





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      _UpperCamelCase  (a__ :List[str] ,      a__ :Tuple ,      a__ :Dict							):






							"""simple docstring"""





							return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]





def      _UpperCamelCase  (a__ :str ,      a__ :List[Any] ,      a__ :Tuple ,      a__ :Dict="attention"							):






							"""simple docstring"""





							UpperCamelCase__					  =      UpperCamelCase__					  =      np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]							)
							UpperCamelCase__					  =      k_tmp.reshape(k_tmp.shape[0] ,      k_tmp.shape[1] * k_tmp.shape[2]							)
							UpperCamelCase__					  =      np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]							)
							UpperCamelCase__					  =      o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,      o_tmp.shape[2]							)
							UpperCamelCase__					  =      np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]							)
							UpperCamelCase__					  =      q_tmp.reshape(q_tmp.shape[0] ,      q_tmp.shape[1] * q_tmp.shape[2]							)
							UpperCamelCase__					  =      np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]							)
							UpperCamelCase__					  =      v_tmp.reshape(v_tmp.shape[0] ,      v_tmp.shape[1] * v_tmp.shape[2]							)
							return k, o, q, v





def      _UpperCamelCase  (a__ :Union[str, Any] ,      a__ :Optional[Any] ,      a__ :List[Any] ,      a__ :Optional[int]=False							):






							"""simple docstring"""





							if split_mlp_wi:
														UpperCamelCase__					  =      params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
														UpperCamelCase__					  =      params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
														UpperCamelCase__					  =      (wi_a, wi_a)
							else:
														UpperCamelCase__					  =      params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

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





def      _UpperCamelCase  (a__ :str ,      a__ :Any ,      a__ :Union[str, Any] ,      a__ :Tuple							):






							"""simple docstring"""





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





def      _UpperCamelCase  (a__ :dict ,      *, a__ :int ,      a__ :bool ,      a__ :bool = False							):






							"""simple docstring"""





							UpperCamelCase__					  =      traverse_util.flatten_dict(variables["""target"""]							)
							UpperCamelCase__					  =      {"""/""".join(a__							): v for k, v in old.items()}

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

							UpperCamelCase__					  =      collections.OrderedDict()

							# Shared embeddings.
							UpperCamelCase__					  =      old["""token_embedder/embedding"""]

							# Encoder.
							for i in range(a__							):
														# Block i, layer 0 (Self Attention).
														UpperCamelCase__					  =      tax_layer_norm_lookup(a__ ,      a__ ,      """encoder""" ,      """pre_attention_layer_norm"""							)
														UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      tax_attention_lookup(a__ ,      a__ ,      """encoder""" ,      """attention"""							)
														UpperCamelCase__					  =      layer_norm
														UpperCamelCase__					  =      k.T
														UpperCamelCase__					  =      o.T
														UpperCamelCase__					  =      q.T
														UpperCamelCase__					  =      v.T

														# Block i, layer 1 (MLP).
														UpperCamelCase__					  =      tax_layer_norm_lookup(a__ ,      a__ ,      """encoder""" ,      """pre_mlp_layer_norm"""							)
														UpperCamelCase__  ,  UpperCamelCase__					  =      tax_mlp_lookup(a__ ,      a__ ,      """encoder""" ,      a__							)
														UpperCamelCase__					  =      layer_norm
														if split_mlp_wi:
																					UpperCamelCase__					  =      wi[0].T
																					UpperCamelCase__					  =      wi[1].T
														else:
																					UpperCamelCase__					  =      wi.T
														UpperCamelCase__					  =      wo.T
														if scalable_attention:
																					# convert the rel_embedding of each layer
																					UpperCamelCase__					  =      tax_relpos_bias_lookup(
																					    a__ ,      a__ ,      """encoder"""							).T

							UpperCamelCase__					  =      old["""encoder/encoder_norm/scale"""]

							if not scalable_attention:
														UpperCamelCase__					  =      tax_relpos_bias_lookup(
														    a__ ,      0 ,      """encoder"""							).T
														UpperCamelCase__					  =      tax_relpos_bias_lookup(
														    a__ ,      0 ,      """decoder"""							).T

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

																					# Block i, layer 1 (Cross Attention).
																					UpperCamelCase__					  =      tax_layer_norm_lookup(a__ ,      a__ ,      """decoder""" ,      """pre_cross_attention_layer_norm"""							)
																					UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      tax_attention_lookup(a__ ,      a__ ,      """decoder""" ,      """encoder_decoder_attention"""							)
																					UpperCamelCase__					  =      layer_norm
																					UpperCamelCase__					  =      k.T
																					UpperCamelCase__					  =      o.T
																					UpperCamelCase__					  =      q.T
																					UpperCamelCase__					  =      v.T

																					# Block i, layer 2 (MLP).
																					UpperCamelCase__					  =      tax_layer_norm_lookup(a__ ,      a__ ,      """decoder""" ,      """pre_mlp_layer_norm"""							)
																					UpperCamelCase__  ,  UpperCamelCase__					  =      tax_mlp_lookup(a__ ,      a__ ,      """decoder""" ,      a__							)
																					UpperCamelCase__					  =      layer_norm
																					if split_mlp_wi:
																												UpperCamelCase__					  =      wi[0].T
																												UpperCamelCase__					  =      wi[1].T
																					else:
																												UpperCamelCase__					  =      wi.T
																					UpperCamelCase__					  =      wo.T

																					if scalable_attention:
																												# convert the rel_embedding of each layer
																												UpperCamelCase__					  =      tax_relpos_bias_lookup(a__ ,      a__ ,      """decoder"""							).T

														UpperCamelCase__					  =      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:
																					UpperCamelCase__					  =      old["""decoder/logits_dense/kernel"""].T

							return new





def      _UpperCamelCase  (a__ :Tuple ,      a__ :bool							):






							"""simple docstring"""





							UpperCamelCase__					  =      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:
														UpperCamelCase__					  =      state_dict["""shared.weight"""]

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					UpperCamelCase__					  =      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."""							)
																					UpperCamelCase__					  =      state_dict["""shared.weight"""]

							return state_dict





def      _UpperCamelCase  (a__ :Dict ,      a__ :Tuple ,      a__ :Optional[Any] ,      a__ :str ,      a__ :str							):






							"""simple docstring"""





							UpperCamelCase__					  =      checkpoints.load_tax_checkpoint(a__							)
							UpperCamelCase__					  =      convert_tax_to_pytorch(
							    a__ ,      num_layers=config.num_layers ,      is_encoder_only=a__ ,      scalable_attention=a__							)
							UpperCamelCase__					  =      make_state_dict(a__ ,      a__							)
							model.load_state_dict(a__ ,      strict=a__							)





def      _UpperCamelCase  (a__ :str ,      a__ :Optional[int] ,      a__ :Tuple ,      a__ :bool = False ,      a__ :bool = False ,      ):






							"""simple docstring"""





							UpperCamelCase__					  =      MTaConfig.from_json_file(a__							)
							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:
														UpperCamelCase__					  =      UMTaEncoderModel(a__							)
							else:
														UpperCamelCase__					  =      UMTaForConditionalGeneration(a__							)

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

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

							# Verify that we can load the checkpoint.
							model.from_pretrained(a__							)
							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
					)
					parser.add_argument(
					    "--scalable_attention",
					    action="store_true",
					    help="Whether the model uses scaled attention (umt5 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,
					    args.scalable_attention,
					)











# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
UpperCamelCase__           =			"\nHuman: <<task>>\n\nAssistant: "


UpperCamelCase__           =			"huggingface-tools/default-prompts"
UpperCamelCase__           =			{"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}





def      _UpperCamelCase  (a__ :List[Any] ,      a__ :Optional[Any] ,      a__ :List[str]="run"							):






							"""simple docstring"""





							if prompt_or_repo_id is None:
														UpperCamelCase__					  =      DEFAULT_PROMPTS_REPO

							# prompt is considered a repo ID when it does not contain any kind of space
							if re.search("""\\s""" ,      a__							) is not None:
														return prompt_or_repo_id

							UpperCamelCase__					  =      cached_file(
							    a__ ,      PROMPT_FILES[mode] ,      repo_type="""dataset""" ,      user_agent={"""agent""": agent_name}							)
							with open(a__ ,      """r""" ,      encoding="""utf-8"""							) as f:
														return f.read()




