
'''simple docstring'''







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				str   ,   __SCREAMING_SNAKE_CASE      :				Optional[int]   ,   __SCREAMING_SNAKE_CASE      :				Any							):



						"""simple docstring"""





						lowercase_			:  Optional[int]									= TaConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)
						lowercase_			:  str									= FlaxTaForConditionalGeneration(config=__SCREAMING_SNAKE_CASE							)
						lowercase_			:  Optional[Any]									= checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE							)

						lowercase_			:  Union[str, Any]									= '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

						# Encoder
						for layer_index in range(config.num_layers							):
												lowercase_			:  Dict									= F'''layers_{str(__SCREAMING_SNAKE_CASE							)}'''

												# Self-Attention
												lowercase_			:  Optional[int]									= tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
												lowercase_			:  Dict									= tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
												lowercase_			:  str									= tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
												lowercase_			:  Optional[Any]									= tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

												# Layer Normalization
												lowercase_			:  str									= tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

												if split_mlp_wi:
																		lowercase_			:  Union[str, Any]									= tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		lowercase_			:  Dict									= tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		lowercase_			:  Optional[Any]									= tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												lowercase_			:  int									= tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												lowercase_			:  List[Any]									= tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												lowercase_			:  Dict									= tax_attention_key
												lowercase_			:  List[Any]									= tax_attention_out
												lowercase_			:  Any									= tax_attention_query
												lowercase_			:  Optional[int]									= tax_attention_value

												lowercase_			:  Any									= tax_attention_layer_norm

												if split_mlp_wi:
																		lowercase_			:  Union[str, Any]									= tax_mlp_wi_a
																		lowercase_			:  Dict									= tax_mlp_wi_a
												else:
																		lowercase_			:  Any									= tax_mlp_wi

												lowercase_			:  Any									= tax_mlp_wo
												lowercase_			:  Tuple									= tax_mlp_layer_norm

						# Only for layer 0:
						lowercase_			:  List[Any]									= tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
						lowercase_			:  Optional[int]									= tax_encoder_rel_embedding

						# Assigning
						lowercase_			:  int									= tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
						lowercase_			:  Optional[Any]									= tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_decoder_layers							):
												lowercase_			:  Any									= F'''layers_{str(__SCREAMING_SNAKE_CASE							)}'''

												# Self-Attention
												lowercase_			:  Dict									= tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
												lowercase_			:  str									= tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
												lowercase_			:  List[Any]									= tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
												lowercase_			:  Tuple									= tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

												# Layer Normalization
												lowercase_			:  Optional[Any]									= tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
												    '''scale'''
												]

												# Encoder-Decoder-Attention
												lowercase_			:  str									= tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
												    '''kernel'''
												]
												lowercase_			:  int									= tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
												    '''kernel'''
												]
												lowercase_			:  Union[str, Any]									= tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
												    '''kernel'''
												]
												lowercase_			:  Dict									= tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
												    '''kernel'''
												]

												# Layer Normalization
												lowercase_			:  List[Any]									= tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

												# MLP
												if split_mlp_wi:
																		lowercase_			:  Union[str, Any]									= tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		lowercase_			:  Any									= tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		lowercase_			:  Union[str, Any]									= tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												lowercase_			:  int									= tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												lowercase_			:  Optional[Any]									= tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												lowercase_			:  Any									= tax_attention_key
												lowercase_			:  Optional[int]									= tax_attention_out
												lowercase_			:  Optional[Any]									= tax_attention_query
												lowercase_			:  List[Any]									= tax_attention_value

												lowercase_			:  Tuple									= tax_pre_attention_layer_norm

												lowercase_			:  Dict									= tax_enc_dec_attention_key
												lowercase_			:  Optional[Any]									= tax_enc_dec_attention_out
												lowercase_			:  List[str]									= tax_enc_dec_attention_query
												lowercase_			:  List[str]									= tax_enc_dec_attention_value

												lowercase_			:  Union[str, Any]									= tax_cross_layer_norm

												if split_mlp_wi:
																		lowercase_			:  Optional[int]									= tax_mlp_wi_a
																		lowercase_			:  Tuple									= tax_mlp_wi_a
												else:
																		lowercase_			:  Optional[int]									= tax_mlp_wi

												lowercase_			:  str									= tax_mlp_wo

												lowercase_			:  Optional[int]									= txa_mlp_layer_norm

						# Decoder Normalization
						lowercase_			:  Any									= tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
						lowercase_			:  List[Any]									= txa_decoder_norm

						# Only for layer 0:
						lowercase_			:  Dict									= tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
						lowercase_			:  str									= tax_decoder_rel_embedding

						# Token Embeddings
						lowercase_			:  List[str]									= tax_model['''target''']['''token_embedder''']['''embedding''']
						lowercase_			:  Tuple									= txa_token_embeddings

						# LM Head (only in v1.1 checkpoints)
						if "logits_dense" in tax_model["target"]["decoder"]:
												lowercase_			:  Any									= tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

						flax_model.save_pretrained(__SCREAMING_SNAKE_CASE							)
						print('''T5X Model was sucessfully converted!'''							)


if __name__ == "__main__":
							_lowercase				:						List[Any]												= argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
							)
							parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
							parser.add_argument(
							    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
							)
							_lowercase				:						List[str]												= parser.parse_args()
							convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





'''simple docstring'''







from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
							import torch






class 				lowerCAmelCase__     (						lowerCamelCase_  ):
					lowerCAmelCase_       =  ['''image_processor''', '''tokenizer''']
					lowerCAmelCase_       =  '''OneFormerImageProcessor'''
					lowerCAmelCase_       =  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE = 77       ,							__SCREAMING_SNAKE_CASE = 77       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''	)
											if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''	)

											lowercase_			:  Dict									= max_seq_length
											lowercase_			:  Dict									= task_seq_length

											super().__init__(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=77	):


											"""simple docstring"""





											if text_list is None:
																	raise ValueError('''tokens cannot be None.'''	)

											lowercase_			:  str									= self.tokenizer(__SCREAMING_SNAKE_CASE       ,							padding='''max_length'''       ,							max_length=__SCREAMING_SNAKE_CASE       ,							truncation=__SCREAMING_SNAKE_CASE	)

											lowercase_		,				lowercase_			:  List[str]									= tokens['''attention_mask'''], tokens['''input_ids''']

											lowercase_			:  List[str]									= []
											for attn_mask, input_id in zip(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  Optional[Any]									= torch.tensor(__SCREAMING_SNAKE_CASE	) * torch.tensor(__SCREAMING_SNAKE_CASE	)
																	token_inputs.append(token.unsqueeze(0	)	)

											lowercase_			:  str									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)
											return token_inputs






					def __call__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if task_inputs is None:
																	raise ValueError('''You have to specify the task_input. Found None.'''	)
											elif images is None:
																	raise ValueError('''You have to specify the image. Found None.'''	)

											if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs	):
																	raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''	)

											lowercase_			:  Optional[int]									= self.image_processor(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[str]									= [task_inputs]

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) and all(isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) for task_input in task_inputs	):
																	lowercase_			:  List[str]									= []
																	for task in task_inputs:
																							lowercase_			:  Tuple									= F'''the task is {task}'''
																							task_token_inputs.append(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  Tuple									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.task_seq_length	)
											else:
																	raise TypeError('''Task Inputs should be a string or a list of strings.'''	)

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Any									= encoded_inputs.text_inputs

																	lowercase_			:  int									= []
																	for texts in texts_list:
																							lowercase_			:  Any									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Tuple									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if task_inputs is None:
																	raise ValueError('''You have to specify the task_input. Found None.'''	)
											elif images is None:
																	raise ValueError('''You have to specify the image. Found None.'''	)

											if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs	):
																	raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''	)

											lowercase_			:  Dict									= self.image_processor.encode_inputs(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[Any]									= [task_inputs]

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) and all(isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) for task_input in task_inputs	):
																	lowercase_			:  int									= []
																	for task in task_inputs:
																							lowercase_			:  int									= F'''the task is {task}'''
																							task_token_inputs.append(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  str									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.task_seq_length	)
											else:
																	raise TypeError('''Task Inputs should be a string or a list of strings.'''	)

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Dict									= encoded_inputs.text_inputs

																	lowercase_			:  str									= []
																	for texts in texts_list:
																							lowercase_			:  List[str]									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Union[str, Any]									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_semantic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_instance_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_panoptic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)



