import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			Tuple						,__lowercase						:			int     ):
		'''simple docstring'''




		return params[f'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			Optional[Any]						,__lowercase						:			List[str]						,__lowercase						:			Any="attention"     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]     )
		A_		:    List[Any]         =				k_tmp.reshape(k_tmp.shape[0]						,k_tmp.shape[1] * k_tmp.shape[2]     )
		A_		:    Tuple         =				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]     )
		A_		:    List[Any]         =				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,o_tmp.shape[2]     )
		A_		:    Tuple         =				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]     )
		A_		:    Tuple         =				q_tmp.reshape(q_tmp.shape[0]						,q_tmp.shape[1] * q_tmp.shape[2]     )
		A_		:    str         =				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]     )
		A_		:    Dict         =				v_tmp.reshape(v_tmp.shape[0]						,v_tmp.shape[1] * v_tmp.shape[2]     )
		return k, o, q, v




def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			str						,__lowercase						:			Union[str, Any]						,__lowercase						:			List[str]=False     ):
		'''simple docstring'''




		if split_mlp_wi:
				A_		:    List[str]         =				params[f'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
				A_		:    List[Any]         =				params[f'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
				A_		:    Tuple         =				(wi_a, wi_a)
		else:
				A_		:    Optional[int]         =				params[f'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

		A_		:    Any         =				params[f'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
		return wi, wo




def     UpperCamelCase						(	__lowercase						:			List[str]						,__lowercase						:			List[str]						,__lowercase						:			Optional[Any]						,__lowercase						:			Optional[Any]     ):
		'''simple docstring'''




		return params[f'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]




def     UpperCamelCase						(	__lowercase						:			dict						,*, __lowercase						:			int						,__lowercase						:			bool						,__lowercase						:			bool = False     ):
		'''simple docstring'''




		A_		:    List[str]         =				traverse_util.flatten_dict(variables['target']     )
		A_		:    Optional[Any]         =				{'/'.join(__lowercase     ): v for k, v in old.items()}

		# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
		A_		:    Optional[int]         =				'encoder/encoder/mlp/wi_0/kernel' in old
		print('Split MLP:'						,__lowercase     )

		A_		:    Tuple         =				collections.OrderedDict()

		# Shared embeddings.
		A_		:    int         =				old['token_embedder/embedding']

		# Encoder.
		for i in range(__lowercase     ):
				# Block i, layer 0 (Self Attention).
				A_		:    Tuple         =				tax_layer_norm_lookup(__lowercase						,__lowercase						,'encoder'						,'pre_attention_layer_norm'     )
				A_       ,      A_       ,      A_       ,      A_		:    List[str]         =				tax_attention_lookup(__lowercase						,__lowercase						,'encoder'						,'attention'     )
				A_		:    Union[str, Any]         =				layer_norm
				A_		:    Optional[int]         =				k.T
				A_		:    Any         =				o.T
				A_		:    Dict         =				q.T
				A_		:    List[Any]         =				v.T

				# Block i, layer 1 (MLP).
				A_		:    List[str]         =				tax_layer_norm_lookup(__lowercase						,__lowercase						,'encoder'						,'pre_mlp_layer_norm'     )
				A_       ,      A_		:    List[str]         =				tax_mlp_lookup(__lowercase						,__lowercase						,'encoder'						,__lowercase     )
				A_		:    Optional[int]         =				layer_norm
				if split_mlp_wi:
						A_		:    int         =				wi[0].T
						A_		:    Dict         =				wi[1].T
				else:
						A_		:    int         =				wi.T
				A_		:    int         =				wo.T
				if scalable_attention:
						# convert the rel_embedding of each layer
						A_		:    Tuple         =				tax_relpos_bias_lookup(
						    __lowercase						,__lowercase						,'encoder'     ).T

		A_		:    Dict         =				old['encoder/encoder_norm/scale']

		if not scalable_attention:
				A_		:    Tuple         =				tax_relpos_bias_lookup(
				    __lowercase						,0						,'encoder'     ).T
				A_		:    Dict         =				tax_relpos_bias_lookup(
				    __lowercase						,0						,'decoder'     ).T

		if not is_encoder_only:
				# Decoder.
				for i in range(__lowercase     ):
						# Block i, layer 0 (Self Attention).
						A_		:    Union[str, Any]         =				tax_layer_norm_lookup(__lowercase						,__lowercase						,'decoder'						,'pre_self_attention_layer_norm'     )
						A_       ,      A_       ,      A_       ,      A_		:    List[str]         =				tax_attention_lookup(__lowercase						,__lowercase						,'decoder'						,'self_attention'     )
						A_		:    Tuple         =				layer_norm
						A_		:    Optional[int]         =				k.T
						A_		:    Union[str, Any]         =				o.T
						A_		:    List[Any]         =				q.T
						A_		:    List[Any]         =				v.T

						# Block i, layer 1 (Cross Attention).
						A_		:    Tuple         =				tax_layer_norm_lookup(__lowercase						,__lowercase						,'decoder'						,'pre_cross_attention_layer_norm'     )
						A_       ,      A_       ,      A_       ,      A_		:    Dict         =				tax_attention_lookup(__lowercase						,__lowercase						,'decoder'						,'encoder_decoder_attention'     )
						A_		:    List[Any]         =				layer_norm
						A_		:    Dict         =				k.T
						A_		:    Optional[int]         =				o.T
						A_		:    Any         =				q.T
						A_		:    Any         =				v.T

						# Block i, layer 2 (MLP).
						A_		:    Any         =				tax_layer_norm_lookup(__lowercase						,__lowercase						,'decoder'						,'pre_mlp_layer_norm'     )
						A_       ,      A_		:    Dict         =				tax_mlp_lookup(__lowercase						,__lowercase						,'decoder'						,__lowercase     )
						A_		:    Union[str, Any]         =				layer_norm
						if split_mlp_wi:
								A_		:    str         =				wi[0].T
								A_		:    Optional[int]         =				wi[1].T
						else:
								A_		:    Optional[Any]         =				wi.T
						A_		:    Tuple         =				wo.T

						if scalable_attention:
								# convert the rel_embedding of each layer
								A_		:    List[Any]         =				tax_relpos_bias_lookup(__lowercase						,__lowercase						,'decoder'     ).T

				A_		:    List[Any]         =				old['decoder/decoder_norm/scale']

				# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
				if "decoder/logits_dense/kernel" in old:
						A_		:    Optional[Any]         =				old['decoder/logits_dense/kernel'].T

		return new




def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			bool     ):
		'''simple docstring'''




		A_		:    str         =				collections.OrderedDict([(k, torch.from_numpy(v.copy()     )) for (k, v) in converted_params.items()]     )

		# Add what is missing.
		if "encoder.embed_tokens.weight" not in state_dict:
				A_		:    Dict         =				state_dict['shared.weight']

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						A_		:    Optional[int]         =				state_dict['shared.weight']

				if "lm_head.weight" not in state_dict:  # For old 1.0 models.
						print('Using shared word embeddings as lm_head.'     )
						A_		:    Tuple         =				state_dict['shared.weight']

		return state_dict




def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			Optional[Any]						,__lowercase						:			int						,__lowercase						:			int						,__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				checkpoints.load_tax_checkpoint(__lowercase     )
		A_		:    Union[str, Any]         =				convert_tax_to_pytorch(
		    __lowercase						,num_layers=config.num_layers						,is_encoder_only=__lowercase						,scalable_attention=__lowercase     )
		A_		:    Optional[int]         =				make_state_dict(__lowercase						,__lowercase     )
		model.load_state_dict(__lowercase						,strict=__lowercase     )




def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			List[Any]						,__lowercase						:			Union[str, Any]						,__lowercase						:			bool = False						,__lowercase						:			bool = False						,):
		'''simple docstring'''




		A_		:    int         =				MTaConfig.from_json_file(__lowercase     )
		print(f'''Building PyTorch model from configuration: {config}'''     )
		# Non-v1.1 checkpoints could also use T5Model, but this works for all.
		# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
		if is_encoder_only:
				A_		:    str         =				UMTaEncoderModel(__lowercase     )
		else:
				A_		:    Optional[Any]         =				UMTaForConditionalGeneration(__lowercase     )

		# Load weights from tf checkpoint
		load_tax_weights_in_ta(__lowercase						,__lowercase						,__lowercase						,__lowercase						,__lowercase     )

		# Save pytorch-model
		print(f'''Save PyTorch model to {pytorch_dump_path}'''     )
		model.save_pretrained(__lowercase     )

		# Verify that we can load the checkpoint.
		model.from_pretrained(__lowercase     )
		print('Done'     )


if __name__ == "__main__":
	_UpperCAmelCase            =		argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
	# Required parameters
	parser.add_argument(
	    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
	)
	parser.add_argument(
	    """--config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
	)
	parser.add_argument(
	    """--scalable_attention""",
	    action="""store_true""",
	    help="""Whether the model uses scaled attention (umt5 model)""",
	    default=False,
	)
	_UpperCAmelCase            =		parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path,
	    args.config_file,
	    args.pytorch_dump_path,
	    args.is_encoder_only,
	    args.scalable_attention,
	)







import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_UpperCAmelCase            =		logging.get_logger(__name__)





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				def __init__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						warnings.warn(
						    'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
						    ' use DonutImageProcessor instead.'		,     lowercase		,     )
						super().__init__(*lowercase		,     **lowercase )






