




"""simple docstring"""





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Dict:


			"""simple docstring"""
			__UpperCAmelCase	: Union[str, Any]		=					AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_							)
			__UpperCAmelCase	: Optional[Any]		=					FlaxAutoModelForSeqaSeqLM.from_config(config=SCREAMING_SNAKE_CASE_							)
			__UpperCAmelCase	: int		=					checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_							)

			__UpperCAmelCase	: int		=					"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

			if config.model_type == "t5":
						__UpperCAmelCase	: Optional[Any]		=					"SelfAttention"
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						__UpperCAmelCase	: Tuple		=					"LocalSelfAttention"
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						__UpperCAmelCase	: Tuple		=					"TransientGlobalSelfAttention"
			else:
						raise ValueError(
						    "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
						    " attribute with a value from ['local', 'transient-global]."							)

			# Encoder
			for layer_index in range(config.num_layers							):
						__UpperCAmelCase	: List[str]		=					f"layers_{str(SCREAMING_SNAKE_CASE_							)}"

						# Self-Attention
						__UpperCAmelCase	: List[Any]		=					tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
						__UpperCAmelCase	: Union[str, Any]		=					tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
						__UpperCAmelCase	: Optional[Any]		=					tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
						__UpperCAmelCase	: List[Any]		=					tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									__UpperCAmelCase	: str		=					tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

						# Layer Normalization
						__UpperCAmelCase	: Union[str, Any]		=					tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

						if split_mlp_wi:
									__UpperCAmelCase	: List[str]		=					tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
									__UpperCAmelCase	: List[str]		=					tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
						else:
									__UpperCAmelCase	: Any		=					tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

						__UpperCAmelCase	: int		=					tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

						# Layer Normalization
						__UpperCAmelCase	: Optional[int]		=					tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

						# Assigning
						__UpperCAmelCase	: str		=					flax_model.params["encoder"]["block"][str(SCREAMING_SNAKE_CASE_							)]["layer"]
						__UpperCAmelCase	: List[str]		=					tax_attention_key
						__UpperCAmelCase	: Union[str, Any]		=					tax_attention_out
						__UpperCAmelCase	: Any		=					tax_attention_query
						__UpperCAmelCase	: Union[str, Any]		=					tax_attention_value

						__UpperCAmelCase	: Optional[Any]		=					tax_attention_layer_norm

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									__UpperCAmelCase	: Union[str, Any]		=					tax_global_layer_norm

						if split_mlp_wi:
									__UpperCAmelCase	: Union[str, Any]		=					tax_mlp_wi_a
									__UpperCAmelCase	: int		=					tax_mlp_wi_a
						else:
									__UpperCAmelCase	: int		=					tax_mlp_wi

						__UpperCAmelCase	: List[str]		=					tax_mlp_wo
						__UpperCAmelCase	: Tuple		=					tax_mlp_layer_norm

						__UpperCAmelCase	: Any		=					flax_model_encoder_layer_block

			# Only for layer 0:
			__UpperCAmelCase	: Union[str, Any]		=					tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
			__UpperCAmelCase	: int		=					tax_encoder_rel_embedding

			# Side/global relative position_bias + layer norm
			if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						__UpperCAmelCase	: Union[str, Any]		=					tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
						__UpperCAmelCase	: Dict		=					tax_encoder_global_rel_embedding

			# Assigning
			__UpperCAmelCase	: Union[str, Any]		=					tax_model["target"]["encoder"]["encoder_norm"]["scale"]
			__UpperCAmelCase	: Tuple		=					tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_layers							):
						__UpperCAmelCase	: str		=					f"layers_{str(SCREAMING_SNAKE_CASE_							)}"

						# Self-Attention
						__UpperCAmelCase	: int		=					tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
						__UpperCAmelCase	: Optional[int]		=					tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
						__UpperCAmelCase	: Any		=					tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
						__UpperCAmelCase	: Any		=					tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

						# Layer Normalization
						__UpperCAmelCase	: List[str]		=					tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
						    "scale"
						]

						# Encoder-Decoder-Attention
						__UpperCAmelCase	: int		=					tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
						__UpperCAmelCase	: List[str]		=					tax_enc_dec_attention_module["key"]["kernel"]
						__UpperCAmelCase	: int		=					tax_enc_dec_attention_module["out"]["kernel"]
						__UpperCAmelCase	: List[str]		=					tax_enc_dec_attention_module["query"]["kernel"]
						__UpperCAmelCase	: Any		=					tax_enc_dec_attention_module["value"]["kernel"]

						# Layer Normalization
						__UpperCAmelCase	: Union[str, Any]		=					tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

						# MLP
						if split_mlp_wi:
									__UpperCAmelCase	: Any		=					tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
									__UpperCAmelCase	: Optional[int]		=					tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
						else:
									__UpperCAmelCase	: str		=					tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

						__UpperCAmelCase	: Any		=					tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

						# Layer Normalization
						__UpperCAmelCase	: List[str]		=					tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

						# Assigning
						__UpperCAmelCase	: List[Any]		=					flax_model.params["decoder"]["block"][str(SCREAMING_SNAKE_CASE_							)]["layer"]
						__UpperCAmelCase	: Tuple		=					tax_attention_key
						__UpperCAmelCase	: Optional[Any]		=					tax_attention_out
						__UpperCAmelCase	: Dict		=					tax_attention_query
						__UpperCAmelCase	: str		=					tax_attention_value

						__UpperCAmelCase	: Tuple		=					tax_pre_attention_layer_norm

						__UpperCAmelCase	: Union[str, Any]		=					tax_enc_dec_attention_key
						__UpperCAmelCase	: List[str]		=					tax_enc_dec_attention_out
						__UpperCAmelCase	: List[Any]		=					tax_enc_dec_attention_query
						__UpperCAmelCase	: str		=					tax_enc_dec_attention_value

						__UpperCAmelCase	: Dict		=					tax_cross_layer_norm

						if split_mlp_wi:
									__UpperCAmelCase	: Optional[Any]		=					tax_mlp_wi_a
									__UpperCAmelCase	: Dict		=					tax_mlp_wi_a
						else:
									__UpperCAmelCase	: Dict		=					tax_mlp_wi

						__UpperCAmelCase	: List[Any]		=					tax_mlp_wo

						__UpperCAmelCase	: Dict		=					txa_mlp_layer_norm

						__UpperCAmelCase	: Dict		=					flax_model_decoder_layer_block

			# Decoder Normalization
			__UpperCAmelCase	: Tuple		=					tax_model["target"]["decoder"]["decoder_norm"]["scale"]
			__UpperCAmelCase	: Any		=					txa_decoder_norm

			# Only for layer 0:
			__UpperCAmelCase	: List[Any]		=					tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
			__UpperCAmelCase	: Any		=					tax_decoder_rel_embedding

			# Token Embeddings
			__UpperCAmelCase	: List[str]		=					tax_model["target"]["token_embedder"]["embedding"]
			__UpperCAmelCase	: int		=					txa_token_embeddings

			# LM Head (only in v1.1 and LongT5 checkpoints)
			if "logits_dense" in tax_model["target"]["decoder"]:
						__UpperCAmelCase	: List[Any]		=					tax_model["target"]["decoder"]["logits_dense"]["kernel"]

			flax_model.save_pretrained(SCREAMING_SNAKE_CASE_							)
			print("T5X Model was sucessfully converted!"							)


if __name__ == "__main__":
	A   =       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
	)
	parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
	parser.add_argument(
	    """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
	)
	A   =       parser.parse_args()
	convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


A   =       logging.get_logger(__name__)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			List[List[ImageInput]]:


			"""simple docstring"""
			if isinstance(UpperCamelCase				,       (list, tuple)							) and isinstance(videos[0]				,       (list, tuple)							) and is_valid_image(videos[0][0]							):
						return videos

			elif isinstance(UpperCamelCase				,       (list, tuple)							) and is_valid_image(videos[0]							):
						return [videos]

			elif is_valid_image(UpperCamelCase							):
						return [[videos]]

			raise ValueError(f"Could not make batched video from {videos}"							)





class      a__				(	__magic_name__   ):
			lowercase_    =	["pixel_values"]


			def __init__(      self      :    Tuple      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    PILImageResampling = PILImageResampling.BILINEAR      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    Union[int, float] = 1 / 255      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  **UpperCamelCase_      :    List[str]      ,  ):


						"""simple docstring"""




						super().__init__(**UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					size if size is not None else {"shortest_edge": 224}
						__UpperCAmelCase	: Union[str, Any]		=					get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_)
						__UpperCAmelCase	: Optional[int]		=					crop_size if crop_size is not None else {"height": 224, "width": 224}
						__UpperCAmelCase	: List[str]		=					get_size_dict(UpperCamelCase_      ,  param_name="crop_size")

						__UpperCAmelCase	: Optional[Any]		=					do_resize
						__UpperCAmelCase	: Union[str, Any]		=					size
						__UpperCAmelCase	: str		=					do_center_crop
						__UpperCAmelCase	: str		=					crop_size
						__UpperCAmelCase	: Any		=					resample
						__UpperCAmelCase	: List[Any]		=					do_rescale
						__UpperCAmelCase	: Union[str, Any]		=					rescale_factor
						__UpperCAmelCase	: List[Any]		=					do_normalize
						__UpperCAmelCase	: Dict		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__UpperCAmelCase	: Tuple		=					image_std if image_std is not None else IMAGENET_STANDARD_STD


			def       a_     (      self      :    int      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Dict[str, int]      ,  UpperCamelCase_      :    PILImageResampling = PILImageResampling.BILINEAR      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    List[Any]      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_)
						if "shortest_edge" in size:
									__UpperCAmelCase	: Dict		=					get_resize_output_image_size(UpperCamelCase_      ,  size["shortest_edge"]      ,  default_to_square=UpperCamelCase_)
						elif "height" in size and "width" in size:
									__UpperCAmelCase	: Dict		=					(size["height"], size["width"])
						else:
									raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
						return resize(UpperCamelCase_      ,  size=UpperCamelCase_      ,  resample=UpperCamelCase_      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_)


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Dict[str, int]      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    Tuple      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					get_size_dict(UpperCamelCase_)
						if "height" not in size or "width" not in size:
									raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}")
						return center_crop(UpperCamelCase_      ,  size=(size["height"], size["width"])      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_)


			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Union[int, float]      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    Optional[int]      ,  ):


						"""simple docstring"""




						return rescale(UpperCamelCase_      ,  scale=UpperCamelCase_      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_)


			def       a_     (      self      :    List[Any]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Union[float, List[float]]      ,  UpperCamelCase_      :    Union[float, List[float]]      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    Union[str, Any]      ,  ):


						"""simple docstring"""




						return normalize(UpperCamelCase_      ,  mean=UpperCamelCase_      ,  std=UpperCamelCase_      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_)


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    ImageInput      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    PILImageResampling = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    float = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  UpperCamelCase_      :    Optional[ChannelDimension] = ChannelDimension.FIRST      ,  ):


						"""simple docstring"""




						if do_resize and size is None or resample is None:
									raise ValueError("Size and resample must be specified if do_resize is True.")

						if do_center_crop and crop_size is None:
									raise ValueError("Crop size must be specified if do_center_crop is True.")

						if do_rescale and rescale_factor is None:
									raise ValueError("Rescale factor must be specified if do_rescale is True.")

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("Image mean and std must be specified if do_normalize is True.")

						# All transformations expect numpy arrays.
						__UpperCAmelCase	: List[str]		=					to_numpy_array(UpperCamelCase_)

						if do_resize:
									__UpperCAmelCase	: Tuple		=					self.resize(image=UpperCamelCase_      ,  size=UpperCamelCase_      ,  resample=UpperCamelCase_)

						if do_center_crop:
									__UpperCAmelCase	: str		=					self.center_crop(UpperCamelCase_      ,  size=UpperCamelCase_)

						if do_rescale:
									__UpperCAmelCase	: Union[str, Any]		=					self.rescale(image=UpperCamelCase_      ,  scale=UpperCamelCase_)

						if do_normalize:
									__UpperCAmelCase	: Optional[int]		=					self.normalize(image=UpperCamelCase_      ,  mean=UpperCamelCase_      ,  std=UpperCamelCase_)

						__UpperCAmelCase	: Any		=					to_channel_dimension_format(UpperCamelCase_      ,  UpperCamelCase_)
						return image


			def       a_     (      self      :    List[Any]      ,  UpperCamelCase_      :    ImageInput      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    PILImageResampling = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    float = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  UpperCamelCase_      :    Optional[Union[str, TensorType]] = None      ,  UpperCamelCase_      :    ChannelDimension = ChannelDimension.FIRST      ,  **UpperCamelCase_      :    Optional[int]      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					do_resize if do_resize is not None else self.do_resize
						__UpperCAmelCase	: Optional[Any]		=					resample if resample is not None else self.resample
						__UpperCAmelCase	: Any		=					do_center_crop if do_center_crop is not None else self.do_center_crop
						__UpperCAmelCase	: str		=					do_rescale if do_rescale is not None else self.do_rescale
						__UpperCAmelCase	: str		=					rescale_factor if rescale_factor is not None else self.rescale_factor
						__UpperCAmelCase	: Tuple		=					do_normalize if do_normalize is not None else self.do_normalize
						__UpperCAmelCase	: str		=					image_mean if image_mean is not None else self.image_mean
						__UpperCAmelCase	: Optional[Any]		=					image_std if image_std is not None else self.image_std

						__UpperCAmelCase	: Tuple		=					size if size is not None else self.size
						__UpperCAmelCase	: str		=					get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_)
						__UpperCAmelCase	: Any		=					crop_size if crop_size is not None else self.crop_size
						__UpperCAmelCase	: int		=					get_size_dict(UpperCamelCase_      ,  param_name="crop_size")

						if not valid_images(UpperCamelCase_):
									raise ValueError(
									    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
									    "torch.Tensor, tf.Tensor or jax.ndarray.")

						__UpperCAmelCase	: Dict		=					make_batched(UpperCamelCase_)

						__UpperCAmelCase	: List[str]		=					[
						    [
						        self._preprocess_image(
						            image=UpperCamelCase_      ,  do_resize=UpperCamelCase_      ,  size=UpperCamelCase_      ,  resample=UpperCamelCase_      ,  do_center_crop=UpperCamelCase_      ,  crop_size=UpperCamelCase_      ,  do_rescale=UpperCamelCase_      ,  rescale_factor=UpperCamelCase_      ,  do_normalize=UpperCamelCase_      ,  image_mean=UpperCamelCase_      ,  image_std=UpperCamelCase_      ,  data_format=UpperCamelCase_      ,  )
						        for img in video
						    ]
						    for video in videos
						]

						__UpperCAmelCase	: int		=					{"pixel_values": videos}
						return BatchFeature(data=UpperCamelCase_      ,  tensor_type=UpperCamelCase_)


