


'''simple docstring'''

import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
       from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import GLPNImageProcessor







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.config_class(**self.inputs_dict       )
    self.parent.assertTrue(hasattr(a_,       "hidden_sizes"       )       )
    self.parent.assertTrue(hasattr(a_,       "num_attention_heads"       )       )
    self.parent.assertTrue(hasattr(a_,       "num_encoder_blocks"       )       )







class 			UpperCAmelCase			:

 def __init__(			self			:						Optional[int],       a_			:						Any,       a_			:						Tuple=13,       a_			:						int=64,       a_			:						List[str]=3,       a_			:						Optional[int]=4,       a_			:						List[Any]=[2, 2, 2, 2],       a_			:						Optional[int]=[8, 4, 2, 1],       a_			:						int=[16, 32, 64, 128],       a_			:						Any=[1, 4, 8, 16],       a_			:						List[str]=[1, 2, 4, 8],       a_			:						Union[str, Any]=True,       a_			:						List[str]=True,       a_			:						Tuple="gelu",       a_			:						Union[str, Any]=0.1,       a_			:						List[str]=0.1,       a_			:						Union[str, Any]=0.02,       a_			:						Union[str, Any]=16,       a_			:						Dict=3,       a_			:						List[Any]=None,       ):





    """simple docstring"""



    UpperCamelCase__			    =    parent
    UpperCamelCase__			    =    batch_size
    UpperCamelCase__			    =    image_size
    UpperCamelCase__			    =    num_channels
    UpperCamelCase__			    =    num_encoder_blocks
    UpperCamelCase__			    =    sr_ratios
    UpperCamelCase__			    =    depths
    UpperCamelCase__			    =    hidden_sizes
    UpperCamelCase__			    =    downsampling_rates
    UpperCamelCase__			    =    num_attention_heads
    UpperCamelCase__			    =    is_training
    UpperCamelCase__			    =    use_labels
    UpperCamelCase__			    =    hidden_act
    UpperCamelCase__			    =    hidden_dropout_prob
    UpperCamelCase__			    =    attention_probs_dropout_prob
    UpperCamelCase__			    =    initializer_range
    UpperCamelCase__			    =    decoder_hidden_size
    UpperCamelCase__			    =    num_labels
    UpperCamelCase__			    =    scope

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



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

    UpperCamelCase__			    =    None
    if self.use_labels:
       UpperCamelCase__			    =    ids_tensor([self.batch_size, self.image_size, self.image_size],       self.num_labels       )

    UpperCamelCase__			    =    self.get_config()
    return config, pixel_values, labels

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    return GLPNConfig(
        image_size=self.image_size,       num_channels=self.num_channels,       num_encoder_blocks=self.num_encoder_blocks,       depths=self.depths,       hidden_sizes=self.hidden_sizes,       num_attention_heads=self.num_attention_heads,       hidden_act=self.hidden_act,       hidden_dropout_prob=self.hidden_dropout_prob,       attention_probs_dropout_prob=self.attention_probs_dropout_prob,       initializer_range=self.initializer_range,       decoder_hidden_size=self.decoder_hidden_size,       )

 def 		lowercase_    (			self			:						List[Any],       a_			:						Union[str, Any],       a_			:						Optional[Any],       a_			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    GLPNModel(config=a_       )
    model.to(a_       )
    model.eval()
    UpperCamelCase__			    =    model(a_       )
    UpperCamelCase__			    =    UpperCamelCase__			    =    self.image_size // (self.downsampling_rates[-1] * 2)
    self.parent.assertEqual(
        result.last_hidden_state.shape,       (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)       )

 def 		lowercase_    (			self			:						int,       a_			:						List[str],       a_			:						Optional[Any],       a_			:						Tuple       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.num_labels
    UpperCamelCase__			    =    GLPNForDepthEstimation(a_       )
    model.to(a_       )
    model.eval()
    UpperCamelCase__			    =    model(a_       )
    self.parent.assertEqual(result.predicted_depth.shape,       (self.batch_size, self.image_size, self.image_size)       )
    UpperCamelCase__			    =    model(a_,       labels=a_       )
    self.parent.assertEqual(result.predicted_depth.shape,       (self.batch_size, self.image_size, self.image_size)       )

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



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







@require_torch
class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       str            =       (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 _lowerCamelCase			:       Any            =       (
     {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
 )

 _lowerCamelCase			:       Optional[Any]            =       False
 _lowerCamelCase			:       Any            =       False
 _lowerCamelCase			:       Tuple            =       False

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    GLPNModelTester(self       )
    UpperCamelCase__			    =    GLPNConfigTester(self,       config_class=a_       )

 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    self.config_tester.run_common_tests()

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a_       )

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*a_       )

 @unittest.skip("GLPN does not use inputs_embeds"       )
 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    pass

 @unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"       )
 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    pass

 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCamelCase__			    =    ["pixel_values"]
       self.assertListEqual(arg_names[:1],       a_       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase__			    =    True

    for model_class in self.all_model_classes:
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    False
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.eval()
       with torch.no_grad():
          UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )
       UpperCamelCase__			    =    outputs.attentions

       UpperCamelCase__			    =    sum(self.model_tester.depths       )
       self.assertEqual(len(a_       ),       a_       )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.eval()
       with torch.no_grad():
          UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )
       UpperCamelCase__			    =    outputs.attentions

       self.assertEqual(len(a_       ),       a_       )

       # verify the first attentions (first block, first layer)
       UpperCamelCase__			    =    (self.model_tester.image_size // 4) ** 2
       UpperCamelCase__			    =    (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(attentions[0].shape[-3:]       ),       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],       )

       # verify the last attentions (last block, last layer)
       UpperCamelCase__			    =    (self.model_tester.image_size // 32) ** 2
       UpperCamelCase__			    =    (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
       self.assertListEqual(
           list(attentions[-1].shape[-3:]       ),       [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len],       )
       UpperCamelCase__			    =    len(a_       )

       # Check attention is always last and order is fine
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.eval()
       with torch.no_grad():
          UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )

       self.assertEqual(out_len + 1,       len(a_       )       )

       UpperCamelCase__			    =    outputs.attentions

       self.assertEqual(len(a_       ),       a_       )
       # verify the first attentions (first block, first layer)
       UpperCamelCase__			    =    (self.model_tester.image_size // 4) ** 2
       UpperCamelCase__			    =    (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(self_attentions[0].shape[-3:]       ),       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],       )

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    def check_hidden_states_output(a_			:						Union[str, Any],       a_			:						int,       a_			:						List[Any]       ):
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.eval()

       with torch.no_grad():
          UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )

       UpperCamelCase__			    =    outputs.hidden_states

       UpperCamelCase__			    =    self.model_tester.num_encoder_blocks
       self.assertEqual(len(a_       ),       a_       )

       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-3:]       ),       [
               self.model_tester.hidden_sizes[0],
               self.model_tester.image_size // 4,
               self.model_tester.image_size // 4,
           ],       )

    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       UpperCamelCase__			    =    True
       check_hidden_states_output(a_,       a_,       a_       )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       UpperCamelCase__			    =    True

       check_hidden_states_output(a_,       a_,       a_       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    if not self.model_tester.is_training:
       return

    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase__			    =    True

    for model_class in self.all_model_classes:
       if model_class in get_values(a_       ):
          continue
       # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       # this can then be incorporated into _prepare_for_class in test_modeling_common.py
       if model_class.__name__ == "GLPNForDepthEstimation":
          UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    inputs_dict["pixel_values"].shape
          UpperCamelCase__			    =    torch.zeros(
              [self.model_tester.batch_size, height, width],       device=a_       ).long()
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.train()
       UpperCamelCase__			    =    self._prepare_for_class(a_,       a_,       return_labels=a_       )
       UpperCamelCase__			    =    model(**a_       ).loss
       loss.backward()

 @slow
 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCamelCase__			    =    GLPNModel.from_pretrained(a_       )
       self.assertIsNotNone(a_       )







def 							SCREAMING_SNAKE_CASE__(		)		->       int:





   '''simple docstring'''







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







@require_torch
@require_vision
@slow
class 			UpperCAmelCase			(							unittest.TestCase):

 @slow
 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
    UpperCamelCase__			    =    GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(a_       )

    UpperCamelCase__			    =    prepare_img()
    UpperCamelCase__			    =    image_processor(images=a_,       return_tensors="pt"       ).to(a_       )

    # forward pass
    with torch.no_grad():
       UpperCamelCase__			    =    model(**a_       )

    # verify the predicted depth
    UpperCamelCase__			    =    torch.Size([1, 480, 640]       )
    self.assertEqual(outputs.predicted_depth.shape,       a_       )

    UpperCamelCase__			    =    torch.tensor(
        [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]       ).to(a_       )

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



'''simple docstring'''

import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					List[str]     ,		_UpperCamelCase   :					List[str] )		->       List[str]:





   '''simple docstring'''







   UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    pt_weights[F'{prefix}.in_proj_weight'].chunk(3     ,		dim=0 )
   UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    pt_weights[F'{prefix}.in_proj_bias'].chunk(3     ,		dim=0 )

   UpperCamelCase__			    =    pt_weights[F'{prefix}.out_proj.weight']
   UpperCamelCase__			    =    pt_weights[F'{prefix}.out_proj.bias']

   UpperCamelCase__			    =    q_proj
   UpperCamelCase__			    =    q_proj_bias

   UpperCamelCase__			    =    k_proj
   UpperCamelCase__			    =    k_proj_bias

   UpperCamelCase__			    =    v_proj
   UpperCamelCase__			    =    v_proj_bias

   UpperCamelCase__			    =    out_proj_weights
   UpperCamelCase__			    =    out_proj_bias



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Union[str, Any]     ,		_UpperCamelCase   :					List[str]     ,		_UpperCamelCase   :					Dict )		->       Tuple:





   '''simple docstring'''







   copy_linear(hf_mlp.fca     ,		_UpperCamelCase     ,		F'{prefix}.c_fc' )
   copy_linear(hf_mlp.fca     ,		_UpperCamelCase     ,		F'{prefix}.c_proj' )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Tuple     ,		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					Optional[int] )		->       Any:





   '''simple docstring'''







   UpperCamelCase__			    =    pt_weights[F'{prefix}.weight'].data
   UpperCamelCase__			    =    pt_weights[F'{prefix}.bias'].data



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					List[Any]     ,		_UpperCamelCase   :					Any )		->       Union[str, Any]:





   '''simple docstring'''







   copy_linear(hf_layer.layer_norma     ,		_UpperCamelCase     ,		F'{prefix}.ln_1' )
   copy_linear(hf_layer.layer_norma     ,		_UpperCamelCase     ,		F'{prefix}.ln_2' )

   # copy MLP
   copy_mlp(hf_layer.mlp     ,		_UpperCamelCase     ,		F'{prefix}.mlp' )

   # copy attn
   copy_attn_layer(hf_layer.self_attn     ,		_UpperCamelCase     ,		F'{prefix}.attn' )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Tuple     ,		_UpperCamelCase   :					Optional[int]     ,		_UpperCamelCase   :					Optional[int] )		->       List[Any]:





   '''simple docstring'''







   for layer_id, hf_layer in enumerate(_UpperCamelCase ):
      copy_layer(_UpperCamelCase     ,		_UpperCamelCase     ,		F'{prefix}.{layer_id}' )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Tuple     ,		_UpperCamelCase   :					Dict )		->       Tuple:





   '''simple docstring'''







   UpperCamelCase__			    =    pt_weights["text_projection"].data.T

   # copy text encoder
   for name, param in hf_model.text_model.named_parameters():
      UpperCamelCase__			    =    pt_weights[F'bert.{name}'].data



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[str]     ,		_UpperCamelCase   :					Dict )		->       List[Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    pt_weights["visual.proj"].data.T

   # copy layer norms
   copy_linear(hf_model.vision_model.pre_layrnorm     ,		_UpperCamelCase     ,		"visual.ln_pre" )
   copy_linear(hf_model.vision_model.post_layernorm     ,		_UpperCamelCase     ,		"visual.ln_post" )

   # copy embeddings
   UpperCamelCase__			    =    pt_weights["visual.conv1.weight"].data
   UpperCamelCase__			    =    pt_weights["visual.class_embedding"].data
   UpperCamelCase__			    =    pt_weights["visual.positional_embedding"].data

   # copy encoder
   copy_layers(hf_model.vision_model.encoder.layers     ,		_UpperCamelCase     ,		"visual.transformer.resblocks" )



@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					Union[str, Any]     ,		_UpperCamelCase   :					Any=None )		->       List[str]:





   '''simple docstring'''







   assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
   UpperCamelCase__			    =    ChineseCLIPConfig.from_pretrained(_UpperCamelCase )

   UpperCamelCase__			    =    ChineseCLIPModel(_UpperCamelCase ).eval()

   UpperCamelCase__			    =    torch.load(_UpperCamelCase     ,		map_location="cpu" )["state_dict"]
   UpperCamelCase__			    =    {(name[7:] if name.startswith("module." ) else name): value for name, value in pt_weights.items()}

   copy_text_model_and_projection(_UpperCamelCase     ,		_UpperCamelCase )
   copy_vision_model_and_projection(_UpperCamelCase     ,		_UpperCamelCase )
   UpperCamelCase__			    =    pt_weights["logit_scale"].data

   hf_model.save_pretrained(_UpperCamelCase )


if __name__ == "__main__":
       __lowercase:		Tuple    						=  argparse.ArgumentParser()
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default=None,
           type=str,
           help="Path to the output folder storing converted hf PyTorch model.",
       )
       parser.add_argument(
           "--checkpoint_path", default=None, type=str, help="Path to original github format ChineseCLIP checkpoint."
       )
       parser.add_argument(
           "--config_path", default=None, required=True, type=str, help="Path to hf config.json of model to convert."
       )
       __lowercase:		Optional[int]    						=  parser.parse_args()

       convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
       print("The conversion is finished!")