






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       Optional[Any]     ,					lowercase__						:       Any     ,					lowercase__						:       Any="attention"       )		->    str:


				'''simple docstring'''




				lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
				lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
				lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
				lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
				return k, o, q, v





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       List[Any]     ,					lowercase__						:       int     ,					lowercase__						:       Union[str, Any]=False       )		->    str:


				'''simple docstring'''




				if split_mlp_wi:
								lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
								lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
								lowerCAmelCase__      			=						(wi_a, wi_a)
				else:
								lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/mlp/wi/kernel']

				lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/mlp/wo/kernel']
				return wi, wo





def 	lowerCAmelCase_		(lowercase__						:       List[Any]     ,					lowercase__						:       str     ,					lowercase__						:       List[Any]     ,					lowercase__						:       str       )		->    Optional[int]:


				'''simple docstring'''




				return params[f'{prefix}/layers_{i}/{layer_name}/scale']





def 	lowerCAmelCase_		(lowercase__						:       dict     ,					*, lowercase__						:       int     ,					lowercase__						:       bool       )		->    List[str]:


				'''simple docstring'''




				lowerCAmelCase__      			=						traverse_util.flatten_dict(variables['''target''']       )
				lowerCAmelCase__      			=						{'''/'''.join(lowercase__       ): v for k, v in old.items()}

				# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
				lowerCAmelCase__      			=						'''encoder/layers_0/mlp/wi_0/kernel''' in old
				print('''Split MLP:'''     ,					lowercase__       )

				lowerCAmelCase__      			=						collections.OrderedDict()

				# Shared embeddings.
				lowerCAmelCase__      			=						old['''token_embedder/embedding''']

				# Encoder.
				for i in range(lowercase__       ):
								# Block i, layer 0 (Self Attention).
								lowerCAmelCase__      			=						tax_layer_norm_lookup(lowercase__     ,					lowercase__     ,					'''encoder'''     ,					'''pre_attention_layer_norm'''       )
								lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						tax_attention_lookup(lowercase__     ,					lowercase__     ,					'''encoder'''     ,					'''attention'''       )
								lowerCAmelCase__      			=						layer_norm
								lowerCAmelCase__      			=						k.T
								lowerCAmelCase__      			=						o.T
								lowerCAmelCase__      			=						q.T
								lowerCAmelCase__      			=						v.T

								# Block i, layer 1 (MLP).
								lowerCAmelCase__      			=						tax_layer_norm_lookup(lowercase__     ,					lowercase__     ,					'''encoder'''     ,					'''pre_mlp_layer_norm'''       )
								lowerCAmelCase__			,		lowerCAmelCase__      			=						tax_mlp_lookup(lowercase__     ,					lowercase__     ,					'''encoder'''     ,					lowercase__       )
								lowerCAmelCase__      			=						layer_norm
								if split_mlp_wi:
												lowerCAmelCase__      			=						wi[0].T
												lowerCAmelCase__      			=						wi[1].T
								else:
												lowerCAmelCase__      			=						wi.T
								lowerCAmelCase__      			=						wo.T

				lowerCAmelCase__      			=						old[
				    '''encoder/relpos_bias/rel_embedding'''
				].T
				lowerCAmelCase__      			=						old['''encoder/encoder_norm/scale''']

				if not is_encoder_only:
								# Decoder.
								for i in range(lowercase__       ):
												# Block i, layer 0 (Self Attention).
												lowerCAmelCase__      			=						tax_layer_norm_lookup(lowercase__     ,					lowercase__     ,					'''decoder'''     ,					'''pre_self_attention_layer_norm'''       )
												lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						tax_attention_lookup(lowercase__     ,					lowercase__     ,					'''decoder'''     ,					'''self_attention'''       )
												lowerCAmelCase__      			=						layer_norm
												lowerCAmelCase__      			=						k.T
												lowerCAmelCase__      			=						o.T
												lowerCAmelCase__      			=						q.T
												lowerCAmelCase__      			=						v.T

												# Block i, layer 1 (Cross Attention).
												lowerCAmelCase__      			=						tax_layer_norm_lookup(lowercase__     ,					lowercase__     ,					'''decoder'''     ,					'''pre_cross_attention_layer_norm'''       )
												lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						tax_attention_lookup(lowercase__     ,					lowercase__     ,					'''decoder'''     ,					'''encoder_decoder_attention'''       )
												lowerCAmelCase__      			=						layer_norm
												lowerCAmelCase__      			=						k.T
												lowerCAmelCase__      			=						o.T
												lowerCAmelCase__      			=						q.T
												lowerCAmelCase__      			=						v.T

												# Block i, layer 2 (MLP).
												lowerCAmelCase__      			=						tax_layer_norm_lookup(lowercase__     ,					lowercase__     ,					'''decoder'''     ,					'''pre_mlp_layer_norm'''       )
												lowerCAmelCase__			,		lowerCAmelCase__      			=						tax_mlp_lookup(lowercase__     ,					lowercase__     ,					'''decoder'''     ,					lowercase__       )
												lowerCAmelCase__      			=						layer_norm
												if split_mlp_wi:
																lowerCAmelCase__      			=						wi[0].T
																lowerCAmelCase__      			=						wi[1].T
												else:
																lowerCAmelCase__      			=						wi.T
												lowerCAmelCase__      			=						wo.T

								lowerCAmelCase__      			=						old['''decoder/decoder_norm/scale''']
								lowerCAmelCase__      			=						old[
								    '''decoder/relpos_bias/rel_embedding'''
								].T

								# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
								if "decoder/logits_dense/kernel" in old:
												lowerCAmelCase__      			=						old['''decoder/logits_dense/kernel'''].T

				return new





def 	lowerCAmelCase_		(lowercase__						:       int     ,					lowercase__						:       bool       )		->    Union[str, Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						collections.OrderedDict([(k, torch.from_numpy(v.copy()       )) for (k, v) in converted_params.items()]       )

				# Add what is missing.
				if "encoder.embed_tokens.weight" not in state_dict:
								lowerCAmelCase__      			=						state_dict['''shared.weight''']

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												lowerCAmelCase__      			=						state_dict['''shared.weight''']

								if "lm_head.weight" not in state_dict:  # For old 1.0 models.
												print('''Using shared word embeddings as lm_head.'''       )
												lowerCAmelCase__      			=						state_dict['''shared.weight''']

				return state_dict





def 	lowerCAmelCase_		(lowercase__						:       Optional[Any]     ,					lowercase__						:       str     ,					lowercase__						:       Tuple     ,					lowercase__						:       Tuple       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						checkpoints.load_tax_checkpoint(lowercase__       )
				lowerCAmelCase__      			=						convert_tax_to_pytorch(lowercase__     ,					num_layers=config.num_layers     ,					is_encoder_only=lowercase__       )
				lowerCAmelCase__      			=						make_state_dict(lowercase__     ,					lowercase__       )
				model.load_state_dict(lowercase__     ,					strict=lowercase__       )





def 	lowerCAmelCase_		(lowercase__						:       List[str]     ,					lowercase__						:       List[str]     ,					lowercase__						:       Any     ,					lowercase__						:       bool = False       )		->    Tuple:


				'''simple docstring'''




				lowerCAmelCase__      			=						TaConfig.from_json_file(lowercase__       )
				print(f'Building PyTorch model from configuration: {config}'       )
				# Non-v1.1 checkpoints could also use T5Model, but this works for all.
				# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
				if is_encoder_only:
								lowerCAmelCase__      			=						TaEncoderModel(lowercase__       )
				else:
								lowerCAmelCase__      			=						TaForConditionalGeneration(lowercase__       )

				# Load weights from tf checkpoint
				load_tax_weights_in_ta(lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__       )

				# Save pytorch-model
				print(f'Save PyTorch model to {pytorch_dump_path}'       )
				model.save_pretrained(lowercase__       )

				# Verify that we can load the checkpoint.
				model.from_pretrained(lowercase__       )
				print('''Done'''       )


if __name__ == "__main__":
			_UpperCAmelCase			:		Dict   				=    argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
			# Required parameters
			parser.add_argument(
			    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
			)
			parser.add_argument(
			    "--config_file",
			    default=None,
			    type=str,
			    required=True,
			    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			parser.add_argument(
			    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
			)
			_UpperCAmelCase			:		str   				=    parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
			)













import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:Tuple		      =    ['image_processor', 'tokenizer']
							UpperCamelCase_		:Tuple		      =    'ViTImageProcessor'
							UpperCamelCase_		:Dict		      =    ('CLIPTokenizer', 'CLIPTokenizerFast')




							def __init__(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any]	):
											lowerCAmelCase__      			=						None
											if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
															lowerCAmelCase__      			=						kwargs.pop('''feature_extractor'''	)

											lowerCAmelCase__      			=						image_processor if image_processor is not None else feature_extractor
											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`.'''	)

											super().__init__(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def __call__(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Any=None ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=None ,							**SCREAMING_SNAKE_CASE_     :   Optional[int]	):
											if text is None and visual_prompt is None and images is None:
															raise ValueError('''You have to specify either text, visual prompt or images.'''	)

											if text is not None and visual_prompt is not None:
															raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''	)

											if text is not None:
															lowerCAmelCase__      			=						self.tokenizer(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if visual_prompt is not None:
															lowerCAmelCase__      			=						self.image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if images is not None:
															lowerCAmelCase__      			=						self.image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if visual_prompt is not None and images is not None:
															lowerCAmelCase__      			=						{
															    '''pixel_values''': image_features.pixel_values,
															    '''conditional_pixel_values''': prompt_features.pixel_values,
															}
															return encoding
											elif text is not None and images is not None:
															lowerCAmelCase__      			=						image_features.pixel_values
															return encoding
											elif text is not None:
															return encoding
											elif visual_prompt is not None:
															lowerCAmelCase__      			=						{
															    '''conditional_pixel_values''': prompt_features.pixel_values,
															}
															return encoding
											else:
															return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_	) ,							tensor_type=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Union[str, Any] ,							*SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							**SCREAMING_SNAKE_CASE_     :   str	):
											return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Optional[int] ,							*SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							**SCREAMING_SNAKE_CASE_     :   Tuple	):
											return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							@property
							def 						__snake_case					(   self     :   str	):
											warnings.warn(
											    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
											return self.image_processor_class




							@property
							def 						__snake_case					(   self     :   Tuple	):
											warnings.warn(
											    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
											return self.image_processor





