



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__					:




    '''simple docstring'''






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






    def       _lowerCAmelCase (	self		):
        __UpperCamelCase			:	str										=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        __UpperCamelCase			:	List[Any]										=       None
        if self.use_labels:
            __UpperCamelCase			:	List[Any]										=       ids_tensor([self.batch_size],		self.type_sequence_label_size		)

        __UpperCamelCase			:	Dict										=       self.get_config()

        return config, pixel_values, labels






    def       _lowerCAmelCase (	self		):
        __UpperCamelCase			:	Optional[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=lowerCamelCase__,		initializer_range=self.initializer_range,		backbone_featmap_shape=self.backbone_featmap_shape,		backbone_config=lowerCamelCase__,		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        __UpperCamelCase			:	Tuple										=       ViTHybridModel(config=lowerCamelCase__		)
        model.to(lowerCamelCase__		)
        model.eval()
        __UpperCamelCase			:	Any										=       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__		):
        __UpperCamelCase			:	Union[str, Any]										=       self.type_sequence_label_size
        __UpperCamelCase			:	Any										=       ViTHybridForImageClassification(lowerCamelCase__		)
        model.to(lowerCamelCase__		)
        model.eval()
        __UpperCamelCase			:	Dict										=       model(lowerCamelCase__,		labels=lowerCamelCase__		)
        self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.type_sequence_label_size)		)






    def       _lowerCAmelCase (	self		):
        __UpperCamelCase			:	Tuple										=       self.prepare_config_and_inputs()
        __UpperCamelCase			:	str										=       config_and_inputs
        __UpperCamelCase			:	int										=       {"""pixel_values""": pixel_values}
        return config, inputs_dict






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




    '''simple docstring'''






    __lowerCamelCase      :			Optional[Any] =				(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
    __lowerCamelCase      :			Dict =				(
        {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
        if is_torch_available()
        else {}
    )
    __lowerCamelCase      :			str =				False
    __lowerCamelCase      :			int =				False
    __lowerCamelCase      :			str =				False






    def       _lowerCAmelCase (	self		):
        __UpperCamelCase			:	str										=       ViTHybridModelTester(self		)
        __UpperCamelCase			:	List[str]										=       ConfigTester(self,		config_class=lowerCamelCase__,		has_text_modality=lowerCamelCase__,		hidden_size=37		)






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






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






    def       _lowerCAmelCase (	self		):
        __UpperCamelCase			:	List[Any]										=       self.model_tester.prepare_config_and_inputs_for_common()

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






    def       _lowerCAmelCase (	self		):
        __UpperCamelCase			:	Tuple										=       self.model_tester.prepare_config_and_inputs_for_common()

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

            __UpperCamelCase			:	List[str]										=       ["""pixel_values"""]
            self.assertListEqual(arg_names[:1],		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        __UpperCamelCase			:	Optional[int]										=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        __UpperCamelCase			:	List[str]										=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        __UpperCamelCase			:	Dict										=       self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCamelCase			:	int										=       _config_zero_init(lowerCamelCase__		)
        for model_class in self.all_model_classes:
            __UpperCamelCase			:	Tuple										=       model_class(config=lowerCamelCase__		)
            # Skip the check for the backbone
            for name, module in model.named_modules():
                if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                    __UpperCamelCase			:	Optional[int]										=       [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		):
        for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCamelCase			:	int										=       ViTHybridModel.from_pretrained(lowerCamelCase__		)
            self.assertIsNotNone(lowerCamelCase__		)




def      __UpperCamelCase     (  )	->  str:



    """simple docstring"""






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






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




    '''simple docstring'''






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






    @slow
    def       _lowerCAmelCase (	self		):
        __UpperCamelCase			:	List[str]										=       ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
            lowerCamelCase__		)

        __UpperCamelCase			:	Optional[Any]										=       self.default_image_processor
        __UpperCamelCase			:	int										=       prepare_img()
        __UpperCamelCase			:	Any										=       image_processor(images=lowerCamelCase__,		return_tensors="""pt"""		).to(lowerCamelCase__		)

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

        # verify the logits
        __UpperCamelCase			:	Optional[Any]										=       torch.Size((1, 1000)		)
        self.assertEqual(outputs.logits.shape,		lowerCamelCase__		)

        __UpperCamelCase			:	Union[str, Any]										=       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		):
        __UpperCamelCase			:	List[Any]										=       ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""		)
        __UpperCamelCase			:	str										=       ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384""",		device_map="""auto"""		)

        __UpperCamelCase			:	List[str]										=       prepare_img()

        __UpperCamelCase			:	Union[str, Any]										=       image_processor(images=lowerCamelCase__,		return_tensors="""pt"""		)
        __UpperCamelCase			:	Dict										=       model(**lowerCamelCase__		)
        __UpperCamelCase			:	List[Any]										=       outputs.logits
        # model predicts one of the 1000 ImageNet classes
        __UpperCamelCase			:	Any										=       logits.argmax(-1		).item()

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







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
SCREAMING_SNAKE_CASE_:Any					=						"""src/diffusers"""

# Matches is_xxx_available()
SCREAMING_SNAKE_CASE_:Optional[Any]					=						re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
SCREAMING_SNAKE_CASE_:Union[str, Any]					=						re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


SCREAMING_SNAKE_CASE_:List[Any]					=						"""
{0} = None
"""

SCREAMING_SNAKE_CASE_:Optional[Any]					=						"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


SCREAMING_SNAKE_CASE_:int					=						"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






    A			:	Union[str, Any]										=       _re_backend.findall(_lowerCAmelCase		)
    if len(_lowerCAmelCase		) == 0:
        return None

    return "_and_".join(_lowerCAmelCase		)




def      __UpperCamelCase     (  )	->  str:



    """simple docstring"""






    with open(os.path.join(_lowerCAmelCase				,					"""__init__.py"""		)				,					"""r"""				,					encoding="""utf-8"""				,					newline="""\n"""		) as f:
        A			:	Dict										=       f.readlines()

    # Get to the point we do the actual imports for type checking
    A			:	Dict										=       0
    A			:	List[Any]										=       {}
    # Go through the end of the file
    while line_index < len(_lowerCAmelCase		):
        # If the line contains is_backend_available, we grab all objects associated with the `else` block
        A			:	Optional[int]										=       find_backend(lines[line_index]		)
        if backend is not None:
            while not lines[line_index].startswith("""else:"""		):
                line_index += 1
            line_index += 1
            A			:	str										=       []
            # Until we unindent, add backend objects to the list
            while line_index < len(_lowerCAmelCase		) and len(lines[line_index]		) > 1:
                A			:	Tuple										=       lines[line_index]
                A			:	List[str]										=       _re_single_line_import.search(_lowerCAmelCase		)
                if single_line_import_search is not None:
                    objects.extend(single_line_import_search.groups()[0].split(""", """		)		)
                elif line.startswith(""" """ * 8		):
                    objects.append(line[8:-2]		)
                line_index += 1

            if len(_lowerCAmelCase		) > 0:
                A			:	List[str]										=       objects
        else:
            line_index += 1

    return backend_specific_objects




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  str:



    """simple docstring"""






    if name.isupper():
        return DUMMY_CONSTANT.format(_lowerCAmelCase		)
    elif name.islower():
        return DUMMY_FUNCTION.format(_lowerCAmelCase				,					_lowerCAmelCase		)
    else:
        return DUMMY_CLASS.format(_lowerCAmelCase				,					_lowerCAmelCase		)




def      __UpperCamelCase     (  _lowerCAmelCase=None		)	->  Tuple:



    """simple docstring"""






    if backend_specific_objects is None:
        A			:	Union[str, Any]										=       read_init()
    # For special correspondence backend to module name as used in the function requires_modulename
    A			:	Any										=       {}

    for backend, objects in backend_specific_objects.items():
        A			:	str										=       """[""" + """, """.join(f'''"{b}"''' for b in backend.split("""_and_"""		)		) + """]"""
        A			:	Any										=       """# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
        dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
        dummy_file += "\n".join([create_dummy_object(_lowerCAmelCase				,					_lowerCAmelCase		) for o in objects]		)
        A			:	Optional[Any]										=       dummy_file

    return dummy_files




def      __UpperCamelCase     (  _lowerCAmelCase=False		)	->  str:



    """simple docstring"""






    A			:	str										=       create_dummy_files()
    # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
    A			:	List[str]										=       {"""torch""": """pt"""}

    # Locate actual dummy modules and read their content.
    A			:	Union[str, Any]										=       os.path.join(_lowerCAmelCase				,					"""utils"""		)
    A			:	Any										=       {
        backend: os.path.join(_lowerCAmelCase				,					f'''dummy_{short_names.get(_lowerCAmelCase				,					_lowerCAmelCase		)}_objects.py'''		)
        for backend in dummy_files.keys()
    }

    A			:	List[Any]										=       {}
    for backend, file_path in dummy_file_paths.items():
        if os.path.isfile(_lowerCAmelCase		):
            with open(_lowerCAmelCase				,					"""r"""				,					encoding="""utf-8"""				,					newline="""\n"""		) as f:
                A			:	Dict										=       f.read()
        else:
            A			:	str										=       """"""

    for backend in dummy_files.keys():
        if dummy_files[backend] != actual_dummies[backend]:
            if overwrite:
                print(
                    f'''Updating diffusers.utils.dummy_{short_names.get(_lowerCAmelCase				,					_lowerCAmelCase		)}_objects.py as the main '''
                    """__init__ has new objects."""		)
                with open(dummy_file_paths[backend]				,					"""w"""				,					encoding="""utf-8"""				,					newline="""\n"""		) as f:
                    f.write(dummy_files[backend]		)
            else:
                raise ValueError(
                    """The main __init__ has objects that are not present in """
                    f'''diffusers.utils.dummy_{short_names.get(_lowerCAmelCase				,					_lowerCAmelCase		)}_objects.py. Run `make fix-copies` '''
                    """to fix this."""		)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:List[Any]					=						argparse.ArgumentParser()
     parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
     SCREAMING_SNAKE_CASE_:Any					=						parser.parse_args()

     check_dummies(args.fix_and_overwrite)


