


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

__lowerCAmelCase			=							logging.get_logger(__name__)





@add_end_docstrings(__UpperCamelCase				)
class   __a      (   __UpperCamelCase				):
     def __init__(       self    ,			**lowerCAmelCase__			) ->    Union[str, Any]:




            '''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 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    Tuple:




            '''simple docstring'''
            lowercase__:       List[Any]        =       {}
            lowercase__:       Optional[int]        =       {}
            lowercase__:       List[str]        =       {}
            # preprocess args
            if "points_per_batch" in kwargs:
                   lowercase__:       Tuple        =       kwargs['points_per_batch']
            if "points_per_crop" in kwargs:
                   lowercase__:       Optional[Any]        =       kwargs['points_per_crop']
            if "crops_n_layers" in kwargs:
                   lowercase__:       Dict        =       kwargs['crops_n_layers']
            if "crop_overlap_ratio" in kwargs:
                   lowercase__:       str        =       kwargs['crop_overlap_ratio']
            if "crop_n_points_downscale_factor" in kwargs:
                   lowercase__:       str        =       kwargs['crop_n_points_downscale_factor']
            # postprocess args
            if "pred_iou_thresh" in kwargs:
                   lowercase__:       Dict        =       kwargs['pred_iou_thresh']
            if "stability_score_offset" in kwargs:
                   lowercase__:       Optional[int]        =       kwargs['stability_score_offset']
            if "mask_threshold" in kwargs:
                   lowercase__:       List[str]        =       kwargs['mask_threshold']
            if "stability_score_thresh" in kwargs:
                   lowercase__:       Optional[int]        =       kwargs['stability_score_thresh']
            if "crops_nms_thresh" in kwargs:
                   lowercase__:       Dict        =       kwargs['crops_nms_thresh']
            if "output_rle_mask" in kwargs:
                   lowercase__:       int        =       kwargs['output_rle_mask']
            if "output_bboxes_mask" in kwargs:
                   lowercase__:       Optional[Any]        =       kwargs['output_bboxes_mask']
            return preprocess_kwargs, forward_params, postprocess_kwargs
     def __call__(       self    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=None    ,			**lowerCAmelCase__			) ->    Optional[Any]:




            '''simple docstring'''
            return super().__call__(lowerCAmelCase__    ,			*lowerCAmelCase__    ,			num_workers=lowerCAmelCase__    ,			batch_size=lowerCAmelCase__    ,			**lowerCAmelCase__			)
     def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=64    ,			lowerCAmelCase__ = 0    ,			lowerCAmelCase__ = 512 / 1_500    ,			lowerCAmelCase__ = 32    ,			lowerCAmelCase__ = 1    ,			) ->    Any:




            '''simple docstring'''
            lowercase__:       List[str]        =       load_image(lowerCAmelCase__			)
            lowercase__:       Optional[Any]        =       self.image_processor.size['longest_edge']
            lowercase__:       int        =       self.image_processor.generate_crop_boxes(
                lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)
            lowercase__:       Optional[Any]        =       self.image_processor(images=lowerCAmelCase__    ,			return_tensors='pt'			)

            with self.device_placement():
                   if self.framework == "pt":
                          lowercase__:       Union[str, Any]        =       self.get_inference_context()
                          with inference_context():
                                 lowercase__:       Tuple        =       self._ensure_tensor_on_device(lowerCAmelCase__    ,			device=self.device			)
                                 lowercase__:       Any        =       self.model.get_image_embeddings(model_inputs.pop('pixel_values'			)			)
                                 lowercase__:       Optional[Any]        =       image_embeddings

            lowercase__:       Union[str, Any]        =       grid_points.shape[1]
            lowercase__:       str        =       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__			):
                   lowercase__:       Any        =       grid_points[:, i : i + points_per_batch, :, :]
                   lowercase__:       List[str]        =       input_labels[:, i : i + points_per_batch]
                   lowercase__:       str        =       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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=0.8_8    ,			lowerCAmelCase__=0.9_5    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=1    ,			) ->    Any:




            '''simple docstring'''
            lowercase__:       List[Any]        =       model_inputs.pop('input_boxes'			)
            lowercase__:       Union[str, Any]        =       model_inputs.pop('is_last'			)
            lowercase__:       Tuple        =       model_inputs.pop('original_sizes'			).tolist()
            lowercase__:       List[Any]        =       model_inputs.pop('reshaped_input_sizes'			).tolist()

            lowercase__:       Dict        =       self.model(**lowerCAmelCase__			)

            # post processing happens here in order to avoid CPU GPU copies of ALL the masks
            lowercase__:       str        =       model_outputs['pred_masks']
            lowercase__:       List[str]        =       self.image_processor.post_process_masks(
                lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			binarize=lowerCAmelCase__			)
            lowercase__:       Union[str, Any]        =       model_outputs['iou_scores']
            lowercase__:       Any        =       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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=False    ,			lowerCAmelCase__=False    ,			lowerCAmelCase__=0.7    ,			) ->    str:




            '''simple docstring'''
            lowercase__:       List[str]        =       []
            lowercase__:       Optional[int]        =       []
            lowercase__:       Optional[Any]        =       []
            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'			)			)

            lowercase__:       int        =       torch.cat(lowerCAmelCase__			)
            lowercase__:       Union[str, Any]        =       torch.cat(lowerCAmelCase__			)
            lowercase__:       str        =       self.image_processor.post_process_for_mask_generation(
                lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)

            lowercase__:       int        =       defaultdict(lowerCAmelCase__			)
            for output in model_outputs:
                   for k, v in output.items():
                          extra[k].append(lowerCAmelCase__			)

            lowercase__:       Union[str, Any]        =       {}
            if output_rle_mask:
                   lowercase__:       Dict        =       rle_mask

            if output_bboxes_mask:
                   lowercase__:       Dict        =       bounding_boxes

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






import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase			=							logging.get_logger(__name__)




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    Any:
       lowercase__:       Dict        =       os.path.abspath(snake_case					)
       logger.info(f'Converting TensorFlow checkpoint from {tf_path}'					)
       # Load weights from TF model
       lowercase__:       Optional[Any]        =       tf.train.list_variables(snake_case					)
       lowercase__:       List[Any]        =       []
       lowercase__:       Tuple        =       []
       lowercase__:       int        =       []
       for full_name, shape in init_vars:
              # logger.info(f"Loading TF weight {name} with shape {shape}")
              lowercase__:       Union[str, Any]        =       full_name.split('/'					)
              if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
                     logger.info(f'Skipping non-model layer {full_name}'					)
                     continue
              if "optimizer" in full_name:
                     logger.info(f'Skipping optimization layer {full_name}'					)
                     continue
              if name[0] == "model":
                     # ignore initial 'model'
                     lowercase__:       str        =       name[1:]
              # figure out how many levels deep the name is
              lowercase__:       Optional[Any]        =       0
              for _name in name:
                     if _name.startswith('layer_with_weights'					):
                            depth += 1
                     else:
                            break
              layer_depth.append(snake_case					)
              # read data
              lowercase__:       Optional[Any]        =       tf.train.load_variable(snake_case      ,							snake_case					)
              names.append('/'.join(snake_case					)					)
              arrays.append(snake_case					)
       logger.info(f'Read a total of {len(snake_case					):,} layers'					)

       # Sanity check
       if len(set(snake_case					)					) != 1:
              raise ValueError(f'Found layer names with different depths (layer depth {list(set(snake_case					)					)})'					)
       lowercase__:       Any        =       list(set(snake_case					)					)[0]
       if layer_depth != 1:
              raise ValueError(
                  'The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'
                  ' heads.'					)

       # convert layers
       logger.info('Converting weights...'					)
       for full_name, array in zip(snake_case      ,							snake_case					):
              lowercase__:       Optional[int]        =       full_name.split('/'					)
              lowercase__:       List[Any]        =       model
              lowercase__:       Any        =       []
              for i, m_name in enumerate(snake_case					):
                     if m_name == ".ATTRIBUTES":
                            # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                            break
                     if m_name.startswith('layer_with_weights'					):
                            lowercase__:       Any        =       int(m_name.split('-'					)[-1]					)
                            if layer_num <= 2:
                                   # embedding layers
                                   # layer_num 0: word_embeddings
                                   # layer_num 1: position_embeddings
                                   # layer_num 2: token_type_embeddings
                                   continue
                            elif layer_num == 3:
                                   # embedding LayerNorm
                                   trace.extend(['embeddings', 'LayerNorm']					)
                                   lowercase__:       str        =       getattr(snake_case      ,							'embeddings'					)
                                   lowercase__:       int        =       getattr(snake_case      ,							'LayerNorm'					)
                            elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                                   # encoder layers
                                   trace.extend(['encoder', 'layer', str(layer_num - 4					)]					)
                                   lowercase__:       int        =       getattr(snake_case      ,							'encoder'					)
                                   lowercase__:       List[str]        =       getattr(snake_case      ,							'layer'					)
                                   lowercase__:       Union[str, Any]        =       pointer[layer_num - 4]
                            elif layer_num == config.num_hidden_layers + 4:
                                   # pooler layer
                                   trace.extend(['pooler', 'dense']					)
                                   lowercase__:       Tuple        =       getattr(snake_case      ,							'pooler'					)
                                   lowercase__:       Tuple        =       getattr(snake_case      ,							'dense'					)
                     elif m_name == "embeddings":
                            trace.append('embeddings'					)
                            lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'embeddings'					)
                            if layer_num == 0:
                                   trace.append('word_embeddings'					)
                                   lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'word_embeddings'					)
                            elif layer_num == 1:
                                   trace.append('position_embeddings'					)
                                   lowercase__:       Dict        =       getattr(snake_case      ,							'position_embeddings'					)
                            elif layer_num == 2:
                                   trace.append('token_type_embeddings'					)
                                   lowercase__:       Optional[Any]        =       getattr(snake_case      ,							'token_type_embeddings'					)
                            else:
                                   raise ValueError(f'Unknown embedding layer with name {full_name}'					)
                            trace.append('weight'					)
                            lowercase__:       List[str]        =       getattr(snake_case      ,							'weight'					)
                     elif m_name == "_attention_layer":
                            # self-attention layer
                            trace.extend(['attention', 'self']					)
                            lowercase__:       int        =       getattr(snake_case      ,							'attention'					)
                            lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'self'					)
                     elif m_name == "_attention_layer_norm":
                            # output attention norm
                            trace.extend(['attention', 'output', 'LayerNorm']					)
                            lowercase__:       str        =       getattr(snake_case      ,							'attention'					)
                            lowercase__:       Optional[Any]        =       getattr(snake_case      ,							'output'					)
                            lowercase__:       List[Any]        =       getattr(snake_case      ,							'LayerNorm'					)
                     elif m_name == "_attention_output_dense":
                            # output attention dense
                            trace.extend(['attention', 'output', 'dense']					)
                            lowercase__:       Optional[Any]        =       getattr(snake_case      ,							'attention'					)
                            lowercase__:       Optional[int]        =       getattr(snake_case      ,							'output'					)
                            lowercase__:       Optional[Any]        =       getattr(snake_case      ,							'dense'					)
                     elif m_name == "_output_dense":
                            # output dense
                            trace.extend(['output', 'dense']					)
                            lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'output'					)
                            lowercase__:       List[Any]        =       getattr(snake_case      ,							'dense'					)
                     elif m_name == "_output_layer_norm":
                            # output dense
                            trace.extend(['output', 'LayerNorm']					)
                            lowercase__:       Any        =       getattr(snake_case      ,							'output'					)
                            lowercase__:       str        =       getattr(snake_case      ,							'LayerNorm'					)
                     elif m_name == "_key_dense":
                            # attention key
                            trace.append('key'					)
                            lowercase__:       Tuple        =       getattr(snake_case      ,							'key'					)
                     elif m_name == "_query_dense":
                            # attention query
                            trace.append('query'					)
                            lowercase__:       List[str]        =       getattr(snake_case      ,							'query'					)
                     elif m_name == "_value_dense":
                            # attention value
                            trace.append('value'					)
                            lowercase__:       Optional[int]        =       getattr(snake_case      ,							'value'					)
                     elif m_name == "_intermediate_dense":
                            # attention intermediate dense
                            trace.extend(['intermediate', 'dense']					)
                            lowercase__:       Any        =       getattr(snake_case      ,							'intermediate'					)
                            lowercase__:       str        =       getattr(snake_case      ,							'dense'					)
                     elif m_name == "_output_layer_norm":
                            # output layer norm
                            trace.append('output'					)
                            lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'output'					)
                     # weights & biases
                     elif m_name in ["bias", "beta"]:
                            trace.append('bias'					)
                            lowercase__:       str        =       getattr(snake_case      ,							'bias'					)
                     elif m_name in ["kernel", "gamma"]:
                            trace.append('weight'					)
                            lowercase__:       Tuple        =       getattr(snake_case      ,							'weight'					)
                     else:
                            logger.warning(f'Ignored {m_name}'					)
        # for certain layers reshape is necessary
              lowercase__:       Any        =       '.'.join(snake_case					)
              if re.match(R'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'      ,							snake_case					) or re.match(
                  R'(\S+)\.attention\.output\.dense\.weight'      ,							snake_case					):
                     lowercase__:       Union[str, Any]        =       array.reshape(pointer.data.shape					)
              if "kernel" in full_name:
                     lowercase__:       str        =       array.transpose()
              if pointer.shape == array.shape:
                     lowercase__:       Union[str, Any]        =       torch.from_numpy(snake_case					)
              else:
                     raise ValueError(
                         f'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
                         f' {array.shape}'					)
              logger.info(f'Successfully set variable {full_name} to PyTorch layer {trace}'					)
       return model




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    Any:
       # Instantiate model
       logger.info(f'Loading model based on config from {config_path}...'					)
       lowercase__:       int        =       BertConfig.from_json_file(snake_case					)
       lowercase__:       Tuple        =       BertModel(snake_case					)

       # Load weights from checkpoint
       logger.info(f'Loading weights from checkpoint {tf_checkpoint_path}...'					)
       load_tfa_weights_in_bert(snake_case      ,							snake_case      ,							snake_case					)

       # Save pytorch-model
       logger.info(f'Saving PyTorch model to {pytorch_dump_path}...'					)
       torch.save(model.state_dict()      ,							snake_case					)


if __name__ == "__main__":
 __lowerCAmelCase			=							argparse.ArgumentParser()
 parser.add_argument(
     '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
 )
 parser.add_argument(
     '''--bert_config_file''',
     type=str,
     required=True,
     help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''',
     type=str,
     required=True,
     help='''Path to the output PyTorch model (must include filename).''',
 )
 __lowerCAmelCase			=							parser.parse_args()
 convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)


