
"""simple docstring"""







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 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase="attention"	):
	__lowerCAmelCase    :		str		    =   np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]	)
	__lowerCAmelCase    :		Union[str, Any]		    =   k_tmp.reshape(k_tmp.shape[0] ,						k_tmp.shape[1] * k_tmp.shape[2]	)
	__lowerCAmelCase    :		str		    =   np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]	)
	__lowerCAmelCase    :		List[str]		    =   o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,						o_tmp.shape[2]	)
	__lowerCAmelCase    :		Tuple		    =   np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]	)
	__lowerCAmelCase    :		Tuple		    =   q_tmp.reshape(q_tmp.shape[0] ,						q_tmp.shape[1] * q_tmp.shape[2]	)
	__lowerCAmelCase    :		Optional[int]		    =   np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :]	)
	__lowerCAmelCase    :		int		    =   v_tmp.reshape(v_tmp.shape[0] ,						v_tmp.shape[1] * v_tmp.shape[2]	)
	return k, o, q, v
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=False	):
	if split_mlp_wi:
		__lowerCAmelCase    :		List[str]		    =   params[F"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
		__lowerCAmelCase    :		str		    =   params[F"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
		__lowerCAmelCase    :		Tuple		    =   (wi_a, wi_a)
	else:
		__lowerCAmelCase    :		int		    =   params[F"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

	__lowerCAmelCase    :		Union[str, Any]		    =   params[F"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
	return wi, wo
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]
def 					__lowerCAmelCase       (_UpperCamelCase ,						*, _UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase = False	):
	__lowerCAmelCase    :		List[Any]		    =   traverse_util.flatten_dict(variables['target']	)
	__lowerCAmelCase    :		str		    =   {'/'.join(_UpperCamelCase	): v for k, v in old.items()}

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

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

	# Shared embeddings.
	__lowerCAmelCase    :		Optional[Any]		    =   old['token_embedder/embedding']

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

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

	__lowerCAmelCase    :		Tuple		    =   old['encoder/encoder_norm/scale']

	if not scalable_attention:
		__lowerCAmelCase    :		Dict		    =   tax_relpos_bias_lookup(
		    _UpperCamelCase ,						0 ,						'encoder'	).T
		__lowerCAmelCase    :		Union[str, Any]		    =   tax_relpos_bias_lookup(
		    _UpperCamelCase ,						0 ,						'decoder'	).T

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

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

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

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

		__lowerCAmelCase    :		int		    =   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    :		Tuple		    =   old['decoder/logits_dense/kernel'].T

	return new
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__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    :		int		    =   state_dict['shared.weight']

	if not is_encoder_only:
		if "decoder.embed_tokens.weight" not in state_dict:
			__lowerCAmelCase    :		List[str]		    =   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    :		List[Any]		    =   state_dict['shared.weight']

	return state_dict
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[int]		    =   checkpoints.load_tax_checkpoint(_UpperCamelCase	)
	__lowerCAmelCase    :		Union[str, Any]		    =   convert_tax_to_pytorch(
	    _UpperCamelCase ,						num_layers=config.num_layers ,						is_encoder_only=_UpperCamelCase ,						scalable_attention=_UpperCamelCase	)
	__lowerCAmelCase    :		Any		    =   make_state_dict(_UpperCamelCase ,						_UpperCamelCase	)
	model.load_state_dict(_UpperCamelCase ,						strict=_UpperCamelCase	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase = False ,						_UpperCamelCase = False ,						):
	__lowerCAmelCase    :		int		    =   MTaConfig.from_json_file(_UpperCamelCase	)
	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    :		Optional[int]		    =   UMTaEncoderModel(_UpperCamelCase	)
	else:
		__lowerCAmelCase    :		Optional[Any]		    =   UMTaForConditionalGeneration(_UpperCamelCase	)

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

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

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


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

"""simple docstring"""







# Imports
import numpy as np




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			self.set_matricies(red=_SCREAMING_SNAKE_CASE ,	green=_SCREAMING_SNAKE_CASE ,	blue=_SCREAMING_SNAKE_CASE ,	red_edge=_SCREAMING_SNAKE_CASE ,	nir=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			if red is not None:
				__lowerCAmelCase    :		Optional[int]		    =   red
			if green is not None:
				__lowerCAmelCase    :		Any		    =   green
			if blue is not None:
				__lowerCAmelCase    :		str		    =   blue
			if red_edge is not None:
				__lowerCAmelCase    :		Dict		    =   red_edge
			if nir is not None:
				__lowerCAmelCase    :		Dict		    =   nir
			return True

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE="" ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			self.set_matricies(red=_SCREAMING_SNAKE_CASE ,	green=_SCREAMING_SNAKE_CASE ,	blue=_SCREAMING_SNAKE_CASE ,	red_edge=_SCREAMING_SNAKE_CASE ,	nir=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[int]		    =   {
			    'ARVI2': self.arvaa,
			    'CCCI': self.ccci,
			    'CVI': self.cvi,
			    'GLI': self.gli,
			    'NDVI': self.ndvi,
			    'BNDVI': self.bndvi,
			    'redEdgeNDVI': self.red_edge_ndvi,
			    'GNDVI': self.gndvi,
			    'GBNDVI': self.gbndvi,
			    'GRNDVI': self.grndvi,
			    'RBNDVI': self.rbndvi,
			    'PNDVI': self.pndvi,
			    'ATSAVI': self.atsavi,
			    'BWDRVI': self.bwdrvi,
			    'CIgreen': self.ci_green,
			    'CIrededge': self.ci_rededge,
			    'CI': self.ci,
			    'CTVI': self.ctvi,
			    'GDVI': self.gdvi,
			    'EVI': self.evi,
			    'GEMI': self.gemi,
			    'GOSAVI': self.gosavi,
			    'GSAVI': self.gsavi,
			    'Hue': self.hue,
			    'IVI': self.ivi,
			    'IPVI': self.ipvi,
			    'I': self.i,
			    'RVI': self.rvi,
			    'MRVI': self.mrvi,
			    'MSAVI': self.m_savi,
			    'NormG': self.norm_g,
			    'NormNIR': self.norm_nir,
			    'NormR': self.norm_r,
			    'NGRDI': self.ngrdi,
			    'RI': self.ri,
			    'S': self.s,
			    'IF': self._if,
			    'DVI': self.dvi,
			    'TVI': self.tvi,
			    'NDRE': self.ndre,
			}

			try:
				return funcs[index]()
			except KeyError:
				print('Index not in the list!'				)
				return False

		def 							__lowerCamelCase							(   self				):
			return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))

		def 							__lowerCamelCase							(   self				):
			return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
			    (self.nir - self.red) / (self.nir + self.red)
			)

		def 							__lowerCamelCase							(   self				):
			return self.nir * (self.red / (self.green**2))

		def 							__lowerCamelCase							(   self				):
			return (2 * self.green - self.red - self.blue) / (
			    2 * self.green + self.red + self.blue
			)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - self.red) / (self.nir + self.red)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - self.blue) / (self.nir + self.blue)

		def 							__lowerCamelCase							(   self				):
			return (self.redEdge - self.red) / (self.redEdge + self.red)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - self.green) / (self.nir + self.green)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - (self.green + self.blue)) / (
			    self.nir + (self.green + self.blue)
			)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - (self.green + self.red)) / (
			    self.nir + (self.green + self.red)
			)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))

		def 							__lowerCamelCase							(   self				):
			return (self.nir - (self.green + self.red + self.blue)) / (
			    self.nir + (self.green + self.red + self.blue)
			)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=0.08 ,	_SCREAMING_SNAKE_CASE=1.22 ,	_SCREAMING_SNAKE_CASE=0.03				):
			return a * (
			    (self.nir - a * self.red - b)
			    / (a * self.nir + self.red - a * b + x * (1 + a**2))
			)

		def 							__lowerCamelCase							(   self				):
			return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)

		def 							__lowerCamelCase							(   self				):
			return (self.nir / self.green) - 1

		def 							__lowerCamelCase							(   self				):
			return (self.nir / self.redEdge) - 1

		def 							__lowerCamelCase							(   self				):
			return (self.red - self.blue) / self.red

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.ndvi()
			return ((ndvi + 0.5) / (abs(ndvi + 0.5				))) * (abs(ndvi + 0.5				) ** (1 / 2))

		def 							__lowerCamelCase							(   self				):
			return self.nir - self.green

		def 							__lowerCamelCase							(   self				):
			return 2.5 * (
			    (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
			)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
			    self.nir + self.red + 0.5
			)
			return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=0.16				):
			return (self.nir - self.green) / (self.nir + self.green + y)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=0.5				):
			return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)

		def 							__lowerCamelCase							(   self				):
			return np.arctan(
			    ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			return (self.nir - b) / (a * self.red)

		def 							__lowerCamelCase							(   self				):
			return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)

		def 							__lowerCamelCase							(   self				):
			return (self.red + self.green + self.blue) / 30.5

		def 							__lowerCamelCase							(   self				):
			return self.nir / self.red

		def 							__lowerCamelCase							(   self				):
			return (self.rvi() - 1) / (self.rvi() + 1)

		def 							__lowerCamelCase							(   self				):
			return (
			    (2 * self.nir + 1)
			    - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
			) / 2

		def 							__lowerCamelCase							(   self				):
			return self.green / (self.nir + self.red + self.green)

		def 							__lowerCamelCase							(   self				):
			return self.nir / (self.nir + self.red + self.green)

		def 							__lowerCamelCase							(   self				):
			return self.red / (self.nir + self.red + self.green)

		def 							__lowerCamelCase							(   self				):
			return (self.green - self.red) / (self.green + self.red)

		def 							__lowerCamelCase							(   self				):
			return (self.red - self.green) / (self.red + self.green)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   np.max([np.max(self.red				), np.max(self.green				), np.max(self.blue				)]				)
			__lowerCAmelCase    :		Dict		    =   np.min([np.min(self.red				), np.min(self.green				), np.min(self.blue				)]				)
			return (max_value - min_value) / max_value

		def 							__lowerCamelCase							(   self				):
			return (2 * self.red - self.green - self.blue) / (self.green - self.blue)

		def 							__lowerCamelCase							(   self				):
			return self.nir / self.red

		def 							__lowerCamelCase							(   self				):
			return (self.ndvi() + 0.5) ** (1 / 2)







		def 							__lowerCamelCase							(   self				):
			return (self.nir - self.redEdge) / (self.nir + self.redEdge)