
import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor





@require_tokenizers
@require_vision
class 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= tempfile.mkdtemp()

					# fmt: off
					__A   			= ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
					# fmt: on
					__A   			= os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES["vocab_file"] )
					with open(self.vocab_file							,"w"							,encoding="utf-8" ) as vocab_writer:
									vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

					__A   			= {
					    "do_resize": True,
					    "size": {"height": 18, "width": 18},
					    "do_normalize": True,
					    "image_mean": [0.5, 0.5, 0.5],
					    "image_std": [0.5, 0.5, 0.5],
					}
					__A   			= os.path.join(self.tmpdirname							,A )
					with open(self.image_processor_file							,"w"							,encoding="utf-8" ) as fp:
									json.dump(A							,A )






	def 				UpperCamelCase_  (       self					: int							,**A					: List[Any] ):
					return BertTokenizer.from_pretrained(self.tmpdirname							,**A )






	def 				UpperCamelCase_  (       self					: int							,**A					: Optional[Any] ):
					return ViTImageProcessor.from_pretrained(self.tmpdirname							,**A )






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					shutil.rmtree(self.tmpdirname )






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= [np.random.randint(2_55							,size=(3, 30, 4_00)							,dtype=np.uinta )]

					__A   			= [Image.fromarray(np.moveaxis(A							,0							,-1 ) ) for x in image_inputs]

					return image_inputs






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.get_tokenizer()
					__A   			= self.get_image_processor()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					processor.save_pretrained(self.tmpdirname )
					__A   			= VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

					self.assertEqual(processor.tokenizer.get_vocab()							,tokenizer.get_vocab() )
					self.assertIsInstance(processor.tokenizer							,(BertTokenizer, BertTokenizerFast) )

					self.assertEqual(processor.image_processor.to_json_string()							,image_processor.to_json_string() )
					self.assertIsInstance(processor.image_processor							,A )






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= VisionTextDualEncoderProcessor(
					    tokenizer=self.get_tokenizer()							,image_processor=self.get_image_processor() )
					processor.save_pretrained(self.tmpdirname )

					__A   			= self.get_tokenizer(bos_token="(BOS)"							,eos_token="(EOS)" )
					__A   			= self.get_image_processor(do_normalize=A							,padding_value=1.0 )

					__A   			= VisionTextDualEncoderProcessor.from_pretrained(
					    self.tmpdirname							,bos_token="(BOS)"							,eos_token="(EOS)"							,do_normalize=A							,padding_value=1.0 )

					self.assertEqual(processor.tokenizer.get_vocab()							,tokenizer_add_kwargs.get_vocab() )
					self.assertIsInstance(processor.tokenizer							,(BertTokenizer, BertTokenizerFast) )

					self.assertEqual(processor.image_processor.to_json_string()							,image_processor_add_kwargs.to_json_string() )
					self.assertIsInstance(processor.image_processor							,A )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					__A   			= self.prepare_image_inputs()

					__A   			= image_processor(A							,return_tensors="np" )
					__A   			= processor(images=A							,return_tensors="np" )

					for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()							,input_processor[key].sum()							,delta=1E-2 )






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					__A   			= "lower newer"

					__A   			= processor(text=A )

					__A   			= tokenizer(A )

					for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]							,encoded_processor[key] )






	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					__A   			= "lower newer"
					__A   			= self.prepare_image_inputs()

					__A   			= processor(text=A							,images=A )

					self.assertListEqual(list(inputs.keys() )							,["input_ids", "token_type_ids", "attention_mask", "pixel_values"] )

					# test if it raises when no input is passed
					with self.assertRaises(A ):
									processor()






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					__A   			= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__A   			= processor.batch_decode(A )
					__A   			= tokenizer.batch_decode(A )

					self.assertListEqual(A							,A )







	def 				UpperCamelCase_  (       self					: int ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					__A   			= "lower newer"
					__A   			= self.prepare_image_inputs()

					__A   			= processor(text=A							,images=A )

					self.assertListEqual(list(inputs.keys() )							,processor.model_input_names )


import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()						)


def 						UpperCAmelCase			(	a_		,   a_						)		->	Tuple:







				"""simple docstring"""



				__A   			= {}

				for key, value in state_dict.items():
								if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
												continue

								__A   			= key.replace("heads.cmd.mim_head.cls.predictions"		,   "mmm_image_head"						)
								__A   			= key.replace("heads.cmd.mlm_head.cls.predictions"		,   "mmm_text_head"						)
								__A   			= key.replace("heads.cmd.itm_head.cls"		,   "itm_head"						)
								__A   			= key.replace("heads.cmd.itm_head.pooler"		,   "itm_head.pooler"						)
								__A   			= key.replace("heads.cmd.clip_head.logit_scale"		,   "flava.logit_scale"						)
								__A   			= key.replace("heads.fairseq_mlm.cls.predictions"		,   "mlm_head"						)
								__A   			= key.replace("heads.imagenet.mim_head.cls.predictions"		,   "mim_head"						)
								__A   			= key.replace("mm_text_projection"		,   "flava.text_to_mm_projection"						)
								__A   			= key.replace("mm_image_projection"		,   "flava.image_to_mm_projection"						)
								__A   			= key.replace("image_encoder.module"		,   "flava.image_model"						)
								__A   			= key.replace("text_encoder.module"		,   "flava.text_model"						)
								__A   			= key.replace("mm_encoder.module.encoder.cls_token"		,   "flava.multimodal_model.cls_token"						)
								__A   			= key.replace("mm_encoder.module"		,   "flava.multimodal_model"						)
								__A   			= key.replace("text_projection"		,   "flava.text_projection"						)
								__A   			= key.replace("image_projection"		,   "flava.image_projection"						)

								__A   			= value.float()

				for key, value in codebook_state_dict.items():
								__A   			= value

				return upgrade


@torch.no_grad()
def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_=None						)		->	Tuple:







				"""simple docstring"""



				if config_path is not None:
								__A   			= FlavaConfig.from_pretrained(a_						)
				else:
								__A   			= FlavaConfig()

				__A   			= FlavaForPreTraining(a_						).eval()

				__A   			= convert_dalle_checkpoint(a_		,   a_		,   save_checkpoint=a_						)

				if os.path.exists(a_						):
								__A   			= torch.load(a_		,   map_location="cpu"						)
				else:
								__A   			= torch.hub.load_state_dict_from_url(a_		,   map_location="cpu"						)

				__A   			= upgrade_state_dict(a_		,   a_						)
				hf_model.load_state_dict(a_						)
				__A   			= hf_model.state_dict()
				__A   			= count_parameters(a_						)
				__A   			= count_parameters(a_						) + count_parameters(a_						)

				assert torch.allclose(a_		,   a_		,   atol=1E-3						)

				hf_model.save_pretrained(a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Any							 =				argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
					parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
