



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        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase="attention"						):





							'''simple docstring'''




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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=False						):





							'''simple docstring'''




							if split_mlp_wi:
														_lowerCAmelCase  :							List[str]				     =					params[F"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
														_lowerCAmelCase  :							int				     =					params[F"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
														_lowerCAmelCase  :							Dict				     =					(wi_a, wi_a)
							else:
														_lowerCAmelCase  :							List[Any]				     =					params[F"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

							_lowerCAmelCase  :							Optional[Any]				     =					params[F"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
							return wi, wo






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]






def        A       (      _lowerCamelCase       ,				*, _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase = False						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[str]				     =					traverse_util.flatten_dict(variables["target"]						)
							_lowerCAmelCase  :							List[str]				     =					{"/".join(_lowerCamelCase						): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							_lowerCAmelCase  :							int				     =					"encoder/encoder/mlp/wi_0/kernel" in old
							print("Split MLP:"       ,				_lowerCamelCase						)

							_lowerCAmelCase  :							List[str]				     =					collections.OrderedDict()

							# Shared embeddings.
							_lowerCAmelCase  :							Dict				     =					old["token_embedder/embedding"]

							# Encoder.
							for i in range(_lowerCamelCase						):
														# Block i, layer 0 (Self Attention).
														_lowerCAmelCase  :							int				     =					tax_layer_norm_lookup(_lowerCamelCase       ,				_lowerCamelCase       ,				"encoder"       ,				"pre_attention_layer_norm"						)
														_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase  :							Union[str, Any]				     =					tax_attention_lookup(_lowerCamelCase       ,				_lowerCamelCase       ,				"encoder"       ,				"attention"						)
														_lowerCAmelCase  :							Union[str, Any]				     =					layer_norm
														_lowerCAmelCase  :							Optional[Any]				     =					k.T
														_lowerCAmelCase  :							Tuple				     =					o.T
														_lowerCAmelCase  :							Tuple				     =					q.T
														_lowerCAmelCase  :							int				     =					v.T

														# Block i, layer 1 (MLP).
														_lowerCAmelCase  :							Any				     =					tax_layer_norm_lookup(_lowerCamelCase       ,				_lowerCamelCase       ,				"encoder"       ,				"pre_mlp_layer_norm"						)
														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[Any]				     =					tax_mlp_lookup(_lowerCamelCase       ,				_lowerCamelCase       ,				"encoder"       ,				_lowerCamelCase						)
														_lowerCAmelCase  :							Optional[Any]				     =					layer_norm
														if split_mlp_wi:
																					_lowerCAmelCase  :							Dict				     =					wi[0].T
																					_lowerCAmelCase  :							str				     =					wi[1].T
														else:
																					_lowerCAmelCase  :							Optional[int]				     =					wi.T
														_lowerCAmelCase  :							Union[str, Any]				     =					wo.T
														if scalable_attention:
																					# convert the rel_embedding of each layer
																					_lowerCAmelCase  :							Any				     =					tax_relpos_bias_lookup(
																					    _lowerCamelCase       ,				_lowerCamelCase       ,				"encoder"						).T

							_lowerCAmelCase  :							Optional[int]				     =					old["encoder/encoder_norm/scale"]

							if not scalable_attention:
														_lowerCAmelCase  :							Any				     =					tax_relpos_bias_lookup(
														    _lowerCamelCase       ,				0       ,				"encoder"						).T
														_lowerCAmelCase  :							int				     =					tax_relpos_bias_lookup(
														    _lowerCamelCase       ,				0       ,				"decoder"						).T

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

																					# Block i, layer 1 (Cross Attention).
																					_lowerCAmelCase  :							List[str]				     =					tax_layer_norm_lookup(_lowerCamelCase       ,				_lowerCamelCase       ,				"decoder"       ,				"pre_cross_attention_layer_norm"						)
																					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase  :							Optional[Any]				     =					tax_attention_lookup(_lowerCamelCase       ,				_lowerCamelCase       ,				"decoder"       ,				"encoder_decoder_attention"						)
																					_lowerCAmelCase  :							Any				     =					layer_norm
																					_lowerCAmelCase  :							List[str]				     =					k.T
																					_lowerCAmelCase  :							Dict				     =					o.T
																					_lowerCAmelCase  :							Dict				     =					q.T
																					_lowerCAmelCase  :							str				     =					v.T

																					# Block i, layer 2 (MLP).
																					_lowerCAmelCase  :							Optional[int]				     =					tax_layer_norm_lookup(_lowerCamelCase       ,				_lowerCamelCase       ,				"decoder"       ,				"pre_mlp_layer_norm"						)
																					_lowerCAmelCase     ,					_lowerCAmelCase  :							Optional[Any]				     =					tax_mlp_lookup(_lowerCamelCase       ,				_lowerCamelCase       ,				"decoder"       ,				_lowerCamelCase						)
																					_lowerCAmelCase  :							str				     =					layer_norm
																					if split_mlp_wi:
																												_lowerCAmelCase  :							List[Any]				     =					wi[0].T
																												_lowerCAmelCase  :							int				     =					wi[1].T
																					else:
																												_lowerCAmelCase  :							Any				     =					wi.T
																					_lowerCAmelCase  :							Union[str, Any]				     =					wo.T

																					if scalable_attention:
																												# convert the rel_embedding of each layer
																												_lowerCAmelCase  :							int				     =					tax_relpos_bias_lookup(_lowerCamelCase       ,				_lowerCamelCase       ,				"decoder"						).T

														_lowerCAmelCase  :							Optional[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:
																					_lowerCAmelCase  :							List[str]				     =					old["decoder/logits_dense/kernel"].T

							return new






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							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:
														_lowerCAmelCase  :							str				     =					state_dict["shared.weight"]

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					_lowerCAmelCase  :							Optional[Any]				     =					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."						)
																					_lowerCAmelCase  :							Tuple				     =					state_dict["shared.weight"]

							return state_dict






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					checkpoints.load_tax_checkpoint(_lowerCamelCase						)
							_lowerCAmelCase  :							int				     =					convert_tax_to_pytorch(
							    _lowerCamelCase       ,				num_layers=config.num_layers       ,				is_encoder_only=_lowerCamelCase       ,				scalable_attention=_lowerCamelCase						)
							_lowerCAmelCase  :							Any				     =					make_state_dict(_lowerCamelCase       ,				_lowerCamelCase						)
							model.load_state_dict(_lowerCamelCase       ,				strict=_lowerCamelCase						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase = False       ,				_lowerCamelCase = False       ,				):





							'''simple docstring'''




							_lowerCAmelCase  :							List[Any]				     =					MTaConfig.from_json_file(_lowerCamelCase						)
							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:
														_lowerCAmelCase  :							List[Any]				     =					UMTaEncoderModel(_lowerCamelCase						)
							else:
														_lowerCAmelCase  :							Dict				     =					UMTaForConditionalGeneration(_lowerCamelCase						)

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

							# Save pytorch-model
							print(F"Save PyTorch model to {pytorch_dump_path}"						)
							model.save_pretrained(_lowerCamelCase						)

							# Verify that we can load the checkpoint.
							model.from_pretrained(_lowerCamelCase						)
							print("Done"						)


if __name__ == "__main__":
						_snake_case	=						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,
						)
						_snake_case	=						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,
						)





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'data2vec-vision'







							def __init__(     self,    __a=768,    __a=12,    __a=12,    __a=3072,    __a="gelu",    __a=0.0,    __a=0.0,    __a=0.02,    __a=1E-12,    __a=224,    __a=16,    __a=3,    __a=False,    __a=False,    __a=False,    __a=False,    __a=0.1,    __a=0.1,    __a=True,    __a=[3, 5, 7, 11],    __a=[1, 2, 3, 6],    __a=True,    __a=0.4,    __a=256,    __a=1,    __a=False,    __a=255,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase  :							Dict				     =					hidden_size
														_lowerCAmelCase  :							List[Any]				     =					num_hidden_layers
														_lowerCAmelCase  :							Any				     =					num_attention_heads
														_lowerCAmelCase  :							str				     =					intermediate_size
														_lowerCAmelCase  :							Optional[Any]				     =					hidden_act
														_lowerCAmelCase  :							int				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Dict				     =					attention_probs_dropout_prob
														_lowerCAmelCase  :							Dict				     =					initializer_range
														_lowerCAmelCase  :							List[str]				     =					layer_norm_eps

														_lowerCAmelCase  :							Optional[int]				     =					image_size
														_lowerCAmelCase  :							List[Any]				     =					patch_size
														_lowerCAmelCase  :							Optional[Any]				     =					num_channels
														_lowerCAmelCase  :							str				     =					use_mask_token
														_lowerCAmelCase  :							List[str]				     =					use_absolute_position_embeddings
														_lowerCAmelCase  :							str				     =					use_relative_position_bias
														_lowerCAmelCase  :							List[str]				     =					use_shared_relative_position_bias
														_lowerCAmelCase  :							List[str]				     =					layer_scale_init_value
														_lowerCAmelCase  :							List[Any]				     =					drop_path_rate
														_lowerCAmelCase  :							Union[str, Any]				     =					use_mean_pooling
														# decode head attributes (semantic segmentation)
														_lowerCAmelCase  :							Tuple				     =					out_indices
														_lowerCAmelCase  :							Tuple				     =					pool_scales
														# auxiliary head attributes (semantic segmentation)
														_lowerCAmelCase  :							Optional[int]				     =					use_auxiliary_head
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_loss_weight
														_lowerCAmelCase  :							int				     =					auxiliary_channels
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_num_convs
														_lowerCAmelCase  :							int				     =					auxiliary_concat_input
														_lowerCAmelCase  :							Dict				     =					semantic_loss_ignore_index



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	version.parse('1.11')







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ])







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return 1E-4
