



import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Dict     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	Tuple="attention"				)->str:
						_lowerCAmelCase 			=  params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
						_lowerCAmelCase 			=  params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
						_lowerCAmelCase 			=  params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
						_lowerCAmelCase 			=  params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
						return k, o, q, v
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	int     ,				_SCREAMING_SNAKE_CASE   :	int     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]=False				)->Tuple:
						if split_mlp_wi:
												_lowerCAmelCase 			=  params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
												_lowerCAmelCase 			=  params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
												_lowerCAmelCase 			=  (wi_a, wi_a)
						else:
												_lowerCAmelCase 			=  params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

						_lowerCAmelCase 			=  params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
						return wi, wo
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]				)->List[str]:
						return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				*, _SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]				)->List[str]:
						_lowerCAmelCase 			=  traverse_util.flatten_dict(variables['''target''']				)
						_lowerCAmelCase 			=  {'''/'''.join(_SCREAMING_SNAKE_CASE				): v for k, v in old.items()}

						# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
						_lowerCAmelCase 			=  '''encoder/layers_0/mlp/wi_0/kernel''' in old
						print('''Split MLP:'''     ,				_SCREAMING_SNAKE_CASE				)

						_lowerCAmelCase 			=  collections.OrderedDict()

						# Shared embeddings.
						_lowerCAmelCase 			=  old['''token_embedder/embedding''']

						# Encoder.
						for i in range(_SCREAMING_SNAKE_CASE				):
												# Block i, layer 0 (Self Attention).
												_lowerCAmelCase 			=  tax_layer_norm_lookup(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				'''encoder'''     ,				'''pre_attention_layer_norm'''				)
												_lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  tax_attention_lookup(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				'''encoder'''     ,				'''attention'''				)
												_lowerCAmelCase 			=  layer_norm
												_lowerCAmelCase 			=  k.T
												_lowerCAmelCase 			=  o.T
												_lowerCAmelCase 			=  q.T
												_lowerCAmelCase 			=  v.T

												# Block i, layer 1 (MLP).
												_lowerCAmelCase 			=  tax_layer_norm_lookup(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				'''encoder'''     ,				'''pre_mlp_layer_norm'''				)
												_lowerCAmelCase			,   _lowerCAmelCase 			=  tax_mlp_lookup(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				'''encoder'''     ,				_SCREAMING_SNAKE_CASE				)
												_lowerCAmelCase 			=  layer_norm
												if split_mlp_wi:
																		_lowerCAmelCase 			=  wi[0].T
																		_lowerCAmelCase 			=  wi[1].T
												else:
																		_lowerCAmelCase 			=  wi.T
												_lowerCAmelCase 			=  wo.T

						_lowerCAmelCase 			=  old[
						    '''encoder/relpos_bias/rel_embedding'''
						].T
						_lowerCAmelCase 			=  old['''encoder/encoder_norm/scale''']

						if not is_encoder_only:
												# Decoder.
												for i in range(_SCREAMING_SNAKE_CASE				):
																		# Block i, layer 0 (Self Attention).
																		_lowerCAmelCase 			=  tax_layer_norm_lookup(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				'''decoder'''     ,				'''pre_self_attention_layer_norm'''				)
																		_lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  tax_attention_lookup(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				'''decoder'''     ,				'''self_attention'''				)
																		_lowerCAmelCase 			=  layer_norm
																		_lowerCAmelCase 			=  k.T
																		_lowerCAmelCase 			=  o.T
																		_lowerCAmelCase 			=  q.T
																		_lowerCAmelCase 			=  v.T

																		# Block i, layer 1 (Cross Attention).
																		_lowerCAmelCase 			=  tax_layer_norm_lookup(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				'''decoder'''     ,				'''pre_cross_attention_layer_norm'''				)
																		_lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  tax_attention_lookup(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				'''decoder'''     ,				'''encoder_decoder_attention'''				)
																		_lowerCAmelCase 			=  layer_norm
																		_lowerCAmelCase 			=  k.T
																		_lowerCAmelCase 			=  o.T
																		_lowerCAmelCase 			=  q.T
																		_lowerCAmelCase 			=  v.T

																		# Block i, layer 2 (MLP).
																		_lowerCAmelCase 			=  tax_layer_norm_lookup(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				'''decoder'''     ,				'''pre_mlp_layer_norm'''				)
																		_lowerCAmelCase			,   _lowerCAmelCase 			=  tax_mlp_lookup(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				'''decoder'''     ,				_SCREAMING_SNAKE_CASE				)
																		_lowerCAmelCase 			=  layer_norm
																		if split_mlp_wi:
																								_lowerCAmelCase 			=  wi[0].T
																								_lowerCAmelCase 			=  wi[1].T
																		else:
																								_lowerCAmelCase 			=  wi.T
																		_lowerCAmelCase 			=  wo.T

												_lowerCAmelCase 			=  old['''decoder/decoder_norm/scale''']
												_lowerCAmelCase 			=  old[
												    '''decoder/relpos_bias/rel_embedding'''
												].T

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

						return new
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]				)->Union[str, Any]:
						_lowerCAmelCase 			=  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 			=  state_dict['''shared.weight''']

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

						return state_dict
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	Any     ,				_SCREAMING_SNAKE_CASE   :	Any     ,				_SCREAMING_SNAKE_CASE   :	str				)->int:
						_lowerCAmelCase 			=  checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE				)
						_lowerCAmelCase 			=  convert_tax_to_pytorch(_SCREAMING_SNAKE_CASE     ,				num_layers=config.num_layers     ,				is_encoder_only=_SCREAMING_SNAKE_CASE				)
						_lowerCAmelCase 			=  make_state_dict(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
						model.load_state_dict(_SCREAMING_SNAKE_CASE     ,				strict=_SCREAMING_SNAKE_CASE				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Dict = False				)->List[str]:
						_lowerCAmelCase 			=  TaConfig.from_json_file(_SCREAMING_SNAKE_CASE				)
						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 			=  TaEncoderModel(_SCREAMING_SNAKE_CASE				)
						else:
												_lowerCAmelCase 			=  TaForConditionalGeneration(_SCREAMING_SNAKE_CASE				)

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

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

						# Verify that we can load the checkpoint.
						model.from_pretrained(_SCREAMING_SNAKE_CASE				)
						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
				)
				UpperCAmelCase_									=							parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
				)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_									=							{"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["DeiTFeatureExtractor"]
    UpperCAmelCase_									=							["DeiTImageProcessor"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DeiTForImageClassification",
        "DeiTForImageClassificationWithTeacher",
        "DeiTForMaskedImageModeling",
        "DeiTModel",
        "DeiTPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDeiTForImageClassification",
        "TFDeiTForImageClassificationWithTeacher",
        "TFDeiTForMaskedImageModeling",
        "TFDeiTModel",
        "TFDeiTPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_deit import DeiTFeatureExtractor
        from .image_processing_deit import DeiTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deit import (
            DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
            DeiTModel,
            DeiTPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deit import (
            TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
            TFDeiTModel,
            TFDeiTPreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase_									=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)