





'''simple docstring'''







# 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 ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
					from PIL import Image
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='naver-clova-ix/donut-base-finetuned-docvqa'
				__a            =(
				    'This is a tool that answers a question about an document (pdf). It takes an input named `document` which '
				    'should be the document containing the information, as well as a `question` that is the question about the '
				    'document. It returns a text that contains the answer to the question.'
				)
				__a            ='document_qa'
				__a            =AutoProcessor
				__a            =VisionEncoderDecoderModel

				__a            =['image', 'text']
				__a            =['text']




				def __init__(  self     :					Optional[Any]					,				*__a     :					Any					,				**__a     :					Union[str, Any]					):
								if not is_vision_available():
												raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool."					)

								super().__init__(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					"Image"					,				__a     :					str					):
								_a								= "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
								_a								= task_prompt.replace("{user_input}"					,				__a					)
								_a								= self.pre_processor.tokenizer(
								    __a					,				add_special_tokens=__a					,				return_tensors="pt"					).input_ids
								_a								= self.pre_processor(__a					,				return_tensors="pt"					).pixel_values

								return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}




				def   UpperCamelCase__   (  self     :					List[Any]					,				__a     :					List[Any]					):
								return self.model.generate(
								    inputs["pixel_values"].to(self.device					)					,				decoder_input_ids=inputs["decoder_input_ids"].to(self.device					)					,				max_length=self.model.decoder.config.max_position_embeddings					,				early_stopping=__a					,				pad_token_id=self.pre_processor.tokenizer.pad_token_id					,				eos_token_id=self.pre_processor.tokenizer.eos_token_id					,				use_cache=__a					,				num_beams=1					,				bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]					,				return_dict_in_generate=__a					,				).sequences




				def   UpperCamelCase__   (  self     :					str					,				__a     :					Tuple					):
								_a								= self.pre_processor.batch_decode(__a					)[0]
								_a								= sequence.replace(self.pre_processor.tokenizer.eos_token					,				""					)
								_a								= sequence.replace(self.pre_processor.tokenizer.pad_token					,				""					)
								_a								= re.sub(r"<.*?>"					,				""					,				__a					,				count=1					).strip()  # remove first task start token
								_a								= self.pre_processor.tokenajson(__a					)

								return sequence["answer"]


'''simple docstring'''




import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


lowercase			:				Optional[int]    		=			{
    '169M': 12,
    '430M': 24,
    '1B5': 24,
    '3B': 32,
    '7B': 32,
    '14B': 40,
}

lowercase			:				Optional[Any]    		=			{
    '169M': 7_68,
    '430M': 10_24,
    '1B5': 20_48,
    '3B': 25_60,
    '7B': 40_96,
    '14B': 51_20,
}







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Optional[Any]					=						list(state_dict.keys()						)
    for name in state_dict_keys:
        A       :   str					=						state_dict.pop(snake_case__						)
        # emb -> embedding
        if name.startswith('''emb.'''						):
            A       :   Optional[Any]					=						name.replace('''emb.'''		,				'''embeddings.'''						)
        # ln_0 -> pre_ln (only present at block 0)
        if name.startswith('''blocks.0.ln0'''						):
            A       :   Union[str, Any]					=						name.replace('''blocks.0.ln0'''		,				'''blocks.0.pre_ln'''						)
        # att -> attention
        A       :   int					=						re.sub(R'''blocks\.(\d+)\.att'''		,				R'''blocks.\1.attention'''		,				snake_case__						)
        # ffn -> feed_forward
        A       :   List[Any]					=						re.sub(R'''blocks\.(\d+)\.ffn'''		,				R'''blocks.\1.feed_forward'''		,				snake_case__						)
        # time_mix_k -> time_mix_key and reshape
        if name.endswith('''.time_mix_k'''						):
            A       :   List[str]					=						name.replace('''.time_mix_k'''		,				'''.time_mix_key'''						)
        # time_mix_v -> time_mix_value and reshape
        if name.endswith('''.time_mix_v'''						):
            A       :   Union[str, Any]					=						name.replace('''.time_mix_v'''		,				'''.time_mix_value'''						)
        # time_mix_r -> time_mix_key and reshape
        if name.endswith('''.time_mix_r'''						):
            A       :   Union[str, Any]					=						name.replace('''.time_mix_r'''		,				'''.time_mix_receptance'''						)

        if name != "head.weight":
            A       :   List[Any]					=						'''rwkv.''' + name

        A       :   Dict					=						weight
    return state_dict







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=False		,				snake_case__=None						):




    '''simple docstring'''



    if tokenizer_file is None:
        print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''						)
        A       :   int					=						5_0277
        A       :   Optional[int]					=						AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''						)
    else:
        A       :   str					=						PreTrainedTokenizerFast(tokenizer_file=snake_case__						)
        A       :   Any					=						len(snake_case__						)
    tokenizer.save_pretrained(snake_case__						)

    # 2. Build the config
    A       :   List[str]					=						list(NUM_HIDDEN_LAYERS_MAPPING.keys()						)
    if size is None:
        # Try to infer size from the checkpoint name
        for candidate in possible_sizes:
            if candidate in checkpoint_file:
                A       :   List[str]					=						candidate
                break
        if size is None:
            raise ValueError('''Could not infer the size, please provide it with the `--size` argument.'''						)
    if size not in possible_sizes:
        raise ValueError(F'`size` should be one of {possible_sizes}, got {size}.'						)

    A       :   Any					=						RwkvConfig(
        vocab_size=snake_case__		,				num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]		,				hidden_size=HIDEN_SIZE_MAPPING[size]		,				)
    config.save_pretrained(snake_case__						)

    # 3. Download model file then convert state_dict
    A       :   Union[str, Any]					=						hf_hub_download(snake_case__		,				snake_case__						)
    A       :   Tuple					=						torch.load(snake_case__		,				map_location='''cpu'''						)
    A       :   List[Any]					=						convert_state_dict(snake_case__						)

    # 4. Split in shards and save
    A,  A       :   List[str]					=						shard_checkpoint(snake_case__						)
    for shard_file, shard in shards.items():
        torch.save(snake_case__		,				os.path.join(snake_case__		,				snake_case__						)						)

    if index is not None:
        A       :   Dict					=						os.path.join(snake_case__		,				snake_case__						)
        # Save the index as well
        with open(snake_case__		,				'''w'''		,				encoding='''utf-8'''						) as f:
            A       :   List[Any]					=						json.dumps(snake_case__		,				indent=2		,				sort_keys=snake_case__						) + '''\n'''
            f.write(snake_case__						)

        # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
        print(
            '''Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.'''						)
        A       :   List[Any]					=						list(shards.keys()						)

        del state_dict
        del shards
        gc.collect()

        for shard_file in shard_files:
            A       :   Union[str, Any]					=						torch.load(os.path.join(snake_case__		,				snake_case__						)						)
            torch.save({k: v.cpu().clone() for k, v in state_dict.items()}		,				os.path.join(snake_case__		,				snake_case__						)						)

    del state_dict
    gc.collect()

    if push_to_hub:
        if model_name is None:
            raise ValueError('''Please provide a `model_name` to push the model to the Hub.'''						)
        A       :   int					=						AutoModelForCausalLM.from_pretrained(snake_case__						)
        model.push_to_hub(snake_case__		,				max_shard_size='''2GB'''						)
        tokenizer.push_to_hub(snake_case__						)


if __name__ == "__main__":
      lowercase			:				List[str]    		=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--repo_id', default=None, type=str, required=True, help='Repo ID from which to pull the checkpoint.'
      )
      parser.add_argument(
          '--checkpoint_file', default=None, type=str, required=True, help='Name of the checkpoint file in the repo.'
      )
      parser.add_argument(
          '--output_dir', default=None, type=str, required=True, help='Where to save the converted model.'
      )
      parser.add_argument(
          '--tokenizer_file',
          default=None,
          type=str,
          help='Path to the tokenizer file to use (if not provided, only the model is converted).',
      )
      parser.add_argument(
          '--size',
          default=None,
          type=str,
          help='Size of the model. Will be inferred from the `checkpoint_file` if not passed.',
      )
      parser.add_argument(
          '--push_to_hub',
          action='store_true',
          help='Push to the Hub the converted model.',
      )
      parser.add_argument(
          '--model_name',
          default=None,
          type=str,
          help='Name of the pushed model on the Hub, including the username / organization.',
      )

      lowercase			:				Union[str, Any]    		=			parser.parse_args()
      convert_rmkv_checkpoint_to_hf_format(
          args.repo_id,
          args.checkpoint_file,
          args.output_dir,
          size=args.size,
          tokenizer_file=args.tokenizer_file,
          push_to_hub=args.push_to_hub,
          model_name=args.model_name,
      )




