


from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

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


@add_end_docstrings(a       )
class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							int     ,					**_lowerCAmelCase							:							List[Any]	) ->			Dict:




     """simple docstring"""
     super().__init__(**_lowerCAmelCase	)
     requires_backends(self     ,					"vision"	)
     requires_backends(self     ,					"torch"	)

     if self.framework != "pt":
        raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''	)

     self.check_model_type(_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Dict     ,					**_lowerCAmelCase							:							int	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       {}
     snake_case_            =       {}
     snake_case_            =       {}
     # preprocess args
     if "points_per_batch" in kwargs:
        snake_case_            =       kwargs["points_per_batch"]
     if "points_per_crop" in kwargs:
        snake_case_            =       kwargs["points_per_crop"]
     if "crops_n_layers" in kwargs:
        snake_case_            =       kwargs["crops_n_layers"]
     if "crop_overlap_ratio" in kwargs:
        snake_case_            =       kwargs["crop_overlap_ratio"]
     if "crop_n_points_downscale_factor" in kwargs:
        snake_case_            =       kwargs["crop_n_points_downscale_factor"]
     # postprocess args
     if "pred_iou_thresh" in kwargs:
        snake_case_            =       kwargs["pred_iou_thresh"]
     if "stability_score_offset" in kwargs:
        snake_case_            =       kwargs["stability_score_offset"]
     if "mask_threshold" in kwargs:
        snake_case_            =       kwargs["mask_threshold"]
     if "stability_score_thresh" in kwargs:
        snake_case_            =       kwargs["stability_score_thresh"]
     if "crops_nms_thresh" in kwargs:
        snake_case_            =       kwargs["crops_nms_thresh"]
     if "output_rle_mask" in kwargs:
        snake_case_            =       kwargs["output_rle_mask"]
     if "output_bboxes_mask" in kwargs:
        snake_case_            =       kwargs["output_bboxes_mask"]
     return preprocess_kwargs, forward_params, postprocess_kwargs



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




     """simple docstring"""
     return super().__call__(_lowerCAmelCase     ,					*_lowerCAmelCase     ,					num_workers=_lowerCAmelCase     ,					batch_size=_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							str=6_4     ,					_lowerCAmelCase							:							int = 0     ,					_lowerCAmelCase							:							float = 5_1_2 / 1_5_0_0     ,					_lowerCAmelCase							:							Optional[int] = 3_2     ,					_lowerCAmelCase							:							Optional[int] = 1     ,					) ->			Any:




     """simple docstring"""
     snake_case_            =       load_image(_lowerCAmelCase	)
     snake_case_            =       self.image_processor.size["longest_edge"]
     snake_case_     ,     snake_case_     ,     snake_case_     ,     snake_case_            =       self.image_processor.generate_crop_boxes(
         _lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
     snake_case_            =       self.image_processor(images=_lowerCAmelCase     ,					return_tensors="pt"	)

     with self.device_placement():
        if self.framework == "pt":
           snake_case_            =       self.get_inference_context()
           with inference_context():
              snake_case_            =       self._ensure_tensor_on_device(_lowerCAmelCase     ,					device=self.device	)
              snake_case_            =       self.model.get_image_embeddings(model_inputs.pop("pixel_values"	)	)
              snake_case_            =       image_embeddings

     snake_case_            =       grid_points.shape[1]
     snake_case_            =       points_per_batch if points_per_batch is not None else n_points

     if points_per_batch <= 0:
        raise ValueError(
            "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
            "To return all points at once, set points_per_batch to None"	)

     for i in range(0     ,					_lowerCAmelCase     ,					_lowerCAmelCase	):
        snake_case_            =       grid_points[:, i : i + points_per_batch, :, :]
        snake_case_            =       input_labels[:, i : i + points_per_batch]
        snake_case_            =       i == n_points - points_per_batch
        yield {
            "input_points": batched_points,
            "input_labels": labels,
            "input_boxes": crop_boxes,
            "is_last": is_last,
            **model_inputs,
        }



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Tuple=0.88     ,					_lowerCAmelCase							:							Dict=0.95     ,					_lowerCAmelCase							:							Any=0     ,					_lowerCAmelCase							:							Union[str, Any]=1     ,					) ->			str:




     """simple docstring"""
     snake_case_            =       model_inputs.pop("input_boxes"	)
     snake_case_            =       model_inputs.pop("is_last"	)
     snake_case_            =       model_inputs.pop("original_sizes"	).tolist()
     snake_case_            =       model_inputs.pop("reshaped_input_sizes"	).tolist()

     snake_case_            =       self.model(**_lowerCAmelCase	)

     # post processing happens here in order to avoid CPU GPU copies of ALL the masks
     snake_case_            =       model_outputs["pred_masks"]
     snake_case_            =       self.image_processor.post_process_masks(
         _lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					binarize=_lowerCAmelCase	)
     snake_case_            =       model_outputs["iou_scores"]
     snake_case_     ,     snake_case_     ,     snake_case_            =       self.image_processor.filter_masks(
         masks[0]     ,					iou_scores[0]     ,					original_sizes[0]     ,					input_boxes[0]     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					)
     return {
         "masks": masks,
         "is_last": is_last,
         "boxes": boxes,
         "iou_scores": iou_scores,
     }



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							Tuple=False     ,					_lowerCAmelCase							:							List[str]=False     ,					_lowerCAmelCase							:							Tuple=0.7     ,					) ->			List[str]:




     """simple docstring"""
     snake_case_            =       []
     snake_case_            =       []
     snake_case_            =       []
     for model_output in model_outputs:
        all_scores.append(model_output.pop("iou_scores"	)	)
        all_masks.extend(model_output.pop("masks"	)	)
        all_boxes.append(model_output.pop("boxes"	)	)

     snake_case_            =       torch.cat(_lowerCAmelCase	)
     snake_case_            =       torch.cat(_lowerCAmelCase	)
     snake_case_     ,     snake_case_     ,     snake_case_     ,     snake_case_            =       self.image_processor.post_process_for_mask_generation(
         _lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)

     snake_case_            =       defaultdict(_lowerCAmelCase	)
     for output in model_outputs:
        for k, v in output.items():
           extra[k].append(_lowerCAmelCase	)

     snake_case_            =       {}
     if output_rle_mask:
        snake_case_            =       rle_mask

     if output_bboxes_mask:
        snake_case_            =       bounding_boxes

     return {"masks": output_masks, "scores": iou_scores, **optional, **extra}








import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
SCREAMING_SNAKE_CASE						:Tuple			=  {
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}



def       _lowerCAmelCase    (				lowerCAmelCase_				:Any				)->Any:
   '''simple docstring'''






   snake_case_            =       list(s_dict.keys()				)
   for key in keys:
      snake_case_            =       r".*/layers_(\d+)"
      snake_case_            =       key
      if re.match(lowerCAmelCase_		,    lowerCAmelCase_				):
         snake_case_            =       re.sub(r"layers_(\d+)"		,    r"block/\1/layer"		,    lowerCAmelCase_				)

      snake_case_            =       r"(encoder|decoder)\/"

      if re.match(lowerCAmelCase_		,    lowerCAmelCase_				):
         snake_case_            =       re.match(lowerCAmelCase_		,    lowerCAmelCase_				).groups()
         if groups[0] == "encoder":
            snake_case_            =       re.sub(r"/mlp/"		,    r"/1/mlp/"		,    lowerCAmelCase_				)
            snake_case_            =       re.sub(r"/pre_mlp_layer_norm/"		,    r"/1/layer_norm/"		,    lowerCAmelCase_				)

         elif groups[0] == "decoder":
            snake_case_            =       re.sub(r"/mlp/"		,    r"/2/mlp/"		,    lowerCAmelCase_				)
            snake_case_            =       re.sub(r"/pre_mlp_layer_norm/"		,    r"/2/layer_norm/"		,    lowerCAmelCase_				)

        # 2. Convert other classic mappings
      for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
         if old_key in new_key:
            snake_case_            =       new_key.replace(lowerCAmelCase_		,    lowerCAmelCase_				)

      print(F'''{key} -> {new_key}'''				)
      snake_case_            =       s_dict.pop(lowerCAmelCase_				)

   if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
      snake_case_            =       s_dict[
          "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
      ].T
   if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
      snake_case_            =       s_dict[
          "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
      ].T

   # 3. Take extra care of the EXPERTS layer
   for key in list(s_dict.keys()				):
      if "expert" in key:
         snake_case_            =       s_dict[key].shape[0]
         snake_case_            =       s_dict[key]
         for idx in range(lowerCAmelCase_				):
            snake_case_            =       expert_weihts[idx]
            print(F'''{key} -> {key.replace('expert/'		,    'nested fstring'				)}'''				)

         s_dict.pop(lowerCAmelCase_				)

   return s_dict


SCREAMING_SNAKE_CASE						:Optional[Any]			=  {
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:Any				)->Optional[int]:
   '''simple docstring'''






   import regex as re

   with open(lowerCAmelCase_		,    "r"				) as f:
      snake_case_            =       f.read()

   snake_case_            =       re.findall(r"(.*) = ([0-9.]*)"		,    lowerCAmelCase_				)
   snake_case_            =       {}
   for param, value in regex_match:
      if param in GIN_TO_CONFIG_MAPPING and value != "":
         snake_case_            =       float(lowerCAmelCase_				) if "." in value else int(lowerCAmelCase_				)

   snake_case_            =       re.findall(r"(.*activations) = \(\'(.*)\',\)"		,    lowerCAmelCase_				)[0]
   snake_case_            =       str(activation[1]				)

   snake_case_            =       num_experts
   snake_case_            =       SwitchTransformersConfig(**lowerCAmelCase_				)
   return config



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:List[Any]		,    lowerCAmelCase_				:Dict=None		,    lowerCAmelCase_				:str="./"		,    lowerCAmelCase_				:Optional[int]=8				)->List[str]:
   '''simple docstring'''






   print(F'''Loading flax weights from : {flax_checkpoint_path}'''				)
   snake_case_            =       checkpoints.load_tax_checkpoint(lowerCAmelCase_				)

   if gin_file is not None:
      snake_case_            =       convert_gin_to_config(lowerCAmelCase_		,    lowerCAmelCase_				)
   else:
      snake_case_            =       SwitchTransformersConfig.from_pretrained(lowerCAmelCase_				)

   snake_case_            =       SwitchTransformersForConditionalGeneration(lowerCAmelCase_				)

   snake_case_            =       flax_params["target"]
   snake_case_            =       flatten_dict(lowerCAmelCase_		,    sep="/"				)
   snake_case_            =       rename_keys(lowerCAmelCase_				)
   snake_case_            =       unflatten_dict(lowerCAmelCase_		,    sep="/"				)

   # Load the flax params in the PT model
   load_flax_weights_in_pytorch_model(lowerCAmelCase_		,    lowerCAmelCase_				)

   print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
   pt_model.save_pretrained(lowerCAmelCase_				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:int			=  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--switch_t5x_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
           ''' model architecture. If not provided, a `gin_file` has to be provided.'''
       ),
   )
   parser.add_argument(
       '''--gin_file''',
       default=None,
       type=str,
       required=False,
       help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
   )
   parser.add_argument(
       '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
   )
   parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
   SCREAMING_SNAKE_CASE						:int			=  parser.parse_args()
   convert_flax_checkpoint_to_pytorch(
       args.switch_tax_checkpoint_path,
       args.config_name,
       args.gin_file,
       args.pytorch_dump_folder_path,
       args.num_experts,
   )




