





import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
			from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image






class 					__A    :

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=1_3       ,  __lowerCAmelCase=6_4       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=3       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=3_2       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=4       ,  __lowerCAmelCase=3_7       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=1_0       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=[1, 1_6, 4, 4]       ,  __lowerCAmelCase=None       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		parent
				lowerCamelCase__								=		batch_size
				lowerCamelCase__								=		image_size
				lowerCamelCase__								=		patch_size
				lowerCamelCase__								=		num_channels
				lowerCamelCase__								=		is_training
				lowerCamelCase__								=		use_labels
				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		hidden_act
				lowerCamelCase__								=		hidden_dropout_prob
				lowerCamelCase__								=		attention_probs_dropout_prob
				lowerCamelCase__								=		type_sequence_label_size
				lowerCamelCase__								=		initializer_range
				lowerCamelCase__								=		scope
				lowerCamelCase__								=		backbone_featmap_shape

				# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				# the number of patches is based on the feature map of the backbone, which by default uses an output stride
				# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
				lowerCamelCase__								=		(self.image_size // 3_2) ** 2
				lowerCamelCase__								=		num_patches + 1





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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

				lowerCamelCase__								=		None
				if self.use_labels:
					lowerCamelCase__								=		ids_tensor([self.batch_size]       ,  self.type_sequence_label_size							)

				lowerCamelCase__								=		self.get_config()

				return config, pixel_values, labels





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		{
				    '''global_padding''': '''same''',
				    '''layer_type''': '''bottleneck''',
				    '''depths''': [3, 4, 9],
				    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
				    '''embedding_dynamic_padding''': True,
				    '''hidden_sizes''': [4, 8, 1_6, 3_2],
				    '''num_groups''': 2,
				}

				return ViTHybridConfig(
				    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=__lowerCAmelCase       ,  initializer_range=self.initializer_range       ,  backbone_featmap_shape=self.backbone_featmap_shape       ,  backbone_config=__lowerCAmelCase       ,  )





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		ViTHybridModel(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(__lowerCAmelCase							)
				self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.type_sequence_label_size
				lowerCamelCase__								=		ViTHybridForImageClassification(__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(__lowerCAmelCase       ,  labels=__lowerCAmelCase							)
				self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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






@require_torch
class 					__A    (    lowerCAmelCase			,				lowerCAmelCase			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
			lowerCAmelCase_							     =						(
			    {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
			    if is_torch_available()
			    else {}
			)
			lowerCAmelCase_							     =						False
			lowerCAmelCase_							     =						False
			lowerCAmelCase_							     =						False





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ViTHybridModelTester(self							)
				lowerCamelCase__								=		ConfigTester(self       ,  config_class=__lowerCAmelCase       ,  has_text_modality=__lowerCAmelCase       ,  hidden_size=3_7							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self.config_tester.run_common_tests()





			@unittest.skip(reason='''ViT does not use inputs_embeds'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__				,	lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					lowerCamelCase__								=		model_class(__lowerCAmelCase							)
					self.assertIsInstance(model.get_input_embeddings()       ,  (nn.Module)							)
					lowerCamelCase__								=		model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(__lowerCAmelCase       ,  nn.Linear							)							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__				,	lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs_for_common()

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

					lowerCamelCase__								=		['''pixel_values''']
					self.assertListEqual(arg_names[:1]       ,  __lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__				,	lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs_for_common()

				lowerCamelCase__								=		_config_zero_init(__lowerCAmelCase							)
				for model_class in self.all_model_classes:
					lowerCamelCase__								=		model_class(config=__lowerCAmelCase							)
					# Skip the check for the backbone
					for name, module in model.named_modules():
						if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
							lowerCamelCase__								=		[F'{name}.{key}' for key in module.state_dict().keys()]
							break

					for name, param in model.named_parameters():
						if param.requires_grad:
							if name in backbone_params:
								continue
							self.assertIn(
							    ((param.data.mean() * 1E9).round() / 1E9).item()       ,  [0.0, 1.0]       ,  msg=F'Parameter {name} of model {model_class} seems not properly initialized'       ,  )





			@slow
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase__								=		ViTHybridModel.from_pretrained(__lowerCAmelCase							)
					self.assertIsNotNone(__lowerCAmelCase							)





def lowerCAmelCase__()				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
	return image






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

			'''simple docstring'''







			@cached_property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return (
				    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
				    if is_vision_available()
				    else None
				)





			@slow
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
				    __lowerCAmelCase							)

				lowerCamelCase__								=		self.default_image_processor
				lowerCamelCase__								=		prepare_img()
				lowerCamelCase__								=		image_processor(images=__lowerCAmelCase       ,  return_tensors='''pt'''							).to(__lowerCAmelCase							)

				# forward pass
				with torch.no_grad():
					lowerCamelCase__								=		model(**__lowerCAmelCase							)

				# verify the logits
				lowerCamelCase__								=		torch.Size((1, 1_0_0_0)							)
				self.assertEqual(outputs.logits.shape       ,  __lowerCAmelCase							)

				lowerCamelCase__								=		torch.tensor([-1.9090, -0.4993, -0.2389]							).to(__lowerCAmelCase							)

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





			@slow
			@require_accelerate
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384'''							)
				lowerCamelCase__								=		ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''       ,  device_map='''auto'''							)

				lowerCamelCase__								=		prepare_img()

				lowerCamelCase__								=		image_processor(images=__lowerCAmelCase       ,  return_tensors='''pt'''							)
				lowerCamelCase__								=		model(**__lowerCAmelCase							)
				lowerCamelCase__								=		outputs.logits
				# model predicts one of the 1000 ImageNet classes
				lowerCamelCase__								=		logits.argmax(-1							).item()

				self.assertTrue(model.config.idalabel[predicted_class_idx]       ,  '''tabby, tabby cat'''							)










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a 						=      logging.get_logger(__name__)

_a 						=      {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""camembert"""





			def __init__(    self       ,  __lowerCAmelCase=3_0_5_2_2       ,  __lowerCAmelCase=7_6_8       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=3_0_7_2       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=5_1_2       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=1E-12       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase="absolute"       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				super().__init__(pad_token_id=__lowerCAmelCase       ,  bos_token_id=__lowerCAmelCase       ,  eos_token_id=__lowerCAmelCase       ,  **__lowerCAmelCase							)

				lowerCamelCase__								=		vocab_size
				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		hidden_act
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		hidden_dropout_prob
				lowerCamelCase__								=		attention_probs_dropout_prob
				lowerCamelCase__								=		max_position_embeddings
				lowerCamelCase__								=		type_vocab_size
				lowerCamelCase__								=		initializer_range
				lowerCamelCase__								=		layer_norm_eps
				lowerCamelCase__								=		position_embedding_type
				lowerCamelCase__								=		use_cache
				lowerCamelCase__								=		classifier_dropout






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				if self.task == "multiple-choice":
					lowerCamelCase__								=		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
					lowerCamelCase__								=		{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				    ]							)



