



import json
import multiprocessing
import os
import re
from collections import defaultdict

import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList


__snake_case		:Optional[Any]   	=  ['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']

class 					_A (			__UpperCAmelCase					):



       def __init__(	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=None  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=1):


           '''simple docstring'''

           __a		 =       tokenizer
           __a		 =       dataset
           __a		 =       len(__SCREAMING_SNAKE_CASE) if n_tasks is None else n_tasks
           __a		 =       n_copies





       def __iter__(	self							:  int):


           '''simple docstring'''

           __a		 =       []
           for task in range(self.n_tasks):
               # without strip, the model generate commented codes ...
               prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip())
           __a		 =       self.tokenizer(__SCREAMING_SNAKE_CASE  ,	padding=__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''')
           for task in range(self.n_tasks):
               for _ in range(self.n_copies):
                   yield {
                       "ids": outputs.input_ids[task],
                       "task_id": task,
                       "input_len": outputs.attention_mask[task].sum(),
                   }


class 					_A (			__UpperCAmelCase					):



       def __init__(	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict):


           '''simple docstring'''

           __a		 =       start_length
           __a		 =       eof_strings
           __a		 =       tokenizer





       def __call__(	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	**__SCREAMING_SNAKE_CASE							:  Tuple):


           '''simple docstring'''

           __a		 =       self.tokenizer.batch_decode(input_ids[:, self.start_length :])
           __a		 =       []
           for decoded_generation in decoded_generations:
               done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings))
           return all(__SCREAMING_SNAKE_CASE)





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       re.split('''(%s)''' % '''|'''.join(_UpperCAmelCase       )     ,				_UpperCAmelCase       )
    # last string should be ""
    return "".join(string_list[:-2]       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase=20     ,				**_UpperCAmelCase       ):
    __a		 =       defaultdict(_UpperCAmelCase       )  # dict of list of generated tokens
    for step, batch in tqdm(enumerate(_UpperCAmelCase       )       ):
        with torch.no_grad():
            __a		 =       batch['''ids'''].shape[-1]
            __a		 =       accelerator.unwrap_model(_UpperCAmelCase       ).generate(
                input_ids=batch['''ids'''][:, : batch['''input_len''']]     ,				num_return_sequences=_UpperCAmelCase     ,				**_UpperCAmelCase       )
            # each task is generated batch_size times
            __a		 =       batch['''task_id'''].repeat(_UpperCAmelCase       )
            __a		 =       accelerator.pad_across_processes(
                _UpperCAmelCase     ,				dim=1     ,				pad_index=tokenizer.pad_token_id       )

            __a ,     __a		 =       accelerator.gather((generated_tokens, generated_tasks)       )
            __a		 =       generated_tokens.cpu().numpy()
            __a		 =       generated_tasks.cpu().numpy()

            for task, generated_tokens in zip(_UpperCAmelCase     ,				_UpperCAmelCase       ):
                gen_token_dict[task].append(_UpperCAmelCase       )

    __a		 =       [[] for _ in range(_UpperCAmelCase       )]
    for task, generated_tokens in gen_token_dict.items():
        for s in generated_tokens:
            __a		 =       tokenizer.decode(_UpperCAmelCase     ,				skip_special_tokens=_UpperCAmelCase     ,				clean_up_tokenization_spaces=_UpperCAmelCase       )
            code_gens[task].append(remove_last_block(_UpperCAmelCase       )       )
    return code_gens





def   __snake_case			(    ):
    # Setup configuration
    __a		 =       HfArgumentParser(_UpperCAmelCase       )
    __a		 =       parser.parse_args()

    transformers.logging.set_verbosity_error()
    # enables code execution in code_eval metric
    __a		 =       args.HF_ALLOW_CODE_EVAL
    # make sure tokenizer plays nice with multiprocessing
    __a		 =       '''false'''

    if args.num_workers is None:
        __a		 =       multiprocessing.cpu_count()

    # Use dataset load to feed to accelerate
    __a		 =       Accelerator()
    set_seed(args.seed     ,				device_specific=_UpperCAmelCase       )

    # Load model and tokenizer
    __a		 =       AutoTokenizer.from_pretrained(args.model_ckpt       )
    __a		 =       tokenizer.eos_token
    __a		 =       AutoModelForCausalLM.from_pretrained(args.model_ckpt       )

    # Generation settings
    __a		 =       {
        '''do_sample''': args.do_sample,
        '''temperature''': args.temperature,
        '''max_new_tokens''': args.max_new_tokens,
        '''top_p''': args.top_p,
        '''top_k''': args.top_k,
        '''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )]       ),
    }

    # Load evaluation dataset and metric
    __a		 =       load_dataset('''openai_humaneval'''       )
    __a		 =       load_metric('''code_eval'''       )

    __a		 =       args.num_tasks if args.num_tasks is not None else len(human_eval['''test''']       )
    __a		 =       args.n_samples // args.batch_size

    __a		 =       TokenizedDataset(_UpperCAmelCase     ,				human_eval['''test''']     ,				n_copies=_UpperCAmelCase     ,				n_tasks=_UpperCAmelCase       )
    # do not confuse args.batch_size, which is actually the num_return_sequences
    __a		 =       DataLoader(_UpperCAmelCase     ,				batch_size=1       )

    # Run a quick test to see if code evaluation is enabled
    try:
        __a		 =       code_eval_metric.compute(references=['''''']     ,				predictions=[['''''']]       )
    except ValueError as exception:
        print(
            '''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
            ''' flag to enable code evaluation.'''       )
        raise exception

    __a ,     __a		 =       accelerator.prepare(_UpperCAmelCase     ,				_UpperCAmelCase       )

    __a		 =       complete_code(
        _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				n_tasks=_UpperCAmelCase     ,				batch_size=args.batch_size     ,				**_UpperCAmelCase     ,				)

    if accelerator.is_main_process:
        __a		 =       []

        for task in tqdm(range(_UpperCAmelCase       )       ):
            __a		 =       human_eval['''test'''][task]['''test''']
            __a		 =       f'check({human_eval["test"][task]["entry_point"]})'
            references.append('''\n''' + test_func + '''\n''' + entry_point       )

        # Evaluate completions with "code_eval" metric
        __a ,     __a		 =       code_eval_metric.compute(
            references=_UpperCAmelCase     ,				predictions=_UpperCAmelCase     ,				num_workers=args.num_workers       )
        print(f'Results: {pass_at_k}'       )

        # Save results to json file
        with open(args.output_file     ,				'''w'''       ) as fp:
            json.dump(_UpperCAmelCase     ,				_UpperCAmelCase       )


# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
 main()










import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       []
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
            f'stage{idx}.patch_embed.proj.weight',
        )       )
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
            f'stage{idx}.patch_embed.proj.bias',
        )       )
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
            f'stage{idx}.patch_embed.norm.weight',
        )       )
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
            f'stage{idx}.patch_embed.norm.bias',
        )       )
    return embed





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       []
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj.bias',
        )       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc1.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc1.bias')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc2.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc2.bias')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', f'stage{idx}.blocks.{cnt}.norm1.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', f'stage{idx}.blocks.{cnt}.norm1.bias')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', f'stage{idx}.blocks.{cnt}.norm2.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', f'stage{idx}.blocks.{cnt}.norm2.bias')       )
    return attention_weights





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       []
    token.append((f'cvt.encoder.stages.{idx}.cls_token', '''stage2.cls_token''')       )
    return token





def   __snake_case			(    ):
    __a		 =       []
    head.append(('''layernorm.weight''', '''norm.weight''')       )
    head.append(('''layernorm.bias''', '''norm.bias''')       )
    head.append(('''classifier.weight''', '''head.weight''')       )
    head.append(('''classifier.bias''', '''head.bias''')       )
    return head





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       '''imagenet-1k-id2label.json'''
    __a		 =       1000

    __a		 =       '''huggingface/label-files'''
    __a		 =       num_labels
    __a		 =       json.load(open(cached_download(hf_hub_url(_UpperCAmelCase     ,				_UpperCAmelCase     ,				repo_type='''dataset'''       )       )     ,				'''r'''       )       )
    __a		 =       {int(_UpperCAmelCase       ): v for k, v in idalabel.items()}

    __a		 =       idalabel
    __a		 =       {v: k for k, v in idalabel.items()}

    __a		 =       __a		 =       CvtConfig(num_labels=_UpperCAmelCase     ,				idalabel=_UpperCAmelCase     ,				labelaid=_UpperCAmelCase       )

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit('''/'''     ,				1       )[-1][4:6] == "13":
        __a		 =       [1, 2, 10]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit('''/'''     ,				1       )[-1][4:6] == "21":
        __a		 =       [1, 4, 16]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        __a		 =       [2, 2, 20]
        __a		 =       [3, 12, 16]
        __a		 =       [192, 768, 1024]

    __a		 =       CvtForImageClassification(_UpperCAmelCase       )
    __a		 =       AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''       )
    __a		 =       image_size
    __a		 =       torch.load(_UpperCAmelCase     ,				map_location=torch.device('''cpu'''       )       )

    __a		 =       OrderedDict()
    __a		 =       []

    for idx in range(len(config.depth       )       ):
        if config.cls_token[idx]:
            __a		 =       list_of_state_dict + cls_token(_UpperCAmelCase       )
        __a		 =       list_of_state_dict + embeddings(_UpperCAmelCase       )
        for cnt in range(config.depth[idx]       ):
            __a		 =       list_of_state_dict + attention(_UpperCAmelCase     ,				_UpperCAmelCase       )

    __a		 =       list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase       )
    for i in range(len(_UpperCAmelCase       )       ):
        __a		 =       original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_UpperCAmelCase       )
    model.save_pretrained(_UpperCAmelCase       )
    image_processor.save_pretrained(_UpperCAmelCase       )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
 __snake_case		:str   	=  argparse.ArgumentParser()
 parser.add_argument(
     '''--cvt_model''',
     default='''cvt-w24''',
     type=str,
     help='''Name of the cvt model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--image_size''',
     default=384,
     type=int,
     help='''Input Image Size''',
 )
 parser.add_argument(
     '''--cvt_file_name''',
     default=r'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
     type=str,
     help='''Input Image Size''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )

 __snake_case		:Dict   	=  parser.parse_args()
 convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





