'''simple docstring'''

import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_     		= logging.get_logger(__name__)



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=MobileNetVaConfig(layer_norm_eps=0.001      )

						if "_quant" in model_name:
												raise ValueError('Quantized models are not supported.'      )

						_lowerCAmelCase										=re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'			,      a__      )
						if matches:
												_lowerCAmelCase										=float(matches[1]      )
												_lowerCAmelCase										=int(matches[2]      )

						# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
						# the usual 1000. The first class (index 0) is "background".
						_lowerCAmelCase										=1_0_0_1
						_lowerCAmelCase										='imagenet-1k-id2label.json'
						_lowerCAmelCase										='huggingface/label-files'
						_lowerCAmelCase										=json.load(open(hf_hub_download(a__			,      a__			,      repo_type='dataset'      )			,      'r'      )      )
						_lowerCAmelCase										={int(a__      ) + 1: v for k, v in idalabel.items()}
						_lowerCAmelCase										='background'
						_lowerCAmelCase										=idalabel
						_lowerCAmelCase										={v: k for k, v in idalabel.items()}

						return config



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										='http://images.cocodataset.org/val2017/000000039769.jpg'
						_lowerCAmelCase										=Image.open(requests.get(a__			,      stream=a__      ).raw      )
						return im



@torch.no_grad()
def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__=False      ):


						'''simple docstring'''






						_lowerCAmelCase										=get_mobilenet_va_config(a__      )

						# Load 🤗 model
						_lowerCAmelCase										=MobileNetVaForImageClassification(a__      ).eval()

						# Load weights from TensorFlow checkpoint
						load_tf_weights_in_mobilenet_va(a__			,      a__			,      a__      )

						# Check outputs on an image, prepared by MobileNetV1ImageProcessor
						_lowerCAmelCase										=MobileNetVaImageProcessor(
						    crop_size={'width': config.image_size, 'height': config.image_size}			,      size={'shortest_edge': config.image_size + 3_2}			,      )
						_lowerCAmelCase										=image_processor(images=prepare_img()			,      return_tensors='pt'      )
						_lowerCAmelCase										=model(**a__      )
						_lowerCAmelCase										=outputs.logits

						assert logits.shape == (1, 1_0_0_1)

						if model_name == "mobilenet_v1_1.0_224":
												_lowerCAmelCase										=torch.tensor([-4.1_739, -1.1_233, 3.1_205]      )
						elif model_name == "mobilenet_v1_0.75_192":
												_lowerCAmelCase										=torch.tensor([-3.9_440, -2.3_141, -0.3_333]      )
						else:
												_lowerCAmelCase										=None

						if expected_logits is not None:
												assert torch.allclose(logits[0, :3]			,      a__			,      atol=1E-4      )

						Path(a__      ).mkdir(exist_ok=a__      )
						print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''      )
						model.save_pretrained(a__      )
						print(F'''Saving image processor to {pytorch_dump_folder_path}'''      )
						image_processor.save_pretrained(a__      )

						if push_to_hub:
												print('Pushing to the hub...'      )
												_lowerCAmelCase										='google/' + model_name
												image_processor.push_to_hub(a__      )
												model.push_to_hub(a__      )


if __name__ == "__main__":
							lowercase_     		= argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--model_name''',
							    default='''mobilenet_v1_1.0_224''',
							    type=str,
							    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
							)
							parser.add_argument(
							    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
							)
							parser.add_argument(
							    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
							)

							lowercase_     		= parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)






'''simple docstring'''

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {
    '''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Tuple       =							'blip_2_vision_model'






		def __init__(   self		,						__A=1408		,						__A=6144		,						__A=39		,						__A=16		,						__A=224		,						__A=14		,						__A="gelu"		,						__A=0.00_001		,						__A=0.0		,						__A=1E-10		,						__A=True		,						**__A		,						) ->     int:
								super().__init__(**__A				)

								_lowerCAmelCase										=hidden_size
								_lowerCAmelCase										=intermediate_size
								_lowerCAmelCase										=num_hidden_layers
								_lowerCAmelCase										=num_attention_heads
								_lowerCAmelCase										=patch_size
								_lowerCAmelCase										=image_size
								_lowerCAmelCase										=initializer_range
								_lowerCAmelCase										=attention_dropout
								_lowerCAmelCase										=layer_norm_eps
								_lowerCAmelCase										=hidden_act
								_lowerCAmelCase										=qkv_bias






		@classmethod
		def 		UpperCamelCase__       (   cls		,						__A		,						**__A				) ->     "PretrainedConfig":
								cls._set_token_in_kwargs(__A				)

								_lowerCAmelCase						,				_lowerCAmelCase										=cls.get_config_dict(__A		,						**__A				)

								# get the vision config dict if we are loading from Blip2Config
								if config_dict.get('model_type'				) == "blip-2":
														_lowerCAmelCase										=config_dict['vision_config']

								if "model_type" in config_dict and hasattr(cls		,						'model_type'				) and config_dict["model_type"] != cls.model_type:
														logger.warning(
														    F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
														    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''				)

								return cls.from_dict(__A		,						**__A				)

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							'blip_2_qformer'






		def __init__(   self		,						__A=3_0522		,						__A=768		,						__A=12		,						__A=12		,						__A=3072		,						__A="gelu"		,						__A=0.1		,						__A=0.1		,						__A=512		,						__A=0.02		,						__A=1E-12		,						__A=0		,						__A="absolute"		,						__A=2		,						__A=1408		,						**__A		,						) ->     List[str]:
								super().__init__(pad_token_id=__A		,						**__A				)

								_lowerCAmelCase										=vocab_size
								_lowerCAmelCase										=hidden_size
								_lowerCAmelCase										=num_hidden_layers
								_lowerCAmelCase										=num_attention_heads
								_lowerCAmelCase										=hidden_act
								_lowerCAmelCase										=intermediate_size
								_lowerCAmelCase										=hidden_dropout_prob
								_lowerCAmelCase										=attention_probs_dropout_prob
								_lowerCAmelCase										=max_position_embeddings
								_lowerCAmelCase										=initializer_range
								_lowerCAmelCase										=layer_norm_eps
								_lowerCAmelCase										=position_embedding_type
								_lowerCAmelCase										=cross_attention_frequency
								_lowerCAmelCase										=encoder_hidden_size






		@classmethod
		def 		UpperCamelCase__       (   cls		,						__A		,						**__A				) ->     "PretrainedConfig":
								cls._set_token_in_kwargs(__A				)

								_lowerCAmelCase						,				_lowerCAmelCase										=cls.get_config_dict(__A		,						**__A				)

								# get the qformer config dict if we are loading from Blip2Config
								if config_dict.get('model_type'				) == "blip-2":
														_lowerCAmelCase										=config_dict['qformer_config']

								if "model_type" in config_dict and hasattr(cls		,						'model_type'				) and config_dict["model_type"] != cls.model_type:
														logger.warning(
														    F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
														    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''				)

								return cls.from_dict(__A		,						**__A				)

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Optional[int]       =							'blip-2'
		lowercase   :      Any       =							True






		def __init__(   self		,						__A=None		,						__A=None		,						__A=None		,						__A=32		,						**__A				) ->     int:
								super().__init__(**__A				)

								if vision_config is None:
														_lowerCAmelCase										={}
														logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.'				)

								if qformer_config is None:
														_lowerCAmelCase										={}
														logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.'				)

								if text_config is None:
														_lowerCAmelCase										={}
														logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'				)

								_lowerCAmelCase										=BlipaVisionConfig(**__A				)
								_lowerCAmelCase										=BlipaQFormerConfig(**__A				)
								_lowerCAmelCase										=text_config['model_type'] if 'model_type' in text_config else 'opt'
								_lowerCAmelCase										=CONFIG_MAPPING[text_model_type](**__A				)

								_lowerCAmelCase										=self.text_config.tie_word_embeddings
								_lowerCAmelCase										=self.text_config.is_encoder_decoder

								_lowerCAmelCase										=num_query_tokens
								_lowerCAmelCase										=self.vision_config.hidden_size
								_lowerCAmelCase										=self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
								_lowerCAmelCase										=1.0
								_lowerCAmelCase										=0.02






		@classmethod
		def 		UpperCamelCase__       (   cls		,						__A		,						__A		,						__A		,						**__A		,						) ->     Any:
								return cls(
								    vision_config=vision_config.to_dict()		,						qformer_config=qformer_config.to_dict()		,						text_config=text_config.to_dict()		,						**__A		,						)






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=copy.deepcopy(self.__dict__				)
								_lowerCAmelCase										=self.vision_config.to_dict()
								_lowerCAmelCase										=self.qformer_config.to_dict()
								_lowerCAmelCase										=self.text_config.to_dict()
								_lowerCAmelCase										=self.__class__.model_type
								return output





