

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 				SCREAMING_SNAKE_CASE__ :





    def __init__(				self						,       a						,       a=13						,       a=64						,       a=2						,       a=3						,       a=True						,       a=True						,       a=32						,       a=5						,       a=4						,       a=37						,       a="gelu"						,       a=0.1						,       a=0.1						,       a=10						,       a=0.02						,       a=[1, 16, 4, 4]						,       a=None						,       ):
         lowercase__   :       List[Any]				=	parent
         lowercase__   :       Optional[Any]				=	batch_size
         lowercase__   :       Dict				=	image_size
         lowercase__   :       Optional[Any]				=	patch_size
         lowercase__   :       List[Any]				=	num_channels
         lowercase__   :       Any				=	is_training
         lowercase__   :       str				=	use_labels
         lowercase__   :       Optional[Any]				=	hidden_size
         lowercase__   :       Optional[int]				=	num_hidden_layers
         lowercase__   :       List[str]				=	num_attention_heads
         lowercase__   :       Union[str, Any]				=	intermediate_size
         lowercase__   :       Optional[Any]				=	hidden_act
         lowercase__   :       Any				=	hidden_dropout_prob
         lowercase__   :       Tuple				=	attention_probs_dropout_prob
         lowercase__   :       Optional[int]				=	type_sequence_label_size
         lowercase__   :       Union[str, Any]				=	initializer_range
         lowercase__   :       Tuple				=	scope
         lowercase__   :       str				=	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
         lowercase__   :       Any				=	(self.image_size // 32) ** 2
         lowercase__   :       Optional[Any]				=	num_patches + 1





    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

         lowercase__   :       Dict				=	None
         if self.use_labels:
              lowercase__   :       Optional[int]				=	ids_tensor([self.batch_size]						,       self.type_sequence_label_size)

         lowercase__   :       Tuple				=	self.get_config()

         return config, pixel_values, labels





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	{
             'global_padding': 'same',
             'layer_type': 'bottleneck',
             'depths': [3, 4, 9],
             'out_features': ['stage1', 'stage2', 'stage3'],
             'embedding_dynamic_padding': True,
             'hidden_sizes': [4, 8, 16, 32],
             '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=__lowercase						,       initializer_range=self.initializer_range						,       backbone_featmap_shape=self.backbone_featmap_shape						,       backbone_config=__lowercase						,       )





    def 				snake_case_    (				self						,       a						,       a						,       a):
         lowercase__   :       Optional[int]				=	ViTHybridModel(config=__lowercase)
         model.to(__lowercase)
         model.eval()
         lowercase__   :       str				=	model(__lowercase)
         self.parent.assertEqual(result.last_hidden_state.shape						,       (self.batch_size, self.seq_length, self.hidden_size))





    def 				snake_case_    (				self						,       a						,       a						,       a):
         lowercase__   :       int				=	self.type_sequence_label_size
         lowercase__   :       List[Any]				=	ViTHybridForImageClassification(__lowercase)
         model.to(__lowercase)
         model.eval()
         lowercase__   :       Optional[Any]				=	model(__lowercase						,       labels=__lowercase)
         self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.type_sequence_label_size))





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.prepare_config_and_inputs()
         lowercase__				,			lowercase__				,			lowercase__   :       Union[str, Any]				=	config_and_inputs
         lowercase__   :       List[Any]				=	{'pixel_values': pixel_values}
         return config, inputs_dict



@require_torch
class 				SCREAMING_SNAKE_CASE__ (__A		,	__A		,	unittest.TestCase					):
    __lowerCamelCase						:	Optional[int]														=					(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
    __lowerCamelCase						:	Optional[int]														=					(
        {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
        if is_torch_available()
        else {}
    )
    __lowerCamelCase						:	Optional[Any]														=					False
    __lowerCamelCase						:	str														=					False
    __lowerCamelCase						:	Tuple														=					False





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	ViTHybridModelTester(self)
         lowercase__   :       List[str]				=	ConfigTester(self						,       config_class=__lowercase						,       has_text_modality=__lowercase						,       hidden_size=37)





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





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





    def 				snake_case_    (				self):
         lowercase__				,			lowercase__   :       List[str]				=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowercase__   :       Union[str, Any]				=	model_class(__lowercase)
              self.assertIsInstance(model.get_input_embeddings()						,       (nn.Module))
              lowercase__   :       List[str]				=	model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(__lowercase						,       nn.Linear))





    def 				snake_case_    (				self):
         lowercase__				,			lowercase__   :       Union[str, Any]				=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowercase__   :       List[Any]				=	model_class(__lowercase)
              lowercase__   :       str				=	inspect.signature(model.forward)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              lowercase__   :       int				=	[*signature.parameters.keys()]

              lowercase__   :       Dict				=	['pixel_values']
              self.assertListEqual(arg_names[:1]						,       __lowercase)





    def 				snake_case_    (				self):
         lowercase__   :       List[Any]				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowercase)





    def 				snake_case_    (				self):
         lowercase__   :       List[Any]				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__lowercase)





    def 				snake_case_    (				self):
         lowercase__				,			lowercase__   :       List[str]				=	self.model_tester.prepare_config_and_inputs_for_common()

         lowercase__   :       List[Any]				=	_config_zero_init(__lowercase)
         for model_class in self.all_model_classes:
              lowercase__   :       Optional[Any]				=	model_class(config=__lowercase)
              # Skip the check for the backbone
              for name, module in model.named_modules():
                   if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                        lowercase__   :       Union[str, Any]				=	[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 				snake_case_    (				self):
         for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowercase__   :       Dict				=	ViTHybridModel.from_pretrained(__lowercase)
              self.assertIsNotNone(__lowercase)



def      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
     return image


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





    @cached_property
    def 				snake_case_    (				self):
         return (
             ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
             if is_vision_available()
             else None
         )





    @slow
    def 				snake_case_    (				self):
         lowercase__   :       Optional[int]				=	ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
             __lowercase)

         lowercase__   :       Optional[Any]				=	self.default_image_processor
         lowercase__   :       int				=	prepare_img()
         lowercase__   :       Dict				=	image_processor(images=__lowercase						,       return_tensors='pt').to(__lowercase)

         # forward pass
         with torch.no_grad():
              lowercase__   :       Union[str, Any]				=	model(**__lowercase)

         # verify the logits
         lowercase__   :       Tuple				=	torch.Size((1, 1000))
         self.assertEqual(outputs.logits.shape						,       __lowercase)

         lowercase__   :       List[Any]				=	torch.tensor([-1.9_090, -0.4_993, -0.2_389]).to(__lowercase)

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





    @slow
    @require_accelerate
    def 				snake_case_    (				self):
         lowercase__   :       str				=	ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384')
         lowercase__   :       Any				=	ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'						,       device_map='auto')

         lowercase__   :       str				=	prepare_img()

         lowercase__   :       str				=	image_processor(images=__lowercase						,       return_tensors='pt')
         lowercase__   :       Any				=	model(**__lowercase)
         lowercase__   :       int				=	outputs.logits
         # model predicts one of the 1000 ImageNet classes
         lowercase__   :       List[str]				=	logits.argmax(-1).item()

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


import os

try:
       from .build_directory_md import good_file_paths
except ImportError:
       from build_directory_md import good_file_paths  # type: ignore

snake_case_        =			list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

snake_case_        =			[file for file in filepaths if file != file.lower()]
if upper_files:
       print(F'''{len(upper_files)} files contain uppercase characters:''')
       print('''\n'''.join(upper_files) + '''\n''')

snake_case_        =			[file for file in filepaths if ''' ''' in file]
if space_files:
       print(F'''{len(space_files)} files contain space characters:''')
       print('''\n'''.join(space_files) + '''\n''')

snake_case_        =			[file for file in filepaths if '''-''' in file]
if hyphen_files:
       print(F'''{len(hyphen_files)} files contain hyphen characters:''')
       print('''\n'''.join(hyphen_files) + '''\n''')

snake_case_        =			[file for file in filepaths if os.sep not in file]
if nodir_files:
       print(F'''{len(nodir_files)} files are not in a directory:''')
       print('''\n'''.join(nodir_files) + '''\n''')

snake_case_        =			len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
       import sys

       sys.exit(bad_files)

