




import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	str:
		SCREAMING_SNAKE_CASE_					      =							s.rsplit(lowerCAmelCase    ,						lowerCAmelCase )
		return new.join(lowerCAmelCase )


def 					_a							(					lowerCAmelCase )->	Optional[int]:
		# 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							(					lowerCAmelCase )->	Optional[int]:
		SCREAMING_SNAKE_CASE_					      =							{}

		SCREAMING_SNAKE_CASE_					      =							['group_1', 'group_2', 'group_3', 'group_4']
		for key, value in state_dict.items():
				for group_key in group_keys:
						if group_key in key:
								SCREAMING_SNAKE_CASE_					      =							key.replace(F'''{group_key}.'''    ,						F'''{group_key}.group.''' )

				if "res_path" in key:
						SCREAMING_SNAKE_CASE_					      =							key.replace('res_path.'    ,						'res_path.path.' )

				if key.endswith('.w' ):
						SCREAMING_SNAKE_CASE_					      =							rreplace(lowerCAmelCase    ,						'.w'    ,						'.weight'    ,						1 )
				if key.endswith('.b' ):
						SCREAMING_SNAKE_CASE_					      =							rreplace(lowerCAmelCase    ,						'.b'    ,						'.bias'    ,						1 )

				SCREAMING_SNAKE_CASE_					      =							value.float()

		return upgrade


@torch.no_grad()
def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None    ,						lowerCAmelCase=True )->	List[Any]:
		from dall_e import Encoder

		SCREAMING_SNAKE_CASE_					      =							Encoder()
		if os.path.exists(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							torch.load(lowerCAmelCase )
		else:
				SCREAMING_SNAKE_CASE_					      =							torch.hub.load_state_dict_from_url(lowerCAmelCase )

		if isinstance(lowerCAmelCase    ,						lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							ckpt.state_dict()
		encoder.load_state_dict(lowerCAmelCase )

		if config_path is not None:
				SCREAMING_SNAKE_CASE_					      =							FlavaImageCodebookConfig.from_pretrained(lowerCAmelCase )
		else:
				SCREAMING_SNAKE_CASE_					      =							FlavaImageCodebookConfig()

		SCREAMING_SNAKE_CASE_					      =							FlavaImageCodebook(lowerCAmelCase ).eval()
		SCREAMING_SNAKE_CASE_					      =							encoder.state_dict()

		SCREAMING_SNAKE_CASE_					      =							upgrade_state_dict(lowerCAmelCase )
		hf_model.load_state_dict(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							hf_model.state_dict()
		SCREAMING_SNAKE_CASE_					      =							count_parameters(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							count_parameters(lowerCAmelCase )

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

		if save_checkpoint:
				hf_model.save_pretrained(lowerCAmelCase )
		else:
				return hf_state_dict


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Union[str, 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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
						SCREAMING_SNAKE_CASE:     Dict									= parser.parse_args()

						convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





from typing import Mapping

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


SCREAMING_SNAKE_CASE:     List[Any]									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Optional[Any]									= {
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="umt5"
						lowerCAmelCase__			      =["past_key_values"]



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   Union[str, Any]=25_01_12					,							snake_case__	:   Optional[int]=5_12					,							snake_case__	:   Optional[Any]=64					,							snake_case__	:   str=10_24					,							snake_case__	:   Dict=8					,							snake_case__	:   Union[str, Any]=None					,							snake_case__	:   List[Any]=6					,							snake_case__	:   List[Any]=32					,							snake_case__	:   List[Any]=1_28					,							snake_case__	:   Optional[int]=0.1					,							snake_case__	:   List[str]=1e-6					,							snake_case__	:   Optional[Any]=1.0					,							snake_case__	:   Optional[int]="gated-gelu"					,							snake_case__	:   Any=True					,							snake_case__	:   List[Any]=True					,							snake_case__	:   List[str]="T5Tokenizer"					,							snake_case__	:   List[str]=True					,							snake_case__	:   Tuple=0					,							snake_case__	:   Optional[int]=1					,							snake_case__	:   List[str]=0					,							**snake_case__	:   Any					,							):

								"""simple docstring"""

								super().__init__(
								    is_encoder_decoder=snake_case__					,							tokenizer_class=snake_case__					,							tie_word_embeddings=snake_case__					,							pad_token_id=snake_case__					,							eos_token_id=snake_case__					,							decoder_start_token_id=snake_case__					,							**snake_case__					,							)
								SCREAMING_SNAKE_CASE_					      =							vocab_size
								SCREAMING_SNAKE_CASE_					      =							d_model
								SCREAMING_SNAKE_CASE_					      =							d_kv
								SCREAMING_SNAKE_CASE_					      =							d_ff
								SCREAMING_SNAKE_CASE_					      =							num_layers
								SCREAMING_SNAKE_CASE_					      =							(
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								SCREAMING_SNAKE_CASE_					      =							num_heads
								SCREAMING_SNAKE_CASE_					      =							relative_attention_num_buckets
								SCREAMING_SNAKE_CASE_					      =							relative_attention_max_distance
								SCREAMING_SNAKE_CASE_					      =							dropout_rate
								SCREAMING_SNAKE_CASE_					      =							layer_norm_epsilon
								SCREAMING_SNAKE_CASE_					      =							initializer_factor
								SCREAMING_SNAKE_CASE_					      =							feed_forward_proj
								SCREAMING_SNAKE_CASE_					      =							use_cache

								SCREAMING_SNAKE_CASE_					      =							self.feed_forward_proj.split('-'  )
								SCREAMING_SNAKE_CASE_					      =							act_info[-1]
								SCREAMING_SNAKE_CASE_					      =							act_info[0] == 'gated'

								if len(snake_case__  ) > 1 and act_info[0] != "gated" or len(snake_case__  ) > 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\''  )

								if feed_forward_proj == "gated-gelu":
										SCREAMING_SNAKE_CASE_					      =							'gelu_new'



						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return self.d_model



						@property
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return self.num_heads






						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return self.num_layers



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def 	__a					(			self	:   int  ):

								"""simple docstring"""

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

								if self.use_past:
										self.fill_with_past_key_values_(snake_case__					,							direction='inputs'  )

								return common_inputs



						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return 13






						@property
						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								return 5e-4