



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       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	int="attention"				)  ->     List[Any]:
 _snake_case    :  Dict					=					params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
 _snake_case    :  Optional[int]					=					params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
 _snake_case    :  List[str]					=					params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
 _snake_case    :  List[Any]					=					params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
 return k, o, q, v



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	str=False				)  ->     str:
 if split_mlp_wi:
  _snake_case    :  List[Any]					=					params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
  _snake_case    :  int					=					params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
  _snake_case    :  Dict					=					(wi_a, wi_a)
 else:
  _snake_case    :  int					=					params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

 _snake_case    :  Tuple					=					params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
 return wi, wo



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				)  ->     Dict:
 return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	dict				,       *, SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	bool				)  ->     int:
 _snake_case    :  Any					=					traverse_util.flatten_dict(variables["""target"""]				)
 _snake_case    :  str					=					{"""/""".join(SCREAMING_SNAKE_CASE__				): v for k, v in old.items()}

 # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
 _snake_case    :  Optional[int]					=					"""encoder/layers_0/mlp/wi_0/kernel""" in old
 print("""Split MLP:"""				,       SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Any					=					collections.OrderedDict()

 # Shared embeddings.
 _snake_case    :  Union[str, Any]					=					old["""token_embedder/embedding"""]

 # Encoder.
 for i in range(SCREAMING_SNAKE_CASE__				):
  # Block i, layer 0 (Self Attention).
  _snake_case    :  Tuple					=					tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       """encoder"""				,       """pre_attention_layer_norm"""				)
  _snake_case	,    _snake_case	,    _snake_case	,    _snake_case    :  int					=					tax_attention_lookup(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       """encoder"""				,       """attention"""				)
  _snake_case    :  Optional[int]					=					layer_norm
  _snake_case    :  int					=					k.T
  _snake_case    :  str					=					o.T
  _snake_case    :  List[str]					=					q.T
  _snake_case    :  Union[str, Any]					=					v.T

  # Block i, layer 1 (MLP).
  _snake_case    :  List[Any]					=					tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       """encoder"""				,       """pre_mlp_layer_norm"""				)
  _snake_case	,    _snake_case    :  Any					=					tax_mlp_lookup(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       """encoder"""				,       SCREAMING_SNAKE_CASE__				)
  _snake_case    :  Optional[int]					=					layer_norm
  if split_mlp_wi:
   _snake_case    :  Optional[int]					=					wi[0].T
   _snake_case    :  Any					=					wi[1].T
  else:
   _snake_case    :  List[str]					=					wi.T
  _snake_case    :  Any					=					wo.T

 _snake_case    :  Optional[Any]					=					old[
     """encoder/relpos_bias/rel_embedding"""
 ].T
 _snake_case    :  Optional[Any]					=					old["""encoder/encoder_norm/scale"""]

 if not is_encoder_only:
  # Decoder.
  for i in range(SCREAMING_SNAKE_CASE__				):
   # Block i, layer 0 (Self Attention).
   _snake_case    :  Tuple					=					tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       """decoder"""				,       """pre_self_attention_layer_norm"""				)
   _snake_case	,    _snake_case	,    _snake_case	,    _snake_case    :  Dict					=					tax_attention_lookup(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       """decoder"""				,       """self_attention"""				)
   _snake_case    :  List[Any]					=					layer_norm
   _snake_case    :  List[str]					=					k.T
   _snake_case    :  Optional[int]					=					o.T
   _snake_case    :  Dict					=					q.T
   _snake_case    :  Union[str, Any]					=					v.T

   # Block i, layer 1 (Cross Attention).
   _snake_case    :  Dict					=					tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       """decoder"""				,       """pre_cross_attention_layer_norm"""				)
   _snake_case	,    _snake_case	,    _snake_case	,    _snake_case    :  Optional[int]					=					tax_attention_lookup(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       """decoder"""				,       """encoder_decoder_attention"""				)
   _snake_case    :  Optional[int]					=					layer_norm
   _snake_case    :  int					=					k.T
   _snake_case    :  Any					=					o.T
   _snake_case    :  int					=					q.T
   _snake_case    :  int					=					v.T

   # Block i, layer 2 (MLP).
   _snake_case    :  Optional[int]					=					tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       """decoder"""				,       """pre_mlp_layer_norm"""				)
   _snake_case	,    _snake_case    :  Tuple					=					tax_mlp_lookup(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       """decoder"""				,       SCREAMING_SNAKE_CASE__				)
   _snake_case    :  Dict					=					layer_norm
   if split_mlp_wi:
    _snake_case    :  str					=					wi[0].T
    _snake_case    :  List[Any]					=					wi[1].T
   else:
    _snake_case    :  int					=					wi.T
   _snake_case    :  int					=					wo.T

  _snake_case    :  List[str]					=					old["""decoder/decoder_norm/scale"""]
  _snake_case    :  int					=					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:
   _snake_case    :  Dict					=					old["""decoder/logits_dense/kernel"""].T

 return new



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	bool				)  ->     Optional[int]:
 _snake_case    :  Optional[Any]					=					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:
  _snake_case    :  Dict					=					state_dict["""shared.weight"""]

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   _snake_case    :  Tuple					=					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."""				)
   _snake_case    :  str					=					state_dict["""shared.weight"""]

 return state_dict



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				)  ->     Optional[int]:
 _snake_case    :  Optional[int]					=					checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  str					=					convert_tax_to_pytorch(SCREAMING_SNAKE_CASE__				,       num_layers=config.num_layers				,       is_encoder_only=SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					make_state_dict(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 model.load_state_dict(SCREAMING_SNAKE_CASE__				,       strict=SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	bool = False				)  ->     Optional[int]:
 _snake_case    :  List[Any]					=					TaConfig.from_json_file(SCREAMING_SNAKE_CASE__				)
 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:
  _snake_case    :  Dict					=					TaEncoderModel(SCREAMING_SNAKE_CASE__				)
 else:
  _snake_case    :  Optional[int]					=					TaForConditionalGeneration(SCREAMING_SNAKE_CASE__				)

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

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

 # Verify that we can load the checkpoint.
 model.from_pretrained(SCREAMING_SNAKE_CASE__				)
 print("""Done"""				)


if __name__ == "__main__":
  a__    =	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
  )
  a__    =	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 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		["""image_processor""", """tokenizer"""]
   snake_case_					: str          =		"""ChineseCLIPImageProcessor"""
   snake_case_					: Tuple          =		("""BertTokenizer""", """BertTokenizerFast""")

   def __init__(					self							:			Any				,		lowerCAmelCase							:			Dict=None				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			str)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""				,		lowerCAmelCase				,		)
     _snake_case    :  Tuple					=					kwargs.pop("""feature_extractor""")

    _snake_case    :  Dict					=					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__(lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Optional[int]					=					self.image_processor

   def __call__(					self							:			List[Any]				,		lowerCAmelCase							:			str=None				,		lowerCAmelCase							:			Optional[int]=None				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			Optional[int])				->				Optional[Any]:

    """simple docstring"""







    if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none.""")

    if text is not None:
     _snake_case    :  Dict					=					self.tokenizer(lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase)

    if images is not None:
     _snake_case    :  Any					=					self.image_processor(lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase)

    if text is not None and images is not None:
     _snake_case    :  Optional[Any]					=					image_features.pixel_values
     return encoding
    elif text is not None:
     return encoding
    else:
     return BatchEncoding(data=dict(**lowerCAmelCase)				,		tensor_type=lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Dict				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			Any)				->				Optional[Any]:

    """simple docstring"""







    return self.tokenizer.batch_decode(*lowerCAmelCase				,		**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Tuple				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			int)				->				str:

    """simple docstring"""







    return self.tokenizer.decode(*lowerCAmelCase				,		**lowerCAmelCase)

   @property
   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				List[str]:

    """simple docstring"""







    _snake_case    :  Dict					=					self.tokenizer.model_input_names
    _snake_case    :  int					=					self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


   @property
   def      UpperCamelCase_  (					self							:			str)				->				Tuple:

    """simple docstring"""







    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,		lowerCAmelCase				,		)
    return self.image_processor_class
