

import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


__snake_case							:	str											=       [
    # tf -> hf
    ("/", "."),
    ("layer_", "layers."),
    ("kernel", "weight"),
    ("beta", "bias"),
    ("gamma", "weight"),
    ("pegasus", "model"),
]
__snake_case							:	List[Any]											=       [
    (".output.dense", ".fc2"),
    ("intermediate.LayerNorm", "final_layer_norm"),
    ("intermediate.dense", "fc1"),
]

__snake_case							:	str											=       (
    INIT_COMMON
    + [
        ("attention.self.LayerNorm", "self_attn_layer_norm"),
        ("attention.output.dense", "self_attn.out_proj"),
        ("attention.self", "self_attn"),
        ("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
        ("attention.encdec_output.dense", "encoder_attn.out_proj"),
        ("attention.encdec", "encoder_attn"),
        ("key", "k_proj"),
        ("value", "v_proj"),
        ("query", "q_proj"),
        ("decoder.LayerNorm", "decoder.layernorm_embedding"),
    ]
    + END_COMMON
)

__snake_case							:	Dict											=       (
    INIT_COMMON
    + [
        ("embeddings.word_embeddings", "shared.weight"),
        ("embeddings.position_embeddings", "embed_positions.weight"),
        ("attention.self.LayerNorm", "self_attn_layer_norm"),
        ("attention.output.dense", "self_attn.output"),
        ("attention.self", "self_attn.self"),
        ("encoder.LayerNorm", "encoder.layernorm_embedding"),
    ]
    + END_COMMON
)

__snake_case							:	Union[str, Any]											=       [
    "encdec/key/bias",
    "encdec/query/bias",
    "encdec/value/bias",
    "self/key/bias",
    "self/query/bias",
    "self/value/bias",
    "encdec_output/dense/bias",
    "attention/output/dense/bias",
]


def  _UpperCAmelCase							(		a__   ,     a__):

			'''simple docstring'''



			for tf_name, hf_name in patterns:
						a_		:		List[Any]      				=      k.replace(a__   ,     a__)
			return k


def  _UpperCAmelCase							(		a__   ,     a__):

			'''simple docstring'''



			a_		:		str      				=      BigBirdPegasusConfig(**a__)
			a_		:		Union[str, Any]      				=      BigBirdPegasusForConditionalGeneration(a__)
			a_		:		List[Any]      				=      torch_model.state_dict()
			a_		:		Optional[int]      				=      {}

			# separating decoder weights
			a_		:		List[str]      				=      {k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder""")}
			a_		:		int      				=      {k: tf_weights[k] for k in tf_weights if not k.startswith("""pegasus/decoder""")}

			for k, v in tqdm(decoder_weights.items()   ,     """tf -> hf conversion"""):
						a_		:		int      				=      [k.endswith(a__) for ending in KEYS_TO_IGNORE]
						if any(a__):
									continue
						a_		:		Optional[Any]      				=      DECODER_PATTERNS
						a_		:		Tuple      				=      rename_state_dict_key(a__   ,     a__)
						if new_k not in state_dict:
									raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})''')
						if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]):
									a_		:		Optional[Any]      				=      v.T
						a_		:		Dict      				=      torch.from_numpy(a__)
						assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

			for k, v in tqdm(remaining_weights.items()   ,     """tf -> hf conversion"""):
						a_		:		Any      				=      [k.endswith(a__) for ending in KEYS_TO_IGNORE]
						if any(a__):
									continue
						a_		:		List[str]      				=      REMAINING_PATTERNS
						a_		:		Union[str, Any]      				=      rename_state_dict_key(a__   ,     a__)
						if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
									raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})''')
						if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]):
									a_		:		List[Any]      				=      v.T
						a_		:		Tuple      				=      torch.from_numpy(a__)
						if k != "pegasus/embeddings/position_embeddings":
									assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

			a_		:		Union[str, Any]      				=      mapping["""model.embed_positions.weight"""]
			a_		:		Tuple      				=      mapping.pop("""model.embed_positions.weight""")
			a_				, a_		:		Tuple      				=      torch_model.load_state_dict(a__   ,     strict=a__)
			a_		:		Dict      				=      [
			    k
			    for k in missing
			    if k
			    not in [
			        """final_logits_bias""",
			        """model.encoder.embed_tokens.weight""",
			        """model.decoder.embed_tokens.weight""",
			        """lm_head.weight""",
			    ]
			]
			assert unexpected_missing == [], f'''no matches found for the following torch keys {unexpected_missing}'''
			assert extra == [], f'''no matches found for the following tf keys {extra}'''
			return torch_model


def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



			a_		:		Union[str, Any]      				=      tf.train.list_variables(a__)
			a_		:		Any      				=      {}
			a_		:		Optional[int]      				=      ["""global_step"""]
			for name, shape in tqdm(a__   ,     desc="""converting tf checkpoint to dict"""):
						a_		:		Optional[int]      				=      any(pat in name for pat in ignore_name)
						if skip_key:
									continue
						a_		:		Optional[Any]      				=      tf.train.load_variable(a__   ,     a__)
						a_		:		Dict      				=      array
			return tf_weights


def  _UpperCAmelCase							(		a__   ,     a__   ,     a__):

			'''simple docstring'''



			a_		:		Union[str, Any]      				=      get_tf_weights_as_numpy(a__)
			a_		:		Dict      				=      convert_bigbird_pegasus(a__   ,     a__)
			torch_model.save_pretrained(a__)


if __name__ == "__main__":
	__snake_case							:	List[str]											=       argparse.ArgumentParser()
	parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
	parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
	__snake_case							:	Optional[Any]											=       parser.parse_args()
	__snake_case							:	int											=       {}
	convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







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: Any      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       **lowerCamelCase_		):
						"""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      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def __call__(self		,       lowerCamelCase_		,       *lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       **lowerCamelCase_		):
						"""simple docstring"""

						return super().__call__(lowerCamelCase_		,       *lowerCamelCase_		,       num_workers=lowerCamelCase_		,       batch_size=lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=64		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = 512 / 1500		,       lowerCamelCase_ = 32		,       lowerCamelCase_ = 1		,       ):
						"""simple docstring"""

						a					     =   load_image(lowerCamelCase_		)
						a					     =   self.image_processor.size["longest_edge"]
						a     ,				a     ,				a     ,				a					     =   self.image_processor.generate_crop_boxes(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors="pt"		)

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

						a					     =   grid_points.shape[1]
						a					     =   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_		):
											a					     =   grid_points[:, i : i + points_per_batch, :, :]
											a					     =   input_labels[:, i : i + points_per_batch]
											a					     =   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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=0.88		,       lowerCamelCase_=0.95		,       lowerCamelCase_=0		,       lowerCamelCase_=1		,       ):
						"""simple docstring"""

						a					     =   model_inputs.pop("input_boxes"		)
						a					     =   model_inputs.pop("is_last"		)
						a					     =   model_inputs.pop("original_sizes"		).tolist()
						a					     =   model_inputs.pop("reshaped_input_sizes"		).tolist()

						a					     =   self.model(**lowerCamelCase_		)

						# post processing happens here in order to avoid CPU GPU copies of ALL the masks
						a					     =   model_outputs["pred_masks"]
						a					     =   self.image_processor.post_process_masks(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       binarize=lowerCamelCase_		)
						a					     =   model_outputs["iou_scores"]
						a     ,				a     ,				a					     =   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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       lowerCamelCase_=0.7		,       ):
						"""simple docstring"""

						a					     =   []
						a					     =   []
						a					     =   []
						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"		)		)

						a					     =   torch.cat(lowerCamelCase_		)
						a					     =   torch.cat(lowerCamelCase_		)
						a     ,				a     ,				a     ,				a					     =   self.image_processor.post_process_for_mask_generation(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

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

						a					     =   {}
						if output_rle_mask:
											a					     =   rle_mask

						if output_bboxes_mask:
											a					     =   bounding_boxes

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