



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 		_lowercase		:

   def __init__(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       Optional[int]=13							,   __lowerCAmelCase			:       int=64							,   __lowerCAmelCase			:       Tuple=2							,   __lowerCAmelCase			:       Dict=3							,   __lowerCAmelCase			:       Dict=True							,   __lowerCAmelCase			:       Union[str, Any]=True							,   __lowerCAmelCase			:       Union[str, Any]=32							,   __lowerCAmelCase			:       List[Any]=5							,   __lowerCAmelCase			:       List[str]=4							,   __lowerCAmelCase			:       List[Any]=37							,   __lowerCAmelCase			:       List[Any]="gelu"							,   __lowerCAmelCase			:       Union[str, Any]=0.1							,   __lowerCAmelCase			:       List[Any]=0.1							,   __lowerCAmelCase			:       List[Any]=10							,   __lowerCAmelCase			:       Union[str, Any]=0.0_2							,   __lowerCAmelCase			:       Optional[Any]=[1, 16, 4, 4]							,   __lowerCAmelCase			:       Tuple=None							,   )       ->				int:


    """simple docstring"""






    a						=		parent
    a						=		batch_size
    a						=		image_size
    a						=		patch_size
    a						=		num_channels
    a						=		is_training
    a						=		use_labels
    a						=		hidden_size
    a						=		num_hidden_layers
    a						=		num_attention_heads
    a						=		intermediate_size
    a						=		hidden_act
    a						=		hidden_dropout_prob
    a						=		attention_probs_dropout_prob
    a						=		type_sequence_label_size
    a						=		initializer_range
    a						=		scope
    a						=		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
    a						=		(self.image_size // 32) ** 2
    a						=		num_patches + 1

   def 		A							(      self			:       Tuple							)       ->				Optional[int]:


    """simple docstring"""






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

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

    a						=		self.get_config()

    return config, pixel_values, labels

   def 		A							(      self			:       Any							)       ->				List[str]:


    """simple docstring"""






    a						=		{
        "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 		A							(      self			:       Any							,   __lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       Optional[Any]							)       ->				Optional[Any]:


    """simple docstring"""






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

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       List[Any]							)       ->				Optional[int]:


    """simple docstring"""






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




   def 		A							(      self			:       Dict							)       ->				Tuple:


    """simple docstring"""






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



@require_torch
class 		_lowercase		(       UpperCAmelCase__,   UpperCAmelCase__,   unittest.TestCase	):
   _UpperCAmelCase     	=			(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
   _UpperCAmelCase     	=			(
       {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
       if is_torch_available()
       else {}
   )
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False

   def 		A							(      self			:       Union[str, Any]							)       ->				int:


    """simple docstring"""






    a						=		ViTHybridModelTester(self							)
    a						=		ConfigTester(self							,   config_class=__lowerCAmelCase							,   has_text_modality=__lowerCAmelCase							,   hidden_size=37							)

   def 		A							(      self			:       str							)       ->				Optional[int]:


    """simple docstring"""






    self.config_tester.run_common_tests()

   @unittest.skip(reason="ViT does not use inputs_embeds"							)
   def 		A							(      self			:       str							)       ->				Any:


    """simple docstring"""






    pass

   def 		A							(      self			:       Dict							)       ->				Dict:


    """simple docstring"""






    a		,  a						=		self.model_tester.prepare_config_and_inputs_for_common()

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

   def 		A							(      self			:       Tuple							)       ->				str:


    """simple docstring"""






    a		,  a						=		self.model_tester.prepare_config_and_inputs_for_common()

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

     a						=		["pixel_values"]
     self.assertListEqual(arg_names[:1]							,   __lowerCAmelCase							)

   def 		A							(      self			:       str							)       ->				Tuple:


    """simple docstring"""






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

   def 		A							(      self			:       List[Any]							)       ->				str:


    """simple docstring"""






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

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


    """simple docstring"""






    a		,  a						=		self.model_tester.prepare_config_and_inputs_for_common()

    a						=		_config_zero_init(__lowerCAmelCase							)
    for model_class in self.all_model_classes:
     a						=		model_class(config=__lowerCAmelCase							)
     # Skip the check for the backbone
     for name, module in model.named_modules():
      if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
       a						=		[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 		A							(      self			:       Tuple							)       ->				Union[str, Any]:


    """simple docstring"""






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


def 	UpperCAmelCase__      (      ):
 a						=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
 return image




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

   @cached_property
   def 		A							(      self			:       Optional[int]							)       ->				str:


    """simple docstring"""






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

   @slow
   def 		A							(      self			:       str							)       ->				List[str]:


    """simple docstring"""






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

    a						=		self.default_image_processor
    a						=		prepare_img()
    a						=		image_processor(images=__lowerCAmelCase							,   return_tensors="pt"							).to(__lowerCAmelCase							)

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

    # verify the logits
    a						=		torch.Size((1, 1000)							)
    self.assertEqual(outputs.logits.shape							,   __lowerCAmelCase							)

    a						=		torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9]							).to(__lowerCAmelCase							)

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




   @slow
   @require_accelerate
   def 		A							(      self			:       int							)       ->				int:


    """simple docstring"""






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

    a						=		prepare_img()

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

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_		:     str        =     logging.get_logger(__name__)

A_		:     List[Any]        =     {
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''rwkv'''
   _UpperCAmelCase     	=			{'''max_position_embeddings''': '''context_length'''}

   def __init__(      self			:       List[str]							,   __lowerCAmelCase			:       Union[str, Any]=5_0277							,   __lowerCAmelCase			:       str=1024							,   __lowerCAmelCase			:       Union[str, Any]=4096							,   __lowerCAmelCase			:       Optional[int]=32							,   __lowerCAmelCase			:       Optional[int]=None							,   __lowerCAmelCase			:       Optional[Any]=None							,   __lowerCAmelCase			:       List[Any]=1E-5							,   __lowerCAmelCase			:       Union[str, Any]=0							,   __lowerCAmelCase			:       Optional[Any]=0							,   __lowerCAmelCase			:       Dict=6							,   __lowerCAmelCase			:       int=False							,   __lowerCAmelCase			:       Tuple=True							,   **__lowerCAmelCase			:       List[str]							,   )       ->				List[Any]:


    """simple docstring"""






    a						=		vocab_size
    a						=		context_length
    a						=		hidden_size
    a						=		num_hidden_layers
    a						=		attention_hidden_size if attention_hidden_size is not None else hidden_size
    a						=		intermediate_size if intermediate_size is not None else 4 * hidden_size
    a						=		layer_norm_epsilon
    a						=		rescale_every
    a						=		use_cache

    a						=		bos_token_id
    a						=		eos_token_id

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

