1				)

															# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]				)   ,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]   ,   )

									_UpperCAmelCase							,			_UpperCAmelCase													=					self.model_tester.prepare_config_and_inputs_for_common()
									_UpperCAmelCase													=					["basic", "bottleneck"]
									for model_class in self.all_model_classes:
															for layer_type in layers_type:
																					_UpperCAmelCase													=					layer_type
																					_UpperCAmelCase													=					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"]
																					_UpperCAmelCase													=					True

																					check_hidden_states_output(snake_case__   ,   snake_case__   ,   snake_case__				)







			@unittest.skip(reason="ResNet does not use feedforward chunking"				)
			def UpperCamelCase      (	self   :      int				):




									"""simple docstring"""


									pass







			def UpperCamelCase      (	self   :      str				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case__				)







			@slow
			def UpperCamelCase      (	self   :      Any				):




									"""simple docstring"""


									for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_UpperCAmelCase													=					ResNetModel.from_pretrained(snake_case__				)
															self.assertIsNotNone(snake_case__				)




def 							__SCREAMING_SNAKE_CASE  (      ):

						'''simple docstring'''


						_UpperCAmelCase													=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
						return image
@require_torch
@require_vision
class    __lowerCAmelCase			(	unittest.TestCase			):







			@cached_property
			def UpperCamelCase      (	self   :      Tuple				):




									"""simple docstring"""


									return (
									    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
									    if is_vision_available()
									    else None
									)







			@slow
			def UpperCamelCase      (	self   :      Dict				):




									"""simple docstring"""


									_UpperCAmelCase													=					ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(snake_case__				)

									_UpperCAmelCase													=					self.default_image_processor
									_UpperCAmelCase													=					prepare_img()
									_UpperCAmelCase													=					image_processor(images=snake_case__   ,   return_tensors="pt"				).to(snake_case__				)

									# forward pass
									with torch.no_grad():
															_UpperCAmelCase													=					model(**snake_case__				)

									# verify the logits
									_UpperCAmelCase													=					torch.Size((1, 1_000)				)
									self.assertEqual(outputs.logits.shape   ,   snake_case__				)

									_UpperCAmelCase													=					torch.tensor([-11.1_069, -9.7_877, -8.3_777]				).to(snake_case__				)

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




@require_torch
class    __lowerCAmelCase			(	UpperCAmelCase__ ,		unittest.TestCase			):
			snake_case_				:			Optional[int]								=					(ResNetBackbone,) if is_torch_available() else ()
			snake_case_				:			List[str]								=					False
			snake_case_				:			List[str]								=					ResNetConfig







			def UpperCamelCase      (	self   :      Dict				):




									"""simple docstring"""


									_UpperCAmelCase													=					ResNetModelTester(self				)










import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase_     :    Union[str, Any]      =			datasets.utils.logging.get_logger(__name__)






@dataclass
class    __lowerCAmelCase			(	datasets.BuilderConfig			):
			snake_case_				:			Optional[datasets.Features]								=					None






class    __lowerCAmelCase			(	datasets.ArrowBasedBuilder			):
			snake_case_				:			str								=					ArrowConfig







			def UpperCamelCase      (	self   :      Optional[int]				):




									"""simple docstring"""


									return datasets.DatasetInfo(features=self.config.features				)







			def UpperCamelCase      (	self   :      str   ,   snake_case__   :      Optional[int]				):




									"""simple docstring"""


									if not self.config.data_files:
															raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""				)
									_UpperCAmelCase													=					dl_manager.download_and_extract(self.config.data_files				)
									if isinstance(snake_case__   ,   (str, list, tuple)				):
															_UpperCAmelCase													=					data_files
															if isinstance(snake_case__   ,   snake_case__				):
																					_UpperCAmelCase													=					[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															_UpperCAmelCase													=					[dl_manager.iter_files(snake_case__				) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,   gen_kwargs={"files": files}				)]
									_UpperCAmelCase													=					[]
									for split_name, files in data_files.items():
															if isinstance(snake_case__   ,   snake_case__				):
																					_UpperCAmelCase													=					[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															_UpperCAmelCase													=					[dl_manager.iter_files(snake_case__				) for file in files]
															# Infer features is they are stoed in the arrow schema
															if self.info.features is None:
																					for file in itertools.chain.from_iterable(snake_case__				):
																											with open(snake_case__   ,   "rb"				) as f:
																																	_UpperCAmelCase													=					datasets.Features.from_arrow_schema(pa.ipc.open_stream(snake_case__				).schema				)
																											break
															splits.append(datasets.SplitGenerator(name=snake_case__   ,   gen_kwargs={"files": files}				)				)
									return splits







			def UpperCamelCase      (	self   :      int   ,   snake_case__   :      pa.Table				):




									"""simple docstring"""


									if self.info.features is not None:
															# more expensive cast to support nested features with keys in a different order
															# allows str <-> int/float or str to Audio for example
															_UpperCAmelCase													=					table_cast(snake_case__   ,   self.info.features.arrow_schema				)
									return pa_table







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      int				):




									"""simple docstring"""


									for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__				)				):
															with open(snake_case__   ,   "rb"				) as f:
																					try:
																											for batch_idx, record_batch in enumerate(pa.ipc.open_stream(snake_case__				)				):
																																	_UpperCAmelCase													=					pa.Table.from_batches([record_batch]				)
																																	# Uncomment for debugging (will print the Arrow table size and elements)
																																	# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																	# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																	yield F"""{file_idx}_{batch_idx}""", self._cast_table(snake_case__				)
																					except ValueError as e:
																											logger.error(F"""Failed to read file '{file}' with error {type(snake_case__				)}: {e}"""				)
																											raise




