
"""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 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      Dict  ,A_     :      int  ,A_     :      int=12  ,A_     :      Union[str, Any]=7  ,A_     :      List[Any]=True  ,A_     :      str=True  ,A_     :      Optional[Any]=True  ,A_     :      str=99  ,A_     :      Any=32  ,A_     :      Tuple=32  ,A_     :      List[str]=2  ,A_     :      Dict=4  ,A_     :      int=37  ,A_     :      Optional[int]=0.1  ,A_     :      Any=0.1  ,A_     :      int=512  ,A_     :      str=0.02  ,A_     :      int=0  ,A_     :      str=None  ,)     ->   List[str]:
							A						   =   parent
							A						   =   batch_size
							A						   =   seq_length
							A						   =   is_training
							A						   =   use_input_mask
							A						   =   use_labels
							A						   =   vocab_size
							A						   =   hidden_size
							A						   =   projection_dim
							A						   =   num_hidden_layers
							A						   =   num_attention_heads
							A						   =   intermediate_size
							A						   =   dropout
							A						   =   attention_dropout
							A						   =   max_position_embeddings
							A						   =   initializer_range
							A						   =   scope
							A						   =   bos_token_id


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   str:
							A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size      )

							A						   =   None
							if self.use_input_mask:
								A						   =   random_attention_mask([self.batch_size, self.seq_length]      )

							if input_mask is not None:
								A						   =   input_mask.numpy()
								A   ,      A						   =   input_mask.shape
								A						   =   np.random.randint(1  ,seq_length - 1  ,size=(batch_size,)      )
								for batch_idx, start_index in enumerate(A_      ):
									A						   =   1
									A						   =   0

							A						   =   self.get_config()

							return config, input_ids, tf.convert_to_tensor(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[Any]:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      int  ,A_     :      Optional[int]  ,A_     :      Optional[int]      )     ->   Dict:
							A						   =   TFBlipTextModel(config=A_      )
							A						   =   model(A_  ,attention_mask=A_  ,training=A_      )
							A						   =   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 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Optional[Any]:
							A						   =   self.prepare_config_and_inputs()
							A   ,      A   ,      A						   =   config_and_inputs
							A						   =   {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict







@require_tf
class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Tuple      =    (TFBlipTextModel,) if is_tf_available() else ()
						_lowerCamelCase:    Any      =    False
						_lowerCamelCase:    Any      =    False
						_lowerCamelCase:    List[str]      =    False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Tuple:
							A						   =   BlipTextModelTester(self      )
							A						   =   ConfigTester(self  ,config_class=A_  ,hidden_size=37      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   str:
							self.config_tester.run_common_tests()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   int:
							pass


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							pass


						@unittest.skip(reason='Blip does not use inputs_embeds'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Any:
							pass


						@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Union[str, Any]:
							pass


						@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   int:
							pass


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   str:
							for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A						   =   TFBlipTextModel.from_pretrained(A_      )
								self.assertIsNotNone(A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[Any]=True      )     ->   List[str]:
							super().test_pt_tf_model_equivalence(allow_missing_keys=A_      )

"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification






def       _snake_case				(		snake_case__   :     int     ):
	A						   =   SwinvaConfig()
	A						   =   swinva_name.split('_'     )

	A						   =   name_split[1]
	if "to" in name_split[3]:
		A						   =   int(name_split[3][-3:]     )
	else:
		A						   =   int(name_split[3]     )
	if "to" in name_split[2]:
		A						   =   int(name_split[2][-2:]     )
	else:
		A						   =   int(name_split[2][6:]     )

	if model_size == "tiny":
		A						   =   96
		A						   =   (2, 2, 6, 2)
		A						   =   (3, 6, 12, 24)
	elif model_size == "small":
		A						   =   96
		A						   =   (2, 2, 18, 2)
		A						   =   (3, 6, 12, 24)
	elif model_size == "base":
		A						   =   128
		A						   =   (2, 2, 18, 2)
		A						   =   (4, 8, 16, 32)
	else:
		A						   =   192
		A						   =   (2, 2, 18, 2)
		A						   =   (6, 12, 24, 48)

	if "to" in swinva_name:
		A						   =   (12, 12, 12, 6)

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		A						   =   2_1841
		A						   =   'huggingface/label-files'
		A						   =   'imagenet-22k-id2label.json'
		A						   =   json.load(open(hf_hub_download(snake_case__	,      snake_case__	,      repo_type='dataset'     )	,      'r'     )     )
		A						   =   {int(snake_case__     ): v for k, v in idalabel.items()}
		A						   =   idalabel
		A						   =   {v: k for k, v in idalabel.items()}

	else:
		A						   =   1000
		A						   =   'huggingface/label-files'
		A						   =   'imagenet-1k-id2label.json'
		A						   =   json.load(open(hf_hub_download(snake_case__	,      snake_case__	,      repo_type='dataset'     )	,      'r'     )     )
		A						   =   {int(snake_case__     ): v for k, v in idalabel.items()}
		A						   =   idalabel
		A						   =   {v: k for k, v in idalabel.items()}

	A						   =   img_size
	A						   =   num_classes
	A						   =   embed_dim
	A						   =   depths
	A						   =   num_heads
	A						   =   window_size

	return config






def       _snake_case				(		snake_case__   :     List[Any]     ):
	if "patch_embed.proj" in name:
		A						   =   name.replace('patch_embed.proj'	,      'embeddings.patch_embeddings.projection'     )
	if "patch_embed.norm" in name:
		A						   =   name.replace('patch_embed.norm'	,      'embeddings.norm'     )
	if "layers" in name:
		A						   =   'encoder.' + name
	if "attn.proj" in name:
		A						   =   name.replace('attn.proj'	,      'attention.output.dense'     )
	if "attn" in name:
		A						   =   name.replace('attn'	,      'attention.self'     )
	if "norm1" in name:
		A						   =   name.replace('norm1'	,      'layernorm_before'     )
	if "norm2" in name:
		A						   =   name.replace('norm2'	,      'layernorm_after'     )
	if "mlp.fc1" in name:
		A						   =   name.replace('mlp.fc1'	,      'intermediate.dense'     )
	if "mlp.fc2" in name:
		A						   =   name.replace('mlp.fc2'	,      'output.dense'     )
	if "q_bias" in name:
		A						   =   name.replace('q_bias'	,      'query.bias'     )
	if "k_bias" in name:
		A						   =   name.replace('k_bias'	,      'key.bias'     )
	if "v_bias" in name:
		A						   =   name.replace('v_bias'	,      'value.bias'     )
	if "cpb_mlp" in name:
		A						   =   name.replace('cpb_mlp'	,      'continuous_position_bias_mlp'     )
	if name == "norm.weight":
		A						   =   'layernorm.weight'
	if name == "norm.bias":
		A						   =   'layernorm.bias'

	if "head" in name:
		A						   =   name.replace('head'	,      'classifier'     )
	else:
		A						   =   'swinv2.' + name

	return name






def       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     List[Any]     ):
	for key in orig_state_dict.copy().keys():
		A						   =   orig_state_dict.pop(snake_case__     )

		if "mask" in key:
			continue
		elif "qkv" in key:
			A						   =   key.split('.'     )
			A						   =   int(key_split[1]     )
			A						   =   int(key_split[3]     )
			A						   =   model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				A						   =   val[:dim, :]
				A						   =   val[dim : dim * 2, :]
				A						   =   val[-dim:, :]
			else:
				A						   =   val[:dim]
				A						   =   val[
				    dim : dim * 2
				]
				A						   =   val[-dim:]
		else:
			A						   =   val

	return orig_state_dict






def       _snake_case				(		snake_case__   :     Optional[int]	,      snake_case__   :     Tuple     ):
	A						   =   timm.create_model(snake_case__	,      pretrained=snake_case__     )
	timm_model.eval()

	A						   =   get_swinva_config(snake_case__     )
	A						   =   SwinvaForImageClassification(snake_case__     )
	model.eval()

	A						   =   convert_state_dict(timm_model.state_dict()	,      snake_case__     )
	model.load_state_dict(snake_case__     )

	A						   =   'http://images.cocodataset.org/val2017/000000039769.jpg'

	A						   =   AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_'	,      '-'     )     )     )
	A						   =   Image.open(requests.get(snake_case__	,      stream=snake_case__     ).raw     )
	A						   =   image_processor(images=snake_case__	,      return_tensors='pt'     )

	A						   =   timm_model(inputs['pixel_values']     )
	A						   =   model(**snake_case__     ).logits

	assert torch.allclose(snake_case__	,      snake_case__	,      atol=1e-3     )

	print(F'Saving model {swinva_name} to {pytorch_dump_folder_path}'     )
	model.save_pretrained(snake_case__     )

	print(F'Saving image processor to {pytorch_dump_folder_path}'     )
	image_processor.save_pretrained(snake_case__     )

	model.push_to_hub(
	    repo_path_or_name=Path(snake_case__	,      snake_case__     )	,      organization='nandwalritik'	,      commit_message='Add model'	,      )


if __name__ == "__main__":
		_lowercase							   =				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--swinv2_name''',
		    default='''swinv2_tiny_patch4_window8_256''',
		    type=str,
		    help='''Name of the Swinv2 timm model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)

		_lowercase							   =				parser.parse_args()
		convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)