



'''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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_="attention"  )			->      Union[str, Any]:
		_a     :      Tuple        =							params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
		_a     :      int        =							params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
		_a     :      List[str]        =							params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
		_a     :      Any        =							params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
		return k, o, q, v



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      Dict:
		if split_mlp_wi:
				_a     :      Optional[int]        =							params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
				_a     :      Tuple        =							params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
				_a     :      List[Any]        =							(wi_a, wi_a)
		else:
				_a     :      Optional[Any]        =							params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

		_a     :      Optional[Any]        =							params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
		return wi, wo



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      str:
		return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]



def      __lowerCamelCase     (      lowerCAmelCase_						,			*, lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[Any]:
		_a     :      Optional[Any]        =							traverse_util.flatten_dict(variables['target']  )
		_a     :      Tuple        =							{'/'.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
		_a     :      Any        =							'encoder/layers_0/mlp/wi_0/kernel' in old
		print('Split MLP:'						,			lowerCAmelCase_  )

		_a     :      Union[str, Any]        =							collections.OrderedDict()

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

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

				# Block i, layer 1 (MLP).
				_a     :      Union[str, Any]        =							tax_layer_norm_lookup(lowerCAmelCase_						,			lowerCAmelCase_						,			'encoder'						,			'pre_mlp_layer_norm'  )
				_a       ,					_a     :      List[str]        =							tax_mlp_lookup(lowerCAmelCase_						,			lowerCAmelCase_						,			'encoder'						,			lowerCAmelCase_  )
				_a     :      Union[str, Any]        =							layer_norm
				if split_mlp_wi:
						_a     :      int        =							wi[0].T
						_a     :      List[Any]        =							wi[1].T
				else:
						_a     :      Any        =							wi.T
				_a     :      Tuple        =							wo.T

		_a     :      int        =							old[
		    'encoder/relpos_bias/rel_embedding'
		].T
		_a     :      Optional[Any]        =							old['encoder/encoder_norm/scale']

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

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

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

				_a     :      List[Any]        =							old['decoder/decoder_norm/scale']
				_a     :      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:
						_a     :      Dict        =							old['decoder/logits_dense/kernel'].T

		return new



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Dict:
		_a     :      Tuple        =							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     :      List[Any]        =							state_dict['shared.weight']

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

		return state_dict



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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = False  )			->      List[Any]:
		_a     :      int        =							TaConfig.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:
				_a     :      str        =							TaEncoderModel(lowerCAmelCase_  )
		else:
				_a     :      Tuple        =							TaForConditionalGeneration(lowerCAmelCase_  )

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









'''simple docstring'''

import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
							import PIL.Image

							from .features import FeatureType


__lowerCAmelCase									=   None
__lowerCAmelCase									=   '''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__lowerCAmelCase									=   [
    np.dtype('''|b1'''),
    np.dtype('''|u1'''),
    np.dtype('''<u2'''),
    np.dtype('''>u2'''),
    np.dtype('''<i2'''),
    np.dtype('''>i2'''),
    np.dtype('''<u4'''),
    np.dtype('''>u4'''),
    np.dtype('''<i4'''),
    np.dtype('''>i4'''),
    np.dtype('''<f4'''),
    np.dtype('''>f4'''),
    np.dtype('''<f8'''),
    np.dtype('''>f8'''),
]

@dataclass
class 	__magic_name__						:
							lowerCAmelCase       :    bool   				=					True
							lowerCAmelCase       :    Optional[str]   				=					None
							# Automatically constructed
							lowerCAmelCase       :    ClassVar[str]   				=					"PIL.Image.Image"
							lowerCAmelCase       :    ClassVar[Any]   				=					pa.struct({'bytes': pa.binary(), 'path': pa.string()}   )
							lowerCAmelCase       :    str   				=					field(default='Image'			,						init=_UpperCamelCase			,						repr=_UpperCamelCase   )







							def __call__(							self					:       Union[str, Any]		):
									return self.pa_type







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]		):
									if config.PIL_AVAILABLE:
											import PIL.Image
									else:
											raise ImportError('To support encoding images, please install \'Pillow\'.'		)

									if isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
											_a     :      Optional[Any]        =							np.array(_UpperCAmelCase		)

									if isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
											return {"path": value, "bytes": None}
									elif isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
											return {"path": None, "bytes": value}
									elif isinstance(_UpperCAmelCase	,np.ndarray		):
											# convert the image array to PNG/TIFF bytes
											return encode_np_array(_UpperCAmelCase		)
									elif isinstance(_UpperCAmelCase	,PIL.Image.Image		):
											# convert the PIL image to bytes (default format is PNG/TIFF)
											return encode_pil_image(_UpperCAmelCase		)
									elif value.get('path'		) is not None and os.path.isfile(value['path']		):
											# we set "bytes": None to not duplicate the data if they're already available locally
											return {"bytes": None, "path": value.get('path'		)}
									elif value.get('bytes'		) is not None or value.get('path'		) is not None:
											# store the image bytes, and path is used to infer the image format using the file extension
											return {"bytes": value.get('bytes'		), "path": value.get('path'		)}
									else:
											raise ValueError(
											    F"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       dict	,_UpperCAmelCase					:       Optional[int]=None		):
									if not self.decode:
											raise RuntimeError('Decoding is disabled for this feature. Please use Image(decode=True) instead.'		)

									if config.PIL_AVAILABLE:
											import PIL.Image
									else:
											raise ImportError('To support decoding images, please install \'Pillow\'.'		)

									if token_per_repo_id is None:
											_a     :      Dict        =							{}

									_a       ,					_a     :      str        =							value['path'], value['bytes']
									if bytes_ is None:
											if path is None:
													raise ValueError(F"""An image should have one of 'path' or 'bytes' but both are None in {value}."""		)
											else:
													if is_local_path(_UpperCAmelCase		):
															_a     :      Any        =							PIL.Image.open(_UpperCAmelCase		)
													else:
															_a     :      List[Any]        =							path.split('::'		)[-1]
															try:
																	_a     :      str        =							string_to_dict(_UpperCAmelCase	,config.HUB_DATASETS_URL		)['repo_id']
																	_a     :      Optional[Any]        =							token_per_repo_id.get(_UpperCAmelCase		)
															except ValueError:
																	_a     :      int        =							None
															with xopen(_UpperCAmelCase	,'rb'	,use_auth_token=_UpperCAmelCase		) as f:
																	_a     :      Tuple        =							BytesIO(f.read()		)
															_a     :      Union[str, Any]        =							PIL.Image.open(bytes_		)
									else:
											_a     :      Optional[int]        =							PIL.Image.open(BytesIO(bytes_		)		)
									image.load()  # to avoid "Too many open files" errors
									return image







							def 							__lowercase			(							self					:       int		):
									from .features import Value

									return (
									    self
									    if self.decode
									    else {
									        "bytes": Value('binary'		),
									        "path": Value('string'		),
									    }
									)







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       Union[pa.StringArray, pa.StructArray, pa.ListArray]		):
									if pa.types.is_string(storage.type		):
											_a     :      Union[str, Any]        =							pa.array([None] * len(_UpperCAmelCase		)	,type=pa.binary()		)
											_a     :      Union[str, Any]        =							pa.StructArray.from_arrays([bytes_array, storage]	,['bytes', 'path']	,mask=storage.is_null()		)
									elif pa.types.is_binary(storage.type		):
											_a     :      List[str]        =							pa.array([None] * len(_UpperCAmelCase		)	,type=pa.string()		)
											_a     :      Any        =							pa.StructArray.from_arrays([storage, path_array]	,['bytes', 'path']	,mask=storage.is_null()		)
									elif pa.types.is_struct(storage.type		):
											if storage.type.get_field_index('bytes'		) >= 0:
													_a     :      Union[str, Any]        =							storage.field('bytes'		)
											else:
													_a     :      Tuple        =							pa.array([None] * len(_UpperCAmelCase		)	,type=pa.binary()		)
											if storage.type.get_field_index('path'		) >= 0:
													_a     :      Union[str, Any]        =							storage.field('path'		)
											else:
													_a     :      Dict        =							pa.array([None] * len(_UpperCAmelCase		)	,type=pa.string()		)
											_a     :      Optional[Any]        =							pa.StructArray.from_arrays([bytes_array, path_array]	,['bytes', 'path']	,mask=storage.is_null()		)
									elif pa.types.is_list(storage.type		):
											_a     :      List[str]        =							pa.array(
											    [encode_np_array(np.array(_UpperCAmelCase		)		)['bytes'] if arr is not None else None for arr in storage.to_pylist()]	,type=pa.binary()	,)
											_a     :      int        =							pa.array([None] * len(_UpperCAmelCase		)	,type=pa.string()		)
											_a     :      Optional[Any]        =							pa.StructArray.from_arrays(
											    [bytes_array, path_array]	,['bytes', 'path']	,mask=bytes_array.is_null()		)
									return array_cast(_UpperCAmelCase	,self.pa_type		)







							def 							__lowercase			(							self					:       Dict	,_UpperCAmelCase					:       pa.StructArray		):

									@no_op_if_value_is_null
									def path_to_bytes(_UpperCAmelCase					:       Tuple		):
											with xopen(_UpperCAmelCase	,'rb'		) as f:
													_a     :      int        =							f.read()
											return bytes_

									_a     :      Any        =							pa.array(
									    [
									        (path_to_bytes(x['path']		) if x['bytes'] is None else x['bytes']) if x is not None else None
									        for x in storage.to_pylist()
									    ]	,type=pa.binary()	,)
									_a     :      Optional[Any]        =							pa.array(
									    [os.path.basename(_UpperCAmelCase		) if path is not None else None for path in storage.field('path'		).to_pylist()]	,type=pa.string()	,)
									_a     :      Dict        =							pa.StructArray.from_arrays([bytes_array, path_array]	,['bytes', 'path']	,mask=bytes_array.is_null()		)
									return array_cast(_UpperCAmelCase	,self.pa_type		)



def      __lowerCamelCase     (      )			->      List[str]:
		if config.PIL_AVAILABLE:
				import PIL.Image
		else:
				raise ImportError('To support encoding images, please install \'Pillow\'.'  )

		global _IMAGE_COMPRESSION_FORMATS
		if _IMAGE_COMPRESSION_FORMATS is None:
				PIL.Image.init()
				_a     :      Dict        =							list(set(PIL.Image.OPEN.keys()  ) & set(PIL.Image.SAVE.keys()  )  )
		return _IMAGE_COMPRESSION_FORMATS



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      bytes:
		_a     :      Optional[int]        =							BytesIO()
		if image.format in list_image_compression_formats():
				_a     :      Optional[Any]        =							image.format
		else:
				_a     :      str        =							'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
		image.save(lowerCAmelCase_						,			format=lowerCAmelCase_  )
		return buffer.getvalue()



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      dict:
		if hasattr(lowerCAmelCase_						,			'filename'  ) and image.filename != "":
				return {"path": image.filename, "bytes": None}
		else:
				return {"path": None, "bytes": image_to_bytes(lowerCAmelCase_  )}



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      dict:
		if config.PIL_AVAILABLE:
				import PIL.Image
		else:
				raise ImportError('To support encoding images, please install \'Pillow\'.'  )

		_a     :      List[Any]        =							array.dtype
		_a     :      Optional[int]        =							dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
		_a     :      Union[str, Any]        =							dtype.kind
		_a     :      Union[str, Any]        =							dtype.itemsize

		_a     :      List[Any]        =							None

		# Multi-channel array case (only np.dtype("|u1") is allowed)
		if array.shape[2:]:
				_a     :      Optional[int]        =							np.dtype('|u1'  )
				if dtype_kind not in ["u", "i"]:
						raise TypeError(
						    f"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."""  )
				if dtype is not dest_dtype:
						warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""  )
    # Exact match
		elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
				_a     :      Union[str, Any]        =							dtype
		else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
				while dtype_itemsize >= 1:
						_a     :      str        =							dtype_byteorder + dtype_kind + str(lowerCAmelCase_  )
						_a     :      List[Any]        =							np.dtype(lowerCAmelCase_  )
						if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
								warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""  )
								break
						else:
								dtype_itemsize //= 2

		if dest_dtype is None:
				raise TypeError(
				    f"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"""  )
		_a     :      Union[str, Any]        =							PIL.Image.fromarray(array.astype(lowerCAmelCase_  )  )
		return {"path": None, "bytes": image_to_bytes(lowerCAmelCase_  )}



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      List[dict]:
		if config.PIL_AVAILABLE:
				import PIL.Image
		else:
				raise ImportError('To support encoding images, please install \'Pillow\'.'  )

		if objs:
				_a       ,					_a     :      Optional[Any]        =							first_non_null_value(lowerCAmelCase_  )
				if isinstance(lowerCAmelCase_						,			lowerCAmelCase_  ):
						return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
				if isinstance(lowerCAmelCase_						,			np.ndarray  ):
						_a     :      List[str]        =							no_op_if_value_is_null(lowerCAmelCase_  )
						return [obj_to_image_dict_func(lowerCAmelCase_  ) for obj in objs]
				elif isinstance(lowerCAmelCase_						,			PIL.Image.Image  ):
						_a     :      List[str]        =							no_op_if_value_is_null(lowerCAmelCase_  )
						return [obj_to_image_dict_func(lowerCAmelCase_  ) for obj in objs]
				else:
						return objs
		else:
				return objs




