





'''simple docstring'''

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

lowercase_					= logging.get_logger(__name__)


@add_end_docstrings(snake_case_       )
class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		**A       )		->   Any:
        super().__init__(**A       )
        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(A       )


    def 			snake_case_(		self ,		**A       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			{}
        _SCREAMING_SNAKE_CASE									=			{}
        _SCREAMING_SNAKE_CASE									=			{}
        # preprocess args
        if "points_per_batch" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""points_per_batch"""]
        if "points_per_crop" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""points_per_crop"""]
        if "crops_n_layers" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""crops_n_layers"""]
        if "crop_overlap_ratio" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""crop_overlap_ratio"""]
        if "crop_n_points_downscale_factor" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""crop_n_points_downscale_factor"""]
        # postprocess args
        if "pred_iou_thresh" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""pred_iou_thresh"""]
        if "stability_score_offset" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""stability_score_offset"""]
        if "mask_threshold" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""mask_threshold"""]
        if "stability_score_thresh" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""stability_score_thresh"""]
        if "crops_nms_thresh" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""crops_nms_thresh"""]
        if "output_rle_mask" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""output_rle_mask"""]
        if "output_bboxes_mask" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""output_bboxes_mask"""]
        return preprocess_kwargs, forward_params, postprocess_kwargs


    def __call__(		self ,		A ,		*A ,		A=None ,		A=None ,		**A       )		->   List[Any]:
        return super().__call__(A ,		*A ,		num_workers=A ,		batch_size=A ,		**A       )


    def 			snake_case_(		self ,		A ,		A=64 ,		A = 0 ,		A = 512 / 1500 ,		A = 32 ,		A = 1 ,		)		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			load_image(A       )
        _SCREAMING_SNAKE_CASE									=			self.image_processor.size["""longest_edge"""]
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.image_processor.generate_crop_boxes(
            A ,		A ,		A ,		A ,		A ,		A       )
        _SCREAMING_SNAKE_CASE									=			self.image_processor(images=A ,		return_tensors="""pt"""       )

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

        _SCREAMING_SNAKE_CASE									=			grid_points.shape[1]
        _SCREAMING_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 ,		A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			grid_points[:, i : i + points_per_batch, :, :]
            _SCREAMING_SNAKE_CASE									=			input_labels[:, i : i + points_per_batch]
            _SCREAMING_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 			snake_case_(		self ,		A ,		A=0.88 ,		A=0.95 ,		A=0 ,		A=1 ,		)		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			model_inputs.pop("""input_boxes"""       )
        _SCREAMING_SNAKE_CASE									=			model_inputs.pop("""is_last"""       )
        _SCREAMING_SNAKE_CASE									=			model_inputs.pop("""original_sizes"""       ).tolist()
        _SCREAMING_SNAKE_CASE									=			model_inputs.pop("""reshaped_input_sizes"""       ).tolist()

        _SCREAMING_SNAKE_CASE									=			self.model(**A       )

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





    def 			snake_case_(		self ,		A ,		A=False ,		A=False ,		A=0.7 ,		)		->   str:
        _SCREAMING_SNAKE_CASE									=			[]
        _SCREAMING_SNAKE_CASE									=			[]
        _SCREAMING_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"""       )       )

        _SCREAMING_SNAKE_CASE									=			torch.cat(A       )
        _SCREAMING_SNAKE_CASE									=			torch.cat(A       )
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.image_processor.post_process_for_mask_generation(
            A ,		A ,		A ,		A       )

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

        _SCREAMING_SNAKE_CASE									=			{}
        if output_rle_mask:
            _SCREAMING_SNAKE_CASE									=			rle_mask

        if output_bboxes_mask:
            _SCREAMING_SNAKE_CASE									=			bounding_boxes

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





import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()






def        a_		(       __lowercase   :	Dict       ,					__lowercase   :	int       ,					__lowercase   :	Optional[Any]=None ) ->  Any:
			# set parameter of one layer
			assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
			_snake_case		=			nn.Parameter(__lowercase )
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
						_snake_case		=			nn.Parameter(__lowercase )






def        a_		(       __lowercase   :	Any       ,					__lowercase   :	Dict       ,					__lowercase   :	Union[str, Any] ) ->  Optional[Any]:
			# set torch weights for 1-to-1 comparison
			_snake_case		=			np.asarray(weights[0] )
			_snake_case		=			np.asarray(weights[1] )
			_snake_case		=			np.asarray(weights[2] )

			set_param(
			    torch_layer.self_attention.query_key       ,					torch.tensor(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.self_attention.value       ,					torch.tensor(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.output.dense       ,					torch.tensor(__lowercase ).view(-1       ,					__lowercase ).contiguous().transpose(0       ,					1 )       ,					)






def        a_		(       __lowercase   :	str       ,					__lowercase   :	Tuple       ,					__lowercase   :	Any ) ->  Optional[Any]:
			# set torch weights for 1-to-1 comparison
			_snake_case		=			np.asarray(weights[0] )
			_snake_case		=			np.asarray(weights[1] )
			_snake_case		=			np.asarray(weights[2] )
			_snake_case		=			np.asarray(weights[3] )

			set_param(
			    torch_layer.self_attention.query       ,					torch.tensor(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.self_attention.key       ,					torch.tensor(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.self_attention.value       ,					torch.tensor(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.output.dense       ,					torch.tensor(__lowercase ).view(-1       ,					__lowercase ).contiguous().transpose(0       ,					1 )       ,					)






def        a_		(       __lowercase   :	Dict       ,					__lowercase   :	List[str]       ,					__lowercase   :	Union[str, Any] ) ->  Optional[Any]:
			# layernorm 1
			_snake_case		=			weights[0][0][0]
			_snake_case		=			np.asarray(layer_norm_a[0] )
			_snake_case		=			np.asarray(layer_norm_a[1] )
			set_param(
			    torch_block.attention.layer_norm       ,					torch.tensor(__lowercase )       ,					torch.tensor(__lowercase )       ,					)

			# lsh weights + output
			_snake_case		=			weights[0][1]
			if len(__lowercase ) < 4:
						set_layer_weights_in_torch_lsh(__lowercase       ,					torch_block.attention       ,					__lowercase )
			else:
						set_layer_weights_in_torch_local(__lowercase       ,					torch_block.attention       ,					__lowercase )

			# intermediate weighs
			_snake_case		=			weights[2][0][1][2]

			# Chunked Feed Forward
			if len(__lowercase ) == 4:
						_snake_case		=			intermediate_weights[2]

			# layernorm 2
			_snake_case		=			np.asarray(intermediate_weights[0][0] )
			_snake_case		=			np.asarray(intermediate_weights[0][1] )
			set_param(
			    torch_block.feed_forward.layer_norm       ,					torch.tensor(__lowercase )       ,					torch.tensor(__lowercase )       ,					)

			# intermediate dense
			_snake_case		=			np.asarray(intermediate_weights[1][0] )
			_snake_case		=			np.asarray(intermediate_weights[1][1] )
			set_param(
			    torch_block.feed_forward.dense.dense       ,					torch.tensor(__lowercase ).transpose(0       ,					1 ).contiguous()       ,					torch.tensor(__lowercase )       ,					)

			# intermediate out
			_snake_case		=			np.asarray(intermediate_weights[4][0] )
			_snake_case		=			np.asarray(intermediate_weights[4][1] )
			set_param(
			    torch_block.feed_forward.output.dense       ,					torch.tensor(__lowercase ).transpose(0       ,					1 ).contiguous()       ,					torch.tensor(__lowercase )       ,					)






def        a_		(       __lowercase   :	Tuple       ,					__lowercase   :	Tuple       ,					__lowercase   :	Dict ) ->  Optional[int]:
			# reformer model
			_snake_case		=			torch_model.reformer

			# word embeds
			_snake_case		=			np.asarray(weights[1] )
			set_param(
			    torch_model_reformer.embeddings.word_embeddings       ,					torch.tensor(__lowercase )       ,					)

			if isinstance(weights[3]       ,					__lowercase ):
						_snake_case		=			torch_model_reformer.embeddings.position_embeddings
						for emb_idx in range(len(position_embeddings.weights ) ):
									_snake_case		=			np.asarray(weights[3][emb_idx][0] )
									assert (
									    position_embeddings.weights[emb_idx].shape == emb_weights.shape
									), f'''{position_embeddings[emb_idx]} emb does not match'''
									_snake_case		=			nn.Parameter(torch.tensor(__lowercase ) )

			_snake_case		=			weights[5]
			assert len(torch_model_reformer.encoder.layers ) * 4 == len(
			    __lowercase ), "HF and trax model do not have the same number of layers"
			for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
						_snake_case		=			trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
						set_block_weights_in_torch(__lowercase       ,					__lowercase       ,					__lowercase )

			# output layer norm
			_snake_case		=			np.asarray(weights[7][0] )
			_snake_case		=			np.asarray(weights[7][1] )
			set_param(
			    torch_model_reformer.encoder.layer_norm       ,					torch.tensor(__lowercase )       ,					torch.tensor(__lowercase )       ,					)

			# output embeddings
			_snake_case		=			np.asarray(weights[9][0] )
			_snake_case		=			np.asarray(weights[9][1] )
			set_param(
			    torch_model.lm_head.decoder       ,					torch.tensor(__lowercase ).transpose(0       ,					1 ).contiguous()       ,					torch.tensor(__lowercase )       ,					)






def        a_		(       __lowercase   :	Optional[Any]       ,					__lowercase   :	Dict       ,					__lowercase   :	List[Any] ) ->  Optional[int]:
			# Initialise PyTorch model
			_snake_case		=			ReformerConfig.from_json_file(__lowercase )
			print(f'''Building PyTorch model from configuration: {config}''' )
			_snake_case		=			ReformerModelWithLMHead(__lowercase )

			with open(__lowercase       ,					'rb' ) as f:
						_snake_case		=			pickle.load(__lowercase )['weights']

			set_model_weights_in_torch(__lowercase       ,					__lowercase       ,					config.hidden_size )

			# Save pytorch-model
			print(f'''Save PyTorch model to {pytorch_dump_path}''' )
			torch.save(model.state_dict()       ,					__lowercase )


if __name__ == "__main__":
			_lowerCamelCase       :				Union[str, Any]       =			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained Reformer model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			_lowerCamelCase       :				List[Any]       =			parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)