






"""simple docstring"""

from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     import torch

     from transformers.modeling_outputs import BaseModelOutput

     from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowercase__					:						Union[str, Any]												=			logging.get_logger(__name__)




@add_end_docstrings(lowerCAmelCase__)
class  _UpperCAmelCase (    lowerCAmelCase__):

 def __init__(  self	: Any						,  **lowercase_	: Optional[Any]       ):
     super().__init__(**lowercase_       )

     if self.framework == "tf":
         raise ValueError(f"The {self.__class__} is only available in PyTorch."       )

     requires_backends(self						,  '''vision'''       )
     self.check_model_type(lowercase_       )

 def __call__(  self	: Optional[int]						,  lowercase_	: Union[str, "Image.Image", List[Dict[str, Any]]]						,  lowercase_	: Union[str, List[str]] = None						,  **lowercase_	: int						,  ):
     if "text_queries" in kwargs:
         snake_case_				:  str       =			kwargs.pop('''text_queries'''       )

     if isinstance(lowercase_						,  (str, Image.Image)       ):
         snake_case_				:  List[Any]       =			{'''image''': image, '''candidate_labels''': candidate_labels}
     else:
         snake_case_				:  List[Any]       =			image
     snake_case_				:  Tuple       =			super().__call__(lowercase_						,  **lowercase_       )
     return results

 def 		_snake_case      (  self	: List[str]						,  **lowercase_	: str       ):
     snake_case_				:  Optional[int]       =			{}
     if "threshold" in kwargs:
         snake_case_				:  Tuple       =			kwargs['''threshold''']
     if "top_k" in kwargs:
         snake_case_				:  Dict       =			kwargs['''top_k''']
     return {}, {}, postprocess_params

 def 		_snake_case      (  self	: str						,  lowercase_	: List[Any]       ):
     snake_case_				:  Optional[Any]       =			load_image(inputs['''image''']       )
     snake_case_				:  Tuple       =			inputs['''candidate_labels''']
     if isinstance(lowercase_						,  lowercase_       ):
         snake_case_				:  Optional[int]       =			candidate_labels.split(''','''       )

     snake_case_				:  Union[str, Any]       =			torch.tensor([[image.height, image.width]]						,  dtype=torch.intaa       )
     for i, candidate_label in enumerate(lowercase_       ):
         snake_case_				:  Any       =			self.tokenizer(lowercase_						,  return_tensors=self.framework       )
         snake_case_				:  Tuple       =			self.image_processor(lowercase_						,  return_tensors=self.framework       )
         yield {
             "is_last": i == len(lowercase_       ) - 1,
             "target_size": target_size,
             "candidate_label": candidate_label,
             **text_inputs,
             **image_features,
         }

 def 		_snake_case      (  self	: List[Any]						,  lowercase_	: Optional[int]       ):
     snake_case_				:  Optional[Any]       =			model_inputs.pop('''target_size'''       )
     snake_case_				:  Tuple       =			model_inputs.pop('''candidate_label'''       )
     snake_case_				:  int       =			model_inputs.pop('''is_last'''       )

     snake_case_				:  List[Any]       =			self.model(**lowercase_       )

     snake_case_				:  Optional[int]       =			{'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
     return model_outputs

 def 		_snake_case      (  self	: Dict						,  lowercase_	: Optional[int]						,  lowercase_	: List[str]=0.1						,  lowercase_	: Optional[int]=None       ):
     snake_case_				:  Any       =			[]
     for model_output in model_outputs:
         snake_case_				:  Any       =			model_output['''candidate_label''']
         snake_case_				:  Tuple       =			BaseModelOutput(lowercase_       )
         snake_case_				:  int       =			self.image_processor.post_process_object_detection(
             outputs=lowercase_						,  threshold=lowercase_						,  target_sizes=model_output['''target_size''']       )[0]

         for index in outputs["scores"].nonzero():
             snake_case_				:  str       =			outputs['''scores'''][index].item()
             snake_case_				:  Dict       =			self._get_bounding_box(outputs['''boxes'''][index][0]       )

             snake_case_				:  List[str]       =			{'''score''': score, '''label''': label, '''box''': box}
             results.append(lowercase_       )

     snake_case_				:  str       =			sorted(lowercase_						,  key=lambda lowercase_       : x["score"]						,  reverse=lowercase_       )
     if top_k:
         snake_case_				:  Optional[Any]       =			results[:top_k]

     return results






 def 		_snake_case      (  self	: List[Any]						,  lowercase_	: "torch.Tensor"       ):
     if self.framework != "pt":
         raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''       )
     snake_case_				:  Optional[Any]       =			box.int().tolist()
     snake_case_				:  Any       =			{
         '''xmin''': xmin,
         '''ymin''': ymin,
         '''xmax''': xmax,
         '''ymax''': ymax,
     }
     return bbox










"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			AutoConfig.from_pretrained(_a					)
    snake_case_				:  Tuple       =			FlaxAutoModelForSeqaSeqLM.from_config(config=_a					)
    snake_case_				:  Union[str, Any]       =			checkpoints.load_tax_checkpoint(_a					)

    snake_case_				:  Optional[int]       =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        snake_case_				:  str       =			'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        snake_case_				:  List[str]       =			'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  Dict       =			'''TransientGlobalSelfAttention'''
    else:
        raise ValueError(
            '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
            ''' attribute with a value from [\'local\', \'transient-global].'''					)

    # Encoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Any       =			f"layers_{str(_a					)}"

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

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

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

        if split_mlp_wi:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  str       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[Any]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Optional[Any]       =			flax_model.params['''encoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  List[str]       =			tax_attention_key
        snake_case_				:  Optional[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  str       =			tax_attention_value

        snake_case_				:  Dict       =			tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Union[str, Any]       =			tax_global_layer_norm

        if split_mlp_wi:
            snake_case_				:  Any       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  Union[str, Any]       =			tax_mlp_wi

        snake_case_				:  List[Any]       =			tax_mlp_wo
        snake_case_				:  int       =			tax_mlp_layer_norm

        snake_case_				:  Any       =			flax_model_encoder_layer_block

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

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        snake_case_				:  Tuple       =			tax_encoder_global_rel_embedding

    # Assigning
    snake_case_				:  Dict       =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    snake_case_				:  Any       =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Tuple       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  str       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        snake_case_				:  Any       =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        snake_case_				:  Optional[Any]       =			tax_enc_dec_attention_module['''key''']['''kernel''']
        snake_case_				:  str       =			tax_enc_dec_attention_module['''out''']['''kernel''']
        snake_case_				:  Union[str, Any]       =			tax_enc_dec_attention_module['''query''']['''kernel''']
        snake_case_				:  List[str]       =			tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  Dict       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Dict       =			flax_model.params['''decoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  int       =			tax_attention_key
        snake_case_				:  List[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  Dict       =			tax_attention_value

        snake_case_				:  str       =			tax_pre_attention_layer_norm

        snake_case_				:  Any       =			tax_enc_dec_attention_key
        snake_case_				:  str       =			tax_enc_dec_attention_out
        snake_case_				:  int       =			tax_enc_dec_attention_query
        snake_case_				:  Any       =			tax_enc_dec_attention_value

        snake_case_				:  Optional[Any]       =			tax_cross_layer_norm

        if split_mlp_wi:
            snake_case_				:  Tuple       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  List[Any]       =			tax_mlp_wi

        snake_case_				:  Dict       =			tax_mlp_wo

        snake_case_				:  List[Any]       =			txa_mlp_layer_norm

        snake_case_				:  Optional[int]       =			flax_model_decoder_layer_block

    # Decoder Normalization
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    snake_case_				:  Tuple       =			txa_decoder_norm

    # Only for layer 0:
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Optional[Any]       =			tax_decoder_rel_embedding

    # Token Embeddings
    snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''token_embedder''']['''embedding''']
    snake_case_				:  Optional[int]       =			txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

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


if __name__ == "__main__":
     lowercase__					:						Tuple												=			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
     )
     parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
     parser.add_argument(
         '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
     )
     lowercase__					:						Dict												=			parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





