




import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE = None       ,    SCREAMING_SNAKE_CASE = None       ,    SCREAMING_SNAKE_CASE = None       ,    )			->							Dict:


			'''simple docstring'''







			if config_name_or_path is None:
						SCREAMING_SNAKE_CASE_								=						'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

			if generator_tokenizer_name_or_path is None:
						SCREAMING_SNAKE_CASE_								=						generator_name_or_path

			if question_encoder_tokenizer_name_or_path is None:
						SCREAMING_SNAKE_CASE_								=						question_encoder_name_or_path

			SCREAMING_SNAKE_CASE_								=						RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

			# Save model.
			SCREAMING_SNAKE_CASE_								=						RagConfig.from_pretrained(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE_								=						gen_config
			SCREAMING_SNAKE_CASE_								=						question_encoder_config

			SCREAMING_SNAKE_CASE_								=						model_class.from_pretrained_question_encoder_generator(
			    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    config=SCREAMING_SNAKE_CASE )
			rag_model.save_pretrained(SCREAMING_SNAKE_CASE )

			# Sanity check.
			model_class.from_pretrained(SCREAMING_SNAKE_CASE )

			# Save tokenizers.
			SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
			gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/' )
			SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
			question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/' )


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       argparse.ArgumentParser()
				parser.add_argument(
				    "--model_type",
				    choices=["rag_sequence", "rag_token"],
				    required=True,
				    type=str,
				    help="RAG model type: rag_sequence, rag_token",
				)
				parser.add_argument("--dest", type=str, required=True, help="Path to the output checkpoint directory.")
				parser.add_argument("--generator_name_or_path", type=str, required=True, help="Generator model identifier")
				parser.add_argument(
				    "--question_encoder_name_or_path", type=str, required=True, help="Question encoder model identifier"
				)

				parser.add_argument(
				    "--generator_tokenizer_name_or_path",
				    type=str,
				    help="Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``",
				)
				parser.add_argument(
				    "--question_encoder_tokenizer_name_or_path",
				    type=str,
				    help="Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``",
				)
				parser.add_argument(
				    "--config_name_or_path",
				    type=str,
				    help=(
				        "Identifier of the model config to use, if not provided, resolves to a base config for a given"
				        " ``model_type``"
				    ),
				)

				SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       parser.parse_args()

				SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       Path(args.dest)
				dest_dir.mkdir(exist_ok=True)

				consolidate(
				    args.model_type,
				    args.generator_name_or_path,
				    args.question_encoder_name_or_path,
				    dest_dir,
				    args.config_name_or_path,
				    args.generator_tokenizer_name_or_path,
				    args.question_encoder_tokenizer_name_or_path,
				)






# Copyright 2023 The HuggingFace 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 torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce

def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						gather(SCREAMING_SNAKE_CASE )
			assert gathered_tensor.tolist() == list(range(1       ,    state.num_processes**2 + 1 ) )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[state.process_index]
			SCREAMING_SNAKE_CASE_								=						gather_object(SCREAMING_SNAKE_CASE )
			assert len(SCREAMING_SNAKE_CASE ) == state.num_processes, F'{gathered_obj}, {len(SCREAMING_SNAKE_CASE )} != {state.num_processes}'
			assert gathered_obj == list(range(state.num_processes ) ), F'{gathered_obj} != {list(range(state.num_processes ) )}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						broadcast(SCREAMING_SNAKE_CASE )
			assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
			assert broadcasted_tensor.tolist() == list(range(1       ,    state.num_processes + 1 ) )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			if state.is_main_process:
						SCREAMING_SNAKE_CASE_								=						torch.arange(state.num_processes + 1 ).to(state.device )
			else:
						SCREAMING_SNAKE_CASE_								=						torch.arange(state.num_processes ).to(state.device )
			SCREAMING_SNAKE_CASE_								=						pad_across_processes(SCREAMING_SNAKE_CASE )
			assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
			if not state.is_main_process:
						assert padded_tensor.tolist() == list(range(0       ,    state.num_processes ) ) + [0]

def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			if state.num_processes != 2:
						return
			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						reduce(SCREAMING_SNAKE_CASE       ,    'sum' )
			SCREAMING_SNAKE_CASE_								=						torch.tensor([4.0, 6] ).to(state.device )
			assert torch.allclose(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ), F'{reduced_tensor} != {truth_tensor}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			if state.num_processes != 2:
						return
			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						reduce(SCREAMING_SNAKE_CASE       ,    'mean' )
			SCREAMING_SNAKE_CASE_								=						torch.tensor([2.0, 3] ).to(state.device )
			assert torch.allclose(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ), F'{reduced_tensor} != {truth_tensor}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			main()

def lowercase    (  )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						PartialState()
			state.print(F'State: {state}' )
			state.print('testing gather' )
			test_gather(SCREAMING_SNAKE_CASE )
			state.print('testing gather_object' )
			test_gather_object(SCREAMING_SNAKE_CASE )
			state.print('testing broadcast' )
			test_broadcast(SCREAMING_SNAKE_CASE )
			state.print('testing pad_across_processes' )
			test_pad_across_processes(SCREAMING_SNAKE_CASE )
			state.print('testing reduce_sum' )
			test_reduce_sum(SCREAMING_SNAKE_CASE )
			state.print('testing reduce_mean' )
			test_reduce_mean(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				main()
