
"""simple docstring"""






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 						_snake_case       (	lowercase__		:  Dict      ,     lowercase__		:  Dict      ,     lowercase__		:  str      ,     lowercase__		:  Tuple="attention"		)  ->				str:


			'''simple docstring'''





			lowerCAmelCase_    :Tuple			     =  params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
			lowerCAmelCase_    :Union[str, Any]			     =  params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
			lowerCAmelCase_    :Any			     =  params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
			lowerCAmelCase_    :Optional[int]			     =  params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
			return k, o, q, v






def 						_snake_case       (	lowercase__		:  Optional[Any]      ,     lowercase__		:  Any      ,     lowercase__		:  int      ,     lowercase__		:  Any=False		)  ->				int:


			'''simple docstring'''





			if split_mlp_wi:
						lowerCAmelCase_    :Tuple			     =  params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
						lowerCAmelCase_    :List[str]			     =  params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
						lowerCAmelCase_    :Tuple			     =  (wi_a, wi_a)
			else:
						lowerCAmelCase_    :List[Any]			     =  params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

			lowerCAmelCase_    :Dict			     =  params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
			return wi, wo






def 						_snake_case       (	lowercase__		:  Any      ,     lowercase__		:  Dict      ,     lowercase__		:  Union[str, Any]      ,     lowercase__		:  Optional[int]		)  ->				Tuple:


			'''simple docstring'''





			return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]






def 						_snake_case       (	lowercase__		:  dict      ,     *, lowercase__		:  int      ,     lowercase__		:  bool		)  ->				List[Any]:


			'''simple docstring'''





			lowerCAmelCase_    :Tuple			     =  traverse_util.flatten_dict(variables["""target"""]		)
			lowerCAmelCase_    :Tuple			     =  {"""/""".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
			lowerCAmelCase_    :Any			     =  """encoder/layers_0/mlp/wi_0/kernel""" in old
			print("""Split MLP:"""      ,     lowercase__		)

			lowerCAmelCase_    :List[Any]			     =  collections.OrderedDict()

			# Shared embeddings.
			lowerCAmelCase_    :Optional[int]			     =  old["""token_embedder/embedding"""]

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

						# Block i, layer 1 (MLP).
						lowerCAmelCase_    :Dict			     =  tax_layer_norm_lookup(lowercase__      ,     lowercase__      ,     """encoder"""      ,     """pre_mlp_layer_norm"""		)
						lowerCAmelCase_     ,       lowerCAmelCase_    :Any			     =  tax_mlp_lookup(lowercase__      ,     lowercase__      ,     """encoder"""      ,     lowercase__		)
						lowerCAmelCase_    :Union[str, Any]			     =  layer_norm
						if split_mlp_wi:
									lowerCAmelCase_    :List[Any]			     =  wi[0].T
									lowerCAmelCase_    :Dict			     =  wi[1].T
						else:
									lowerCAmelCase_    :int			     =  wi.T
						lowerCAmelCase_    :List[str]			     =  wo.T

			lowerCAmelCase_    :Tuple			     =  old[
			    """encoder/relpos_bias/rel_embedding"""
			].T
			lowerCAmelCase_    :List[str]			     =  old["""encoder/encoder_norm/scale"""]

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

									# Block i, layer 1 (Cross Attention).
									lowerCAmelCase_    :int			     =  tax_layer_norm_lookup(lowercase__      ,     lowercase__      ,     """decoder"""      ,     """pre_cross_attention_layer_norm"""		)
									lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_    :Tuple			     =  tax_attention_lookup(lowercase__      ,     lowercase__      ,     """decoder"""      ,     """encoder_decoder_attention"""		)
									lowerCAmelCase_    :Optional[int]			     =  layer_norm
									lowerCAmelCase_    :str			     =  k.T
									lowerCAmelCase_    :Tuple			     =  o.T
									lowerCAmelCase_    :Any			     =  q.T
									lowerCAmelCase_    :int			     =  v.T

									# Block i, layer 2 (MLP).
									lowerCAmelCase_    :Any			     =  tax_layer_norm_lookup(lowercase__      ,     lowercase__      ,     """decoder"""      ,     """pre_mlp_layer_norm"""		)
									lowerCAmelCase_     ,       lowerCAmelCase_    :Dict			     =  tax_mlp_lookup(lowercase__      ,     lowercase__      ,     """decoder"""      ,     lowercase__		)
									lowerCAmelCase_    :List[Any]			     =  layer_norm
									if split_mlp_wi:
												lowerCAmelCase_    :Any			     =  wi[0].T
												lowerCAmelCase_    :Any			     =  wi[1].T
									else:
												lowerCAmelCase_    :Tuple			     =  wi.T
									lowerCAmelCase_    :List[str]			     =  wo.T

						lowerCAmelCase_    :Optional[Any]			     =  old["""decoder/decoder_norm/scale"""]
						lowerCAmelCase_    :Optional[Any]			     =  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_    :Tuple			     =  old["""decoder/logits_dense/kernel"""].T

			return new






def 						_snake_case       (	lowercase__		:  Union[str, Any]      ,     lowercase__		:  bool		)  ->				Union[str, Any]:


			'''simple docstring'''





			lowerCAmelCase_    :Optional[int]			     =  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_    :Optional[int]			     =  state_dict["""shared.weight"""]

			if not is_encoder_only:
						if "decoder.embed_tokens.weight" not in state_dict:
									lowerCAmelCase_    :Tuple			     =  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_    :Any			     =  state_dict["""shared.weight"""]

			return state_dict






def 						_snake_case       (	lowercase__		:  Union[str, Any]      ,     lowercase__		:  str      ,     lowercase__		:  List[Any]      ,     lowercase__		:  Optional[Any]		)  ->				Optional[int]:


			'''simple docstring'''





			lowerCAmelCase_    :List[Any]			     =  checkpoints.load_tax_checkpoint(lowercase__		)
			lowerCAmelCase_    :Optional[int]			     =  convert_tax_to_pytorch(lowercase__      ,     num_layers=config.num_layers      ,     is_encoder_only=lowercase__		)
			lowerCAmelCase_    :Union[str, Any]			     =  make_state_dict(lowercase__      ,     lowercase__		)
			model.load_state_dict(lowercase__      ,     strict=lowercase__		)






def 						_snake_case       (	lowercase__		:  List[Any]      ,     lowercase__		:  Optional[Any]      ,     lowercase__		:  str      ,     lowercase__		:  bool = False		)  ->				Any:


			'''simple docstring'''





			lowerCAmelCase_    :Any			     =  TaConfig.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:
						lowerCAmelCase_    :List[Any]			     =  TaEncoderModel(lowercase__		)
			else:
						lowerCAmelCase_    :List[str]			     =  TaForConditionalGeneration(lowercase__		)

			# Load weights from tf checkpoint
			load_tax_weights_in_ta(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
				)
				__UpperCAmelCase       =  parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
				)








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


__a  			=					logging.get_logger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Union[str, Any]         =	'upernet'
	def __init__(       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Dict=None       ,							SCREAMING_SNAKE_CASE_  :			str=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.02       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=[1, 2, 3, 6]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=True       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.4       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=3_8_4       ,							SCREAMING_SNAKE_CASE_  :			str=2_5_6       ,							SCREAMING_SNAKE_CASE_  :			Dict=1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=False       ,							SCREAMING_SNAKE_CASE_  :			str=2_5_5       ,							**SCREAMING_SNAKE_CASE_  :			str       ,							)       ->     int:
					super().__init__(**SCREAMING_SNAKE_CASE_       )

					if backbone_config is None:
									logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
									lowercase_       =					CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       )
					elif isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									lowercase_       =					backbone_config.get('''model_type'''       )
									lowercase_       =					CONFIG_MAPPING[backbone_model_type]
									lowercase_       =					config_class.from_dict(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					backbone_config
					lowercase_       =					hidden_size
					lowercase_       =					initializer_range
					lowercase_       =					pool_scales
					lowercase_       =					use_auxiliary_head
					lowercase_       =					auxiliary_loss_weight
					lowercase_       =					auxiliary_in_channels
					lowercase_       =					auxiliary_channels
					lowercase_       =					auxiliary_num_convs
					lowercase_       =					auxiliary_concat_input
					lowercase_       =					loss_ignore_index




	def 						_lowercase  (       self  :			List[str]       )       ->     List[str]:
					lowercase_       =					copy.deepcopy(self.__dict__       )
					lowercase_       =					self.backbone_config.to_dict()
					lowercase_       =					self.__class__.model_type
					return output




