


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFResNetForImageClassification, TFResNetModel
			from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class 							a						:




							"""simple docstring"""


							def __init__(				self			:    int       ,      lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    List[Any]=3       ,      lowerCamelCase			:    Dict=32       ,      lowerCamelCase			:    List[Any]=3       ,      lowerCamelCase			:    List[str]=10       ,      lowerCamelCase			:    List[str]=[10, 20, 30, 40]       ,      lowerCamelCase			:    Optional[Any]=[1, 1, 2, 1]       ,      lowerCamelCase			:    Union[str, Any]=True       ,      lowerCamelCase			:    Tuple=True       ,      lowerCamelCase			:    int="relu"       ,      lowerCamelCase			:    int=3       ,      lowerCamelCase			:    str=None       ,      )      ->       Optional[int]:
														__snake_case						:     str									=  parent
														__snake_case						:     List[Any]									=  batch_size
														__snake_case						:     int									=  image_size
														__snake_case						:     Optional[Any]									=  num_channels
														__snake_case						:     Any									=  embeddings_size
														__snake_case						:     Dict									=  hidden_sizes
														__snake_case						:     Tuple									=  depths
														__snake_case						:     List[str]									=  is_training
														__snake_case						:     int									=  use_labels
														__snake_case						:     str									=  hidden_act
														__snake_case						:     Tuple									=  num_labels
														__snake_case						:     Optional[int]									=  scope
														__snake_case						:     int									=  len(lowerCamelCase							)

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

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

														__snake_case						:     Any									=  self.get_config()

														return config, pixel_values, labels

							def        __snake_case   (				self			:    List[Any]							)      ->       Optional[int]:
														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       ,      image_size=self.image_size       ,      )

							def        __snake_case   (				self			:    str       ,      lowerCamelCase			:    List[str]       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    Optional[Any]							)      ->       Tuple:
														__snake_case						:     Dict									=  TFResNetModel(config=lowerCamelCase							)
														__snake_case						:     Dict									=  model(lowerCamelCase							)
														# 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        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    Union[str, Any]       ,      lowerCamelCase			:    Optional[int]							)      ->       Optional[int]:
														__snake_case						:     Optional[int]									=  self.num_labels
														__snake_case						:     List[Any]									=  TFResNetForImageClassification(lowerCamelCase							)
														__snake_case						:     List[Any]									=  model(lowerCamelCase       ,      labels=lowerCamelCase							)
														self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)






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

@require_tf
class 							a						(_lowerCAmelCase	,							_lowerCAmelCase	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[Any]	    =    (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
							__UpperCAmelCase       :				Optional[int]	    =    (
							    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
							    if is_tf_available()
							    else {}
							)

							__UpperCAmelCase       :				Tuple	    =    False
							__UpperCAmelCase       :				Tuple	    =    False
							__UpperCAmelCase       :				Optional[int]	    =    False
							__UpperCAmelCase       :				str	    =    False
							__UpperCAmelCase       :				List[str]	    =    False

							def        __snake_case   (				self			:    Optional[Any]							)      ->       Union[str, Any]:
														__snake_case						:     Any									=  TFResNetModelTester(self							)
														__snake_case						:     List[str]									=  ConfigTester(self       ,      config_class=lowerCamelCase       ,      has_text_modality=lowerCamelCase							)

							def        __snake_case   (				self			:    List[Any]							)      ->       str:
														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        __snake_case   (				self			:    List[str]							)      ->       Optional[int]:
														return

							@unittest.skip(reason="ResNet does not use inputs_embeds"							)
							def        __snake_case   (				self			:    Union[str, Any]							)      ->       List[Any]:
														pass

							@unittest.skip(reason="ResNet does not support input and output embeddings"							)
							def        __snake_case   (				self			:    Tuple							)      ->       List[Any]:
														pass

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

														for model_class in self.all_model_classes:
																					__snake_case						:     Optional[int]									=  model_class(lowerCamelCase							)
																					__snake_case						:     Optional[int]									=  inspect.signature(model.call							)
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					__snake_case						:     Tuple									=  [*signature.parameters.keys()]

																					__snake_case						:     int									=  ["pixel_values"]
																					self.assertListEqual(arg_names[:1]       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    List[Any]							)      ->       str:
														__snake_case						:     int									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase							)

							def        __snake_case   (				self			:    Tuple							)      ->       Optional[int]:
														def check_hidden_states_output(lowerCamelCase			:    int       ,      lowerCamelCase			:    Optional[Any]       ,      lowerCamelCase			:    Tuple							):
																					__snake_case						:     List[Any]									=  model_class(lowerCamelCase							)
																					__snake_case						:     Tuple									=  model(**self._prepare_for_class(lowerCamelCase       ,      lowerCamelCase							)							)

																					__snake_case						:     Optional[Any]									=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																					__snake_case						:     Optional[int]									=  self.model_tester.num_stages
																					self.assertEqual(len(lowerCamelCase							)       ,      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]       ,      )

														__snake_case					,     __snake_case						:     str									=  self.model_tester.prepare_config_and_inputs_for_common()
														__snake_case						:     Optional[int]									=  ["basic", "bottleneck"]
														for model_class in self.all_model_classes:
																					for layer_type in layers_type:
																												__snake_case						:     Tuple									=  layer_type
																												__snake_case						:     Union[str, Any]									=  True
																												check_hidden_states_output(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)

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

																												check_hidden_states_output(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    Any							)      ->       int:
														__snake_case						:     Optional[int]									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCamelCase							)






							@slow
							def        __snake_case   (				self			:    List[str]							)      ->       Optional[int]:
														for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					__snake_case						:     int									=  TFResNetModel.from_pretrained(lowerCamelCase							)
																					self.assertIsNotNone(lowerCamelCase							)







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

@require_tf
@require_vision
class 							a						(unittest.TestCase ):




							"""simple docstring"""


							@cached_property
							def        __snake_case   (				self			:    Dict							)      ->       str:
														return (
														    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
														    if is_vision_available()
														    else None
														)






							@slow
							def        __snake_case   (				self			:    Optional[int]							)      ->       Tuple:
														__snake_case						:     Optional[int]									=  TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

														__snake_case						:     List[Any]									=  self.default_image_processor
														__snake_case						:     Union[str, Any]									=  prepare_img()
														__snake_case						:     str									=  image_processor(images=lowerCamelCase       ,      return_tensors="tf"							)

														# forward pass
														__snake_case						:     Dict									=  model(**lowerCamelCase							)

														# verify the logits
														__snake_case						:     Union[str, Any]									=  tf.TensorShape((1, 1000)							)
														self.assertEqual(outputs.logits.shape       ,      lowerCamelCase							)

														__snake_case						:     List[str]									=  tf.constant([-11.10_69, -9.78_77, -8.37_77]							)

														self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()       ,      lowerCamelCase       ,      atol=1E-4							)							)







import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


a__		     =     {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						List[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	Tuple             =      torchvision.models.resnetaaa(pretrained=lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      list(model.children()	)[:-2]
						__UpperCamelCase					:	List[str]             =      nn.Sequential(*lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						Optional[Any]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.pool(self.model(lowerCAmelCase	)	)
						__UpperCamelCase					:	Dict             =      torch.flatten(lowerCAmelCase					,     start_dim=2	)
						__UpperCamelCase					:	Optional[int]             =      out.transpose(1					,     2	).contiguous()
						return out  # BxNx2048






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def __init__(					self		:						Optional[Any]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Optional[int]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      [json.loads(lowerCAmelCase	) for l in open(lowerCAmelCase	)]
						__UpperCamelCase					:	Optional[Any]             =      os.path.dirname(lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      tokenizer
						__UpperCamelCase					:	Dict             =      labels
						__UpperCamelCase					:	int             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      max_seq_length

						__UpperCamelCase					:	int             =      transforms






	def __len__(					self		:						Union[str, Any]	)	->       List[Any]:







						"""simple docstring"""







						return len(self.data	)






	def __getitem__(					self		:						Optional[Any]					,     lowerCAmelCase		:						List[Any]	)	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]					,     add_special_tokens=lowerCAmelCase	)	)
						__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      sentence[0], sentence[1:-1], sentence[-1]
						__UpperCamelCase					:	Optional[int]             =      sentence[: self.max_seq_length]

						__UpperCamelCase					:	List[str]             =      torch.zeros(self.n_classes	)
						__UpperCamelCase					:	List[str]             =      1

						__UpperCamelCase					:	List[Any]             =      Image.open(os.path.join(self.data_dir					,     self.data[index]["""img"""]	)	).convert("""RGB"""	)
						__UpperCamelCase					:	Dict             =      self.transforms(lowerCAmelCase	)

						return {
						    "image_start_token": start_token,
						    "image_end_token": end_token,
						    "sentence": sentence,
						    "image": image,
						    "label": label,
						}






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      Counter()
						for row in self.data:
											label_freqs.update(row["""label"""]	)
						return label_freqs






def 				A__ (snake_case   :		int				)     ->						Optional[int]:
					__UpperCamelCase					:	str             =      [len(row["""sentence"""]				) for row in batch]
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      len(snake_case				), max(snake_case				)

					__UpperCamelCase					:	List[str]             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)
					__UpperCamelCase					:	Tuple             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)

					for i_batch, (input_row, length) in enumerate(zip(snake_case		,	snake_case				)				):
										__UpperCamelCase					:	Optional[Any]             =      input_row["""sentence"""]
										__UpperCamelCase					:	Any             =      1

					__UpperCamelCase					:	List[Any]             =      torch.stack([row["""image"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""label"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""image_start_token"""] for row in batch]				)
					__UpperCamelCase					:	List[str]             =      torch.stack([row["""image_end_token"""] for row in batch]				)

					return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor





def 				A__ ()     ->						Optional[Any]:
					return [
					    "Crime",
					    "Drama",
					    "Thriller",
					    "Action",
					    "Comedy",
					    "Romance",
					    "Documentary",
					    "Short",
					    "Mystery",
					    "History",
					    "Family",
					    "Adventure",
					    "Fantasy",
					    "Sci-Fi",
					    "Western",
					    "Horror",
					    "Sport",
					    "War",
					    "Music",
					    "Musical",
					    "Animation",
					    "Biography",
					    "Film-Noir",
					]







def 				A__ ()     ->						Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(2_56				),
					        transforms.CenterCrop(2_24				),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46777044, 0.44531429, 0.40661017]		,	std=[0.12221994, 0.12145835, 0.14380469]		,	),
					    ]				)






