




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase__         =      logging.get_logger(__name__)

UpperCamelCase__         =      {
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}
class       a__   ( UpperCamelCase__				):
						_a							:   Dict =   """t5"""
						_a							:   List[str] =   ["""past_key_values"""]
						_a							:   Union[str, Any] =   {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






						def __init__(       self		,      _A=3_2_1_2_8		,      _A=5_1_2		,      _A=6_4		,      _A=2_0_4_8		,      _A=6		,      _A=None		,      _A=8		,      _A=3_2		,      _A=1_2_8		,      _A=0.1		,      _A=1E-6		,      _A=1.0		,      _A="relu"		,      _A=True		,      _A=True		,      _A=0		,      _A=1		,      **_A		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	vocab_size
											__lowerCAmelCase           =	d_model
											__lowerCAmelCase           =	d_kv
											__lowerCAmelCase           =	d_ff
											__lowerCAmelCase           =	num_layers
											__lowerCAmelCase           =	(
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											__lowerCAmelCase           =	num_heads
											__lowerCAmelCase           =	relative_attention_num_buckets
											__lowerCAmelCase           =	relative_attention_max_distance
											__lowerCAmelCase           =	dropout_rate
											__lowerCAmelCase           =	layer_norm_epsilon
											__lowerCAmelCase           =	initializer_factor
											__lowerCAmelCase           =	feed_forward_proj
											__lowerCAmelCase           =	use_cache

											__lowerCAmelCase           =	self.feed_forward_proj.split("-"      )
											__lowerCAmelCase           =	act_info[-1]
											__lowerCAmelCase           =	act_info[0] == "gated"

											if len(__a      ) > 1 and act_info[0] != "gated" or len(__a      ) > 2:
																raise ValueError(
																    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
																    "'gated-gelu' or 'relu'"      )

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																__lowerCAmelCase           =	"gelu_new"

											super().__init__(
											    pad_token_id=__a		,      eos_token_id=__a		,      is_encoder_decoder=__a		,      **__a		,      )






class       a__   ( UpperCamelCase__				):






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	{
											    "input_ids": {0: "batch", 1: "encoder_sequence"},
											    "attention_mask": {0: "batch", 1: "encoder_sequence"},
											}
											if self.use_past:
																__lowerCAmelCase           =	"past_encoder_sequence + sequence"
																__lowerCAmelCase           =	{0: "batch"}
																__lowerCAmelCase           =	{0: "batch", 1: "past_decoder_sequence + sequence"}
											else:
																__lowerCAmelCase           =	{0: "batch", 1: "decoder_sequence"}
																__lowerCAmelCase           =	{0: "batch", 1: "decoder_sequence"}

											if self.use_past:
																self.fill_with_past_key_values_(__a		,      direction="inputs"      )

											return common_inputs






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return 1_3










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      _a				(						SCREAMING_SNAKE_CASE_							:    Any					):
					# encoder.embeddings are double copied in original FLAVA
					return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict				,				SCREAMING_SNAKE_CASE_							:    Dict					):
					__lowerCAmelCase           =	{}

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

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

										__lowerCAmelCase           =	value.float()

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

					return upgrade







@torch.no_grad()
def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    int=None					):
					if config_path is not None:
										__lowerCAmelCase           =	FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
					else:
										__lowerCAmelCase           =	FlavaConfig()

					__lowerCAmelCase           =	FlavaForPreTraining(SCREAMING_SNAKE_CASE_					).eval()

					__lowerCAmelCase           =	convert_dalle_checkpoint(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				save_checkpoint=SCREAMING_SNAKE_CASE_					)

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

					__lowerCAmelCase           =	upgrade_state_dict(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					hf_model.load_state_dict(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	hf_model.state_dict()
					__lowerCAmelCase           =	count_parameters(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	count_parameters(SCREAMING_SNAKE_CASE_					) + count_parameters(SCREAMING_SNAKE_CASE_					)

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

					hf_model.save_pretrained(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
					UpperCamelCase__         =      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""")
					UpperCamelCase__         =      parser.parse_args()

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




