




from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
			import torch

if is_tf_available():
			import tensorflow as tf







class a (     __A  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[str]       	=		['image_processor']
						UpperCamelCase_	:       List[str]       	=		'SamImageProcessor'






						def __init__( self :     Any				,							lowerCamelCase__ :     str				)    ->						Union[str, Any]:
													"""simple docstring"""


													super().__init__(lowerCamelCase__				)
													__lowercase      =    self.image_processor
													__lowercase      =    -10
													__lowercase      =    self.image_processor.size['longest_edge']






						def __call__( self :     Tuple				,							lowerCamelCase__ :     Dict=None				,							lowerCamelCase__ :     Optional[Any]=None				,							lowerCamelCase__ :     List[Any]=None				,							lowerCamelCase__ :     List[str]=None				,							lowerCamelCase__ :     Dict = None				,							**lowerCamelCase__ :     int				,							)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    self.image_processor(
													    lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							**lowerCamelCase__				,							)

													# pop arguments that are not used in the foward but used nevertheless
													__lowercase      =    encoding_image_processor['original_sizes']

													if hasattr(lowerCamelCase__				,							'''numpy'''				):  # Checks if Torch or TF tensor
																				__lowercase      =    original_sizes.numpy()

													__lowercase      =    self._check_and_preprocess_points(
													    input_points=lowerCamelCase__				,							input_labels=lowerCamelCase__				,							input_boxes=lowerCamelCase__				,							)

													__lowercase      =    self._normalize_and_convert(
													    lowerCamelCase__				,							lowerCamelCase__				,							input_points=lowerCamelCase__				,							input_labels=lowerCamelCase__				,							input_boxes=lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							)

													return encoding_image_processor






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     str=None				,							lowerCamelCase__ :     str=None				,							lowerCamelCase__ :     Optional[int]=None				,							lowerCamelCase__ :     List[Any]="pt"				,							)    ->						Optional[Any]:
													"""simple docstring"""


													if input_points is not None:
																				if len(lowerCamelCase__				) != len(lowerCamelCase__				):
																											__lowercase      =    [
																											    self._normalize_coordinates(self.target_size				,							lowerCamelCase__				,							original_sizes[0]				) for point in input_points
																											]
																				else:
																											__lowercase      =    [
																											    self._normalize_coordinates(self.target_size				,							lowerCamelCase__				,							lowerCamelCase__				)
																											    for point, original_size in zip(lowerCamelCase__				,							lowerCamelCase__				)
																											]
																				# check that all arrays have the same shape
																				if not all(point.shape == input_points[0].shape for point in input_points				):
																											if input_labels is not None:
																																		__lowercase      =    self._pad_points_and_labels(lowerCamelCase__				,							lowerCamelCase__				)

																				__lowercase      =    np.array(lowerCamelCase__				)

													if input_labels is not None:
																				__lowercase      =    np.array(lowerCamelCase__				)

													if input_boxes is not None:
																				if len(lowerCamelCase__				) != len(lowerCamelCase__				):
																											__lowercase      =    [
																											    self._normalize_coordinates(self.target_size				,							lowerCamelCase__				,							original_sizes[0]				,							is_bounding_box=lowerCamelCase__				)
																											    for box in input_boxes
																											]
																				else:
																											__lowercase      =    [
																											    self._normalize_coordinates(self.target_size				,							lowerCamelCase__				,							lowerCamelCase__				,							is_bounding_box=lowerCamelCase__				)
																											    for box, original_size in zip(lowerCamelCase__				,							lowerCamelCase__				)
																											]
																				__lowercase      =    np.array(lowerCamelCase__				)

													if input_boxes is not None:
																				if return_tensors == "pt":
																											__lowercase      =    torch.from_numpy(lowerCamelCase__				)
																											# boxes batch size of 1 by default
																											__lowercase      =    input_boxes.unsqueeze(1				) if len(input_boxes.shape				) != 3 else input_boxes
																				elif return_tensors == "tf":
																											__lowercase      =    tf.convert_to_tensor(lowerCamelCase__				)
																											# boxes batch size of 1 by default
																											__lowercase      =    tf.expand_dims(lowerCamelCase__				,							1				) if len(input_boxes.shape				) != 3 else input_boxes
																				encoding_image_processor.update({'''input_boxes''': input_boxes}				)
													if input_points is not None:
																				if return_tensors == "pt":
																											__lowercase      =    torch.from_numpy(lowerCamelCase__				)
																											# point batch size of 1 by default
																											__lowercase      =    input_points.unsqueeze(1				) if len(input_points.shape				) != 4 else input_points
																				elif return_tensors == "tf":
																											__lowercase      =    tf.convert_to_tensor(lowerCamelCase__				)
																											# point batch size of 1 by default
																											__lowercase      =    tf.expand_dims(lowerCamelCase__				,							1				) if len(input_points.shape				) != 4 else input_points
																				encoding_image_processor.update({'''input_points''': input_points}				)
													if input_labels is not None:
																				if return_tensors == "pt":
																											__lowercase      =    torch.from_numpy(lowerCamelCase__				)
																											# point batch size of 1 by default
																											__lowercase      =    input_labels.unsqueeze(1				) if len(input_labels.shape				) != 3 else input_labels
																				elif return_tensors == "tf":
																											__lowercase      =    tf.convert_to_tensor(lowerCamelCase__				)
																											# point batch size of 1 by default
																											__lowercase      =    tf.expand_dims(lowerCamelCase__				,							1				) if len(input_labels.shape				) != 3 else input_labels
																				encoding_image_processor.update({'''input_labels''': input_labels}				)

													return encoding_image_processor






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     Union[str, Any]				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    max([point.shape[0] for point in input_points]				)
													__lowercase      =    []
													for i, point in enumerate(lowerCamelCase__				):
																				if point.shape[0] != expected_nb_points:
																											__lowercase      =    np.concatenate(
																											    [point, np.zeros((expected_nb_points - point.shape[0], 2)				) + self.point_pad_value]				,							axis=0				)
																											__lowercase      =    np.append(input_labels[i]				,							[self.point_pad_value]				)
																				processed_input_points.append(lowerCamelCase__				)
													__lowercase      =    processed_input_points
													return input_points, input_labels






						def     UpperCAmelCase_      ( self :     List[Any]				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     str=False				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    original_size
													__lowercase      =    self.image_processor._get_preprocess_shape(lowerCamelCase__				,							longest_edge=lowerCamelCase__				)
													__lowercase      =    deepcopy(lowerCamelCase__				).astype(lowerCamelCase__				)

													if is_bounding_box:
																				__lowercase      =    coords.reshape(-1				,							2				,							2				)

													__lowercase      =    coords[..., 0] * (new_w / old_w)
													__lowercase      =    coords[..., 1] * (new_h / old_h)

													if is_bounding_box:
																				__lowercase      =    coords.reshape(-1				,							4				)

													return coords






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     int=None				,							lowerCamelCase__ :     Tuple=None				,							lowerCamelCase__ :     Optional[int]=None				,							)    ->						Optional[Any]:
													"""simple docstring"""


													if input_points is not None:
																				if hasattr(lowerCamelCase__				,							'''numpy'''				):  # Checks for TF or Torch tensor
																											__lowercase      =    input_points.numpy().tolist()

																				if not isinstance(lowerCamelCase__				,							lowerCamelCase__				) or not isinstance(input_points[0]				,							lowerCamelCase__				):
																											raise ValueError('''Input points must be a list of list of floating points.'''				)
																				__lowercase      =    [np.array(lowerCamelCase__				) for input_point in input_points]
													else:
																				__lowercase      =    None

													if input_labels is not None:
																				if hasattr(lowerCamelCase__				,							'''numpy'''				):
																											__lowercase      =    input_labels.numpy().tolist()

																				if not isinstance(lowerCamelCase__				,							lowerCamelCase__				) or not isinstance(input_labels[0]				,							lowerCamelCase__				):
																											raise ValueError('''Input labels must be a list of list integers.'''				)
																				__lowercase      =    [np.array(lowerCamelCase__				) for label in input_labels]
													else:
																				__lowercase      =    None

													if input_boxes is not None:
																				if hasattr(lowerCamelCase__				,							'''numpy'''				):
																											__lowercase      =    input_boxes.numpy().tolist()

																				if (
																				    not isinstance(lowerCamelCase__				,							lowerCamelCase__				)
																				    or not isinstance(input_boxes[0]				,							lowerCamelCase__				)
																				    or not isinstance(input_boxes[0][0]				,							lowerCamelCase__				)
																				):
																											raise ValueError('''Input boxes must be a list of list of list of floating points.'''				)
																				__lowercase      =    [np.array(lowerCamelCase__				).astype(np.floataa				) for box in input_boxes]
													else:
																				__lowercase      =    None

													return input_points, input_labels, input_boxes






						@property
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						str:
													"""simple docstring"""


													__lowercase      =    self.image_processor.model_input_names
													return list(dict.fromkeys(lowerCamelCase__				)				)






						def     UpperCAmelCase_      ( self :     int				,							*lowerCamelCase__ :     Dict				,							**lowerCamelCase__ :     Tuple				)    ->						List[str]:
													"""simple docstring"""


													return self.image_processor.post_process_masks(*lowerCamelCase__				,							**lowerCamelCase__				)












import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def 		_A(       UpperCamelCase__							:			Tuple				)    -> Tuple:
							'''simple docstring'''


							__lowercase      =    checkpoints.load_tax_checkpoint(UpperCamelCase__				)
							__lowercase      =    flatten_dict(UpperCamelCase__				)
							return flax_params







def 		_A(       UpperCamelCase__							:			str				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    {}

							__lowercase      =    {
							    '''token_embedder''': '''embeddings''',
							    '''encoder_norm''': '''layernorm''',
							    '''kernel''': '''weight''',
							    '''.out''': '''.output''',
							    '''scale''': '''weight''',
							    '''embedders_0.pos_embedding''': '''row_embedder.weight''',
							    '''embedders_1.pos_embedding''': '''column_embedder.weight''',
							}

							__lowercase      =    {
							    '''query''': '''attention.query''',
							    '''key''': '''attention.key''',
							    '''value''': '''attention.value''',
							    '''output.dense''': '''output''',
							    '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
							    '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
							    '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
							    '''mlp.''': '''mlp.DenseReluDense.''',
							    '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
							    '''self_attention.o''': '''self_attention.attention.o''',
							    '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
							    '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
							    '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
							    '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
							}

							for key in flax_dict.keys():
														if "target" in key:
																					# remove the first prefix from the key
																					__lowercase      =    '''.'''.join(key[1:]				)

																					# rename the key
																					for old, new in CONVERSION_MAPPING.items():
																												__lowercase      =    new_key.replace(UpperCamelCase__				,       UpperCamelCase__				)

																					if "decoder" in new_key:
																												for old, new in DECODER_CONVERSION_MAPPING.items():
																																			__lowercase      =    new_key.replace(UpperCamelCase__				,       UpperCamelCase__				)

																					if "layers" in new_key and "decoder" not in new_key:
																												# use regex to replace the layer number
																												__lowercase      =    re.sub(r'''layers_(\d+)'''				,       r'''layer.\1'''				,       UpperCamelCase__				)
																												__lowercase      =    new_key.replace('''encoder'''				,       '''encoder.encoder'''				)

																					elif "layers" in new_key and "decoder" in new_key:
																												# use regex to replace the layer number
																												__lowercase      =    re.sub(r'''layers_(\d+)'''				,       r'''layer.\1'''				,       UpperCamelCase__				)

																					__lowercase      =    flax_dict[key]

							__lowercase      =    {}
							# convert converted_dict into torch format
							for key in converted_dict.keys():
														if ("embed_tokens" not in key) and ("embedder" not in key):
																					__lowercase      =    torch.from_numpy(converted_dict[key].T				)
														else:
																					__lowercase      =    torch.from_numpy(converted_dict[key]				)

							return converted_torch_dict







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			List[str]=False				,       UpperCamelCase__							:			List[str]=False				)    -> Any:
							'''simple docstring'''


							__lowercase      =    get_flax_param(UpperCamelCase__				)

							if not use_large:
														__lowercase      =    PixaStructVisionConfig()
														__lowercase      =    PixaStructTextConfig()
							else:
														__lowercase      =    PixaStructVisionConfig(
														    hidden_size=1536				,       d_ff=3968				,       num_attention_heads=24				,       num_hidden_layers=18				)
														__lowercase      =    PixaStructTextConfig(hidden_size=1536				,       d_ff=3968				,       num_heads=24				,       num_layers=18				)
							__lowercase      =    PixaStructConfig(
							    vision_config=encoder_config.to_dict()				,       text_config=decoder_config.to_dict()				,       is_vqa=UpperCamelCase__				)

							__lowercase      =    PixaStructForConditionalGeneration(UpperCamelCase__				)

							__lowercase      =    rename_and_convert_flax_params(UpperCamelCase__				)
							model.load_state_dict(UpperCamelCase__				)

							__lowercase      =    AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''				)
							__lowercase      =    PixaStructImageProcessor()
							__lowercase      =    PixaStructProcessor(image_processor=UpperCamelCase__				,       tokenizer=UpperCamelCase__				)

							if use_large:
														__lowercase      =    4096

							__lowercase      =    True

							# mkdir if needed
							os.makedirs(UpperCamelCase__				,       exist_ok=UpperCamelCase__				)

							model.save_pretrained(UpperCamelCase__				)
							processor.save_pretrained(UpperCamelCase__				)

							print('''Model saved in {}'''.format(UpperCamelCase__				)				)


if __name__ == "__main__":
			UpperCAmelCase__				     = argparse.ArgumentParser()
			parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--use_large", action="store_true", help="Use large model.")
			parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
			UpperCAmelCase__				     = parser.parse_args()

			convert_pixastruct_original_pytorch_checkpoint_to_hf(
			    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
			)






