
'''simple docstring'''






from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
 import tensorflow as tf

 from transformers import TFBlipTextModel
 from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class 			UpperCAmelCase__    :



    def __init__(						self   :							Any,__A   :							Optional[int],__A   :							Union[str, Any]=1_2,__A   :							List[Any]=7,__A   :							Any=True,__A   :							str=True,__A   :							Optional[int]=True,__A   :							Dict=9_9,__A   :							List[Any]=3_2,__A   :							str=3_2,__A   :							Tuple=2,__A   :							str=4,__A   :							Dict=3_7,__A   :							str=0.1,__A   :							Optional[int]=0.1,__A   :							Union[str, Any]=5_1_2,__A   :							Any=0.02,__A   :							str=0,__A   :							int=None,):
          _lowerCamelCase					: Dict		    = parent
          _lowerCamelCase					: Optional[Any]		    = batch_size
          _lowerCamelCase					: Optional[Any]		    = seq_length
          _lowerCamelCase					: Dict		    = is_training
          _lowerCamelCase					: str		    = use_input_mask
          _lowerCamelCase					: Optional[Any]		    = use_labels
          _lowerCamelCase					: int		    = vocab_size
          _lowerCamelCase					: Any		    = hidden_size
          _lowerCamelCase					: Tuple		    = projection_dim
          _lowerCamelCase					: Union[str, Any]		    = num_hidden_layers
          _lowerCamelCase					: List[Any]		    = num_attention_heads
          _lowerCamelCase					: int		    = intermediate_size
          _lowerCamelCase					: Optional[Any]		    = dropout
          _lowerCamelCase					: str		    = attention_dropout
          _lowerCamelCase					: int		    = max_position_embeddings
          _lowerCamelCase					: Optional[int]		    = initializer_range
          _lowerCamelCase					: Optional[int]		    = scope
          _lowerCamelCase					: int		    = bos_token_id



    def 	lowerCamelCase_	(						self   :							str  ):
          _lowerCamelCase					: str		    = ids_tensor([self.batch_size, self.seq_length],self.vocab_size  )

          _lowerCamelCase					: Dict		    = None
          if self.use_input_mask:
                _lowerCamelCase					: Optional[Any]		    = random_attention_mask([self.batch_size, self.seq_length]  )

          if input_mask is not None:
                _lowerCamelCase					: int		    = input_mask.numpy()
                _lowerCamelCase					: Union[str, Any]		    = input_mask.shape
                _lowerCamelCase					: Tuple		    = np.random.randint(1,seq_length - 1,size=(batch_size,)  )
                for batch_idx, start_index in enumerate(a_  ):
                      _lowerCamelCase					: List[Any]		    = 1
                      _lowerCamelCase					: Dict		    = 0

          _lowerCamelCase					: int		    = self.get_config()

          return config, input_ids, tf.convert_to_tensor(a_  )



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          return BlipTextConfig(
              vocab_size=self.vocab_size,hidden_size=self.hidden_size,projection_dim=self.projection_dim,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,dropout=self.dropout,attention_dropout=self.attention_dropout,max_position_embeddings=self.max_position_embeddings,initializer_range=self.initializer_range,bos_token_id=self.bos_token_id,)



    def 	lowerCamelCase_	(						self   :							Dict,__A   :							Union[str, Any],__A   :							Optional[int],__A   :							Optional[int]  ):
          _lowerCamelCase					: str		    = TFBlipTextModel(config=a_  )
          _lowerCamelCase					: Dict		    = model(a_,attention_mask=a_,training=a_  )
          _lowerCamelCase					: List[Any]		    = model(a_,training=a_  )
          self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)  )
          self.parent.assertEqual(result.pooler_output.shape,(self.batch_size, self.hidden_size)  )



    def 	lowerCamelCase_	(						self   :							Any  ):
          _lowerCamelCase					: List[Any]		    = self.prepare_config_and_inputs()
          _lowerCamelCase					: List[Any]		    = config_and_inputs
          _lowerCamelCase					: Dict		    = {"input_ids": input_ids, "attention_mask": input_mask}
          return config, inputs_dict


@require_tf
class 			UpperCAmelCase__    (							A    ,						unittest.TestCase			):
    lowerCAmelCase_					     =		(TFBlipTextModel,) if is_tf_available() else ()
    lowerCAmelCase_					     =		False
    lowerCAmelCase_					     =		False
    lowerCAmelCase_					     =		False



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: Optional[int]		    = BlipTextModelTester(self  )
          _lowerCamelCase					: List[str]		    = ConfigTester(self,config_class=a_,hidden_size=3_7  )



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          self.config_tester.run_common_tests()



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: List[str]		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*a_  )



    def 	lowerCamelCase_	(						self   :							Optional[int]  ):
          pass



    def 	lowerCamelCase_	(						self   :							Any  ):
          pass



    @unittest.skip(reason="Blip does not use inputs_embeds"  )
    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          pass



    @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"  )
    def 	lowerCamelCase_	(						self   :							List[str]  ):
          pass



    @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"  )
    def 	lowerCamelCase_	(						self   :							Tuple  ):
          pass



    @slow
    def 	lowerCamelCase_	(						self   :							int  ):
          for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _lowerCamelCase					: Any		    = TFBlipTextModel.from_pretrained(a_  )
                self.assertIsNotNone(a_  )



    def 	lowerCamelCase_	(						self   :							int,__A   :							Tuple=True  ):
          super().test_pt_tf_model_equivalence(allow_missing_keys=a_  )



'''simple docstring'''
import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling




def 	_A  (       A							,A							)  ->				str:
	lowercase						:		Optional[int]   					=		old_name

	if "patch_embed" in old_name:
		lowercase					,		lowercase					,		lowercase						:		Tuple   					=		old_name.split("."							)

		if layer == "0":
			lowercase						:		int   					=		old_name.replace("0"							,"convolution1"							)
		elif layer == "1":
			lowercase						:		List[str]   					=		old_name.replace("1"							,"batchnorm_before"							)
		elif layer == "3":
			lowercase						:		Dict   					=		old_name.replace("3"							,"convolution2"							)
		else:
			lowercase						:		Union[str, Any]   					=		old_name.replace("4"							,"batchnorm_after"							)

	if "network" in old_name and re.search(r"\d\.\d"							,A							):
		lowercase						:		List[str]   					=		r"\b\d{2}\b"
		if bool(re.search(A							,A							)							):
			lowercase						:		str   					=		re.search(r"\d\.\d\d."							,A							).group()
		else:
			lowercase						:		int   					=		re.search(r"\d\.\d."							,A							).group()
		if int(match[0]							) < 6:
			lowercase						:		str   					=		old_name.replace(A							,""							)
			lowercase						:		List[str]   					=		trimmed_name.replace("network"							,match[0] + ".meta4D_layers.blocks." + match[2:-1]							)
			lowercase						:		Optional[Any]   					=		"intermediate_stages." + trimmed_name
		else:
			lowercase						:		str   					=		old_name.replace(A							,""							)
			if int(match[2]							) < num_meta4D_last_stage:
				lowercase						:		Optional[int]   					=		trimmed_name.replace("network"							,"meta4D_layers.blocks." + match[2]							)
			else:
				lowercase						:		List[Any]   					=		str(int(match[2]							) - num_meta4D_last_stage							)
				lowercase						:		List[Any]   					=		trimmed_name.replace("network"							,"meta3D_layers.blocks." + layer_index							)
				if "norm1" in old_name:
					lowercase						:		str   					=		trimmed_name.replace("norm1"							,"layernorm1"							)
				elif "norm2" in old_name:
					lowercase						:		Optional[Any]   					=		trimmed_name.replace("norm2"							,"layernorm2"							)
				elif "fc1" in old_name:
					lowercase						:		Optional[int]   					=		trimmed_name.replace("fc1"							,"linear_in"							)
				elif "fc2" in old_name:
					lowercase						:		str   					=		trimmed_name.replace("fc2"							,"linear_out"							)

			lowercase						:		Dict   					=		"last_stage." + trimmed_name

	elif "network" in old_name and re.search(r".\d."							,A							):
		lowercase						:		Union[str, Any]   					=		old_name.replace("network"							,"intermediate_stages"							)

	if "fc" in new_name:
		lowercase						:		Any   					=		new_name.replace("fc"							,"convolution"							)
	elif ("norm1" in new_name) and ("layernorm1" not in new_name):
		lowercase						:		Optional[Any]   					=		new_name.replace("norm1"							,"batchnorm_before"							)
	elif ("norm2" in new_name) and ("layernorm2" not in new_name):
		lowercase						:		List[str]   					=		new_name.replace("norm2"							,"batchnorm_after"							)
	if "proj" in new_name:
		lowercase						:		Optional[int]   					=		new_name.replace("proj"							,"projection"							)
	if "dist_head" in new_name:
		lowercase						:		Tuple   					=		new_name.replace("dist_head"							,"distillation_classifier"							)
	elif "head" in new_name:
		lowercase						:		Tuple   					=		new_name.replace("head"							,"classifier"							)
	elif "patch_embed" in new_name:
		lowercase						:		Optional[int]   					=		"efficientformer." + new_name
	elif new_name == "norm.weight" or new_name == "norm.bias":
		lowercase						:		str   					=		new_name.replace("norm"							,"layernorm"							)
		lowercase						:		List[Any]   					=		"efficientformer." + new_name
	else:
		lowercase						:		Optional[Any]   					=		"efficientformer.encoder." + new_name

	return new_name




def 	_A  (       A							,A							)  ->				Optional[Any]:
	for key in checkpoint.copy().keys():
		lowercase						:		List[str]   					=		checkpoint.pop(A							)
		lowercase						:		int   					=		val

	return checkpoint




def 	_A  (       )  ->				Optional[int]:
	lowercase						:		str   					=		"http://images.cocodataset.org/val2017/000000039769.jpg"
	lowercase						:		Optional[Any]   					=		Image.open(requests.get(A							,stream=A							).raw							)

	return image




def 	_A  (       A							,A							,A							,A							)  ->				List[Any]:
	lowercase						:		Optional[int]   					=		torch.load(A							,map_location="cpu"							)["model"]
	lowercase						:		int   					=		EfficientFormerConfig.from_json_file(A							)
	lowercase						:		Tuple   					=		EfficientFormerForImageClassificationWithTeacher(A							)
	lowercase						:		int   					=		"_".join(checkpoint_path.split("/"							)[-1].split("."							)[0].split("_"							)[:-1]							)

	lowercase						:		Optional[int]   					=		config.depths[-1] - config.num_metaad_blocks + 1
	lowercase						:		int   					=		convert_torch_checkpoint(A							,A							)

	model.load_state_dict(A							)
	model.eval()

	lowercase						:		List[Any]   					=		{
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

	# prepare image
	lowercase						:		Tuple   					=		prepare_img()
	lowercase						:		Optional[int]   					=		2_5_6
	lowercase						:		str   					=		2_2_4
	lowercase						:		List[str]   					=		EfficientFormerImageProcessor(
	    size={"shortest_edge": image_size}							,crop_size={"height": crop_size, "width": crop_size}							,resample=pillow_resamplings["bicubic"]							,)
	lowercase						:		Union[str, Any]   					=		processor(images=A							,return_tensors="pt"							).pixel_values

	# original processing pipeline
	lowercase						:		Tuple   					=		Compose(
	    [
	        Resize(A							,interpolation=pillow_resamplings["bicubic"]							),
	        CenterCrop(A							),
	        ToTensor(),
	        Normalize(A							,A							),
	    ]							)
	lowercase						:		List[Any]   					=		image_transforms(A							).unsqueeze(0							)

	assert torch.allclose(A							,A							)

	lowercase						:		Union[str, Any]   					=		model(A							)
	lowercase						:		Any   					=		outputs.logits

	lowercase						:		List[str]   					=		(1, 1_0_0_0)

	if "l1" in model_name:
		lowercase						:		Any   					=		torch.Tensor(
		    [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]							)
		assert torch.allclose(logits[0, :1_0]							,A							,atol=1e-3							)
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		lowercase						:		List[Any]   					=		torch.Tensor(
		    [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]							)
		assert torch.allclose(logits[0, :1_0]							,A							,atol=1e-3							)
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		lowercase						:		Optional[int]   					=		torch.Tensor(
		    [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]							)
		assert logits.shape == expected_shape
	else:
		raise ValueError(
		    F'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''							)

	# Save Checkpoints
	Path(A							).mkdir(exist_ok=A							)
	model.save_pretrained(A							)
	print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''							)
	processor.save_pretrained(A							)
	print(F'''Processor successfuly saved at {pytorch_dump_path}'''							)

	if push_to_hub:
		print("Pushing model to the hub..."							)

		model.push_to_hub(
		    repo_id=F'''Bearnardd/{pytorch_dump_path}'''							,commit_message="Add model"							,use_temp_dir=A							,)
		processor.push_to_hub(
		    repo_id=F'''Bearnardd/{pytorch_dump_path}'''							,commit_message="Add image processor"							,use_temp_dir=A							,)


if __name__ == "__main__":
					lowerCAmelCase   : Tuple             =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--pytorch_model_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to EfficientFormer pytorch checkpoint.""",
					)
					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The json file for EfficientFormer model config.""",
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)

					parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
					parser.add_argument(
					    """--no-push_to_hub""",
					    dest="""push_to_hub""",
					    action="""store_false""",
					    help="""Do not push model and image processor to the hub""",
					)
					parser.set_defaults(push_to_hub=True)

					lowerCAmelCase   : Optional[int]             =				parser.parse_args()
					convert_efficientformer_checkpoint(
					    checkpoint_path=args.pytorch_model_path,
					    efficientformer_config_file=args.config_file,
					    pytorch_dump_path=args.pytorch_dump_path,
					    push_to_hub=args.push_to_hub,
					)


