

import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
UpperCamelCase__	:   Optional[int]								=     logging.getLogger(__name__)

UpperCamelCase__	:   Any								=     int(10_000)  # Hardcoded max length to avoid infinite loop

UpperCamelCase__	:   int								=     {
    'gpt2': (GPTaLMHeadModel, GPTaTokenizer),
    'ctrl': (CTRLLMHeadModel, CTRLTokenizer),
    'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
    'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
    'xlm': (XLMWithLMHeadModel, XLMTokenizer),
    'gptj': (GPTJForCausalLM, AutoTokenizer),
    'bloom': (BloomForCausalLM, BloomTokenizerFast),
    'llama': (LlamaForCausalLM, LlamaTokenizer),
    'opt': (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
UpperCamelCase__	:   Optional[Any]								=     'In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>'


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		List[str]							):





    """simple docstring"""


    np.random.seed(args.seed							)
    torch.manual_seed(args.seed							)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed							)


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Any			, _SCREAMING_SNAKE_CASE       :		Any			, _SCREAMING_SNAKE_CASE       :		Union[str, Any]			, _SCREAMING_SNAKE_CASE       :		List[Any]							):





    """simple docstring"""


    if args.temperature > 0.7:
        logger.info('CTRL typically works better with lower temperatures (and lower top_k).'							)

    SCREAMING_SNAKE_CASE_						=    tokenizer.encode(__snake_case			, add_special_tokens=__snake_case							)
    if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()							):
        logger.info('WARNING! You are not starting your generation from a control code so you won\'t get good results'							)
    return prompt_text


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Tuple			, _SCREAMING_SNAKE_CASE       :		List[Any]			, _SCREAMING_SNAKE_CASE       :		Any			, _SCREAMING_SNAKE_CASE       :		str							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    hasattr(model.config			, 'use_lang_emb'							) and model.config.use_lang_emb
    if hasattr(model.config			, 'lang2id'							) and use_lang_emb:
        SCREAMING_SNAKE_CASE_						=    model.config.langaid.keys()
        if args.xlm_language in available_languages:
            SCREAMING_SNAKE_CASE_						=    args.xlm_language
        else:
            SCREAMING_SNAKE_CASE_						=    None
            while language not in available_languages:
                SCREAMING_SNAKE_CASE_						=    input('Using XLM. Select language in ' + str(list(__snake_case							)							) + ' >>> '							)

        SCREAMING_SNAKE_CASE_						=    model.config.langaid[language]
        # kwargs["language"] = tokenizer.lang2id[language]

    # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
    # XLM masked-language modeling (MLM) models need masked token
    # is_xlm_mlm = "mlm" in args.model_name_or_path
    # if is_xlm_mlm:
    #     kwargs["mask_token_id"] = tokenizer.mask_token_id

    return prompt_text


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Optional[int]			, _SCREAMING_SNAKE_CASE       :		int			, _SCREAMING_SNAKE_CASE       :		List[Any]			, _SCREAMING_SNAKE_CASE       :		int							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
    SCREAMING_SNAKE_CASE_						=    prefix + prompt_text
    return prompt_text


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		int			, _SCREAMING_SNAKE_CASE       :		Optional[int]			, _SCREAMING_SNAKE_CASE       :		Dict			, _SCREAMING_SNAKE_CASE       :		Any							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
    SCREAMING_SNAKE_CASE_						=    prefix + prompt_text
    return prompt_text


UpperCamelCase__	:   str								=     {
    'ctrl': prepare_ctrl_input,
    'xlm': prepare_xlm_input,
    'xlnet': prepare_xlnet_input,
    'transfo-xl': prepare_transfoxl_input,
}


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Optional[int]			, _SCREAMING_SNAKE_CASE       :		str							):





    """simple docstring"""


    if length < 0 and max_sequence_length > 0:
        SCREAMING_SNAKE_CASE_						=    max_sequence_length
    elif 0 < max_sequence_length < length:
        SCREAMING_SNAKE_CASE_						=    max_sequence_length  # No generation bigger than model size
    elif length < 0:
        SCREAMING_SNAKE_CASE_						=    MAX_LENGTH  # avoid infinite loop
    return length


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Optional[Any]							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    None
    if hasattr(__snake_case			, 'hidden_size'							):
        SCREAMING_SNAKE_CASE_						=    model_config.hidden_size
    elif hasattr(__snake_case			, 'n_embed'							):
        SCREAMING_SNAKE_CASE_						=    model_config.n_embed
    elif hasattr(__snake_case			, 'n_embd'							):
        SCREAMING_SNAKE_CASE_						=    model_config.n_embd

    SCREAMING_SNAKE_CASE_						=    None
    if hasattr(__snake_case			, 'num_attention_heads'							):
        SCREAMING_SNAKE_CASE_						=    model_config.num_attention_heads
    elif hasattr(__snake_case			, 'n_head'							):
        SCREAMING_SNAKE_CASE_						=    model_config.n_head

    if embedding_size is None or num_head is None or num_head == 0:
        raise ValueError('Check the model config'							)

    SCREAMING_SNAKE_CASE_						=    int(embedding_size / num_head							)
    if hasattr(__snake_case			, 'n_layer'							):
        SCREAMING_SNAKE_CASE_						=    model_config.n_layer
    elif hasattr(__snake_case			, 'num_hidden_layers'							):
        SCREAMING_SNAKE_CASE_						=    model_config.num_hidden_layers
    else:
        raise ValueError('Number of hidden layers couldn\'t be determined from the model config'							)

    return num_layer, num_head, num_embedding_size_per_head


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		List[Any]			, _SCREAMING_SNAKE_CASE       :		int			, _SCREAMING_SNAKE_CASE       :		str							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    sparse_model_config(model.config							)
    if model.config.model_type == "bloom":
        SCREAMING_SNAKE_CASE_						=    tuple(
            (
                torch.empty(int(num_attention_heads * batch_size							)			, __snake_case			, __snake_case							)
                .to(model.dtype							)
                .to(model.device							),
                torch.empty(int(num_attention_heads * batch_size							)			, __snake_case			, __snake_case							)
                .to(model.dtype							)
                .to(model.device							),
            )
            for _ in range(__snake_case							)							)
    else:
        SCREAMING_SNAKE_CASE_						=    tuple(
            (
                torch.empty(__snake_case			, __snake_case			, __snake_case			, __snake_case							)
                .to(model.dtype							)
                .to(model.device							),
                torch.empty(__snake_case			, __snake_case			, __snake_case			, __snake_case							)
                .to(model.dtype							)
                .to(model.device							),
            )
            for _ in range(__snake_case							)							)
    return past_key_values


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Union[str, Any]			, _SCREAMING_SNAKE_CASE       :		int			, _SCREAMING_SNAKE_CASE       :		int							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    len(__snake_case							)
    SCREAMING_SNAKE_CASE_						=    tokenizer.batch_encode_plus(__snake_case			, return_tensors='pt'							)
    SCREAMING_SNAKE_CASE_						=    dummy_input.to(model.device							)
    if model.config.use_cache:
        SCREAMING_SNAKE_CASE_						=    generate_past_key_values(__snake_case			, __snake_case			, 1							)
    SCREAMING_SNAKE_CASE_						=    torch.cat(
        [
            torch.zeros(dummy_input['attention_mask'].shape[0]			, 1							)
            .to(dummy_input['attention_mask'].dtype							)
            .to(model.device							),
            dummy_input['attention_mask'],
        ]			, -1			, )
    return dummy_input



class    __snake_case	(     a_							):
  __lowerCAmelCase  :						str    			=	("_optimized", "_default")

  def __init__(       self    ,		_A    ,		_A):
      SCREAMING_SNAKE_CASE_						=    optimized
      SCREAMING_SNAKE_CASE_						=    default

  def __call__(       self    ,		*_A    ,		**_A):
      if kwargs["past_key_values"] is None and self._default.config.use_cache:
          SCREAMING_SNAKE_CASE_						=    generate_past_key_values(self._default    ,		kwargs['input_ids'].shape[0]    ,		0)
      kwargs.pop('position_ids'    ,		_lowerCamelCase)
      for k in list(kwargs.keys()):
          if kwargs[k] is None or isinstance(kwargs[k]    ,		_lowerCamelCase):
              kwargs.pop(_lowerCamelCase)
      SCREAMING_SNAKE_CASE_						=    self._optimized(**_lowerCamelCase)
      SCREAMING_SNAKE_CASE_						=    outputs[0]
      SCREAMING_SNAKE_CASE_						=    outputs[1]
      SCREAMING_SNAKE_CASE_						=    CausalLMOutputWithPast(
          loss=_lowerCamelCase    ,		logits=_lowerCamelCase    ,		past_key_values=_lowerCamelCase    ,		hidden_states=_lowerCamelCase    ,		attentions=_lowerCamelCase    ,		)
      return fixed_output

  def __getattr__(       self    ,		_A):
      return getattr(self._default    ,		_lowerCamelCase)

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A=None    ,		_A=None    ,		_A=None    ,		**_A):
      return self._default.prepare_inputs_for_generation(
          _lowerCamelCase    ,		past_key_values=_lowerCamelCase    ,		inputs_embeds=_lowerCamelCase    ,		use_cache=_lowerCamelCase    ,		**_lowerCamelCase)


  def     lowerCAmelCase__  (       self    ,		_A    ,		_A):
      return self._default._reorder_cache(_lowerCamelCase    ,		_lowerCamelCase)






def        _UpperCAmelCase							(	):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    argparse.ArgumentParser()
    parser.add_argument(
        '--model_type'			, default=__snake_case			, type=__snake_case			, required=__snake_case			, help='Model type selected in the list: ' + ', '.join(MODEL_CLASSES.keys()							)			, )
    parser.add_argument(
        '--model_name_or_path'			, default=__snake_case			, type=__snake_case			, required=__snake_case			, help='Path to pre-trained model or shortcut name selected in the list: ' + ', '.join(MODEL_CLASSES.keys()							)			, )

    parser.add_argument('--prompt'			, type=__snake_case			, default=''							)
    parser.add_argument('--length'			, type=__snake_case			, default=20							)
    parser.add_argument('--stop_token'			, type=__snake_case			, default=__snake_case			, help='Token at which text generation is stopped'							)

    parser.add_argument(
        '--temperature'			, type=__snake_case			, default=1.0			, help='temperature of 1.0 has no effect, lower tend toward greedy sampling'			, )
    parser.add_argument(
        '--repetition_penalty'			, type=__snake_case			, default=1.0			, help='primarily useful for CTRL model; in that case, use 1.2'							)
    parser.add_argument('--k'			, type=__snake_case			, default=0							)
    parser.add_argument('--p'			, type=__snake_case			, default=0.9							)

    parser.add_argument('--prefix'			, type=__snake_case			, default=''			, help='Text added prior to input.'							)
    parser.add_argument('--padding_text'			, type=__snake_case			, default=''			, help='Deprecated, the use of `--prefix` is preferred.'							)
    parser.add_argument('--xlm_language'			, type=__snake_case			, default=''			, help='Optional language when used with the XLM model.'							)

    parser.add_argument('--seed'			, type=__snake_case			, default=42			, help='random seed for initialization'							)
    parser.add_argument('--no_cuda'			, action='store_true'			, help='Avoid using CUDA when available'							)
    parser.add_argument('--num_return_sequences'			, type=__snake_case			, default=1			, help='The number of samples to generate.'							)
    parser.add_argument(
        '--fp16'			, action='store_true'			, help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'			, )
    parser.add_argument('--jit'			, action='store_true'			, help='Whether or not to use jit trace to accelerate inference'							)
    SCREAMING_SNAKE_CASE_						=    parser.parse_args()

    SCREAMING_SNAKE_CASE_						=    torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
    SCREAMING_SNAKE_CASE_						=    0 if args.no_cuda else torch.cuda.device_count()

    logger.warning(f"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""							)

    set_seed(__snake_case							)

    # Initialize the model and tokenizer
    try:
        SCREAMING_SNAKE_CASE_						=    args.model_type.lower()
        SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    MODEL_CLASSES[args.model_type]
    except KeyError:
        raise KeyError('the model {} you specified is not supported. You are welcome to add it and open a PR :)'							)

    SCREAMING_SNAKE_CASE_						=    tokenizer_class.from_pretrained(args.model_name_or_path							)
    if tokenizer.pad_token is None:
        SCREAMING_SNAKE_CASE_						=    tokenizer.eos_token
    SCREAMING_SNAKE_CASE_						=    model_class.from_pretrained(args.model_name_or_path							)
    model.to(args.device							)

    if args.fpaa:
        model.half()
    SCREAMING_SNAKE_CASE_						=    getattr(model.config			, 'max_position_embeddings'			, 0							)
    SCREAMING_SNAKE_CASE_						=    adjust_length_to_model(args.length			, max_sequence_length=__snake_case							)
    logger.info(__snake_case							)

    SCREAMING_SNAKE_CASE_						=    args.prompt if args.prompt else input('Model prompt >>> '							)

    # Different models need different input formatting and/or extra arguments
    SCREAMING_SNAKE_CASE_						=    args.model_type in PREPROCESSING_FUNCTIONS.keys()
    if requires_preprocessing:
        SCREAMING_SNAKE_CASE_						=    PREPROCESSING_FUNCTIONS.get(args.model_type							)
        SCREAMING_SNAKE_CASE_						=    prepare_input(__snake_case			, __snake_case			, __snake_case			, __snake_case							)

        if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
            SCREAMING_SNAKE_CASE_						=    {'add_space_before_punct_symbol': True}
        else:
            SCREAMING_SNAKE_CASE_						=    {}

        SCREAMING_SNAKE_CASE_						=    tokenizer.encode(
            __snake_case			, add_special_tokens=__snake_case			, return_tensors='pt'			, **__snake_case							)
    else:
        SCREAMING_SNAKE_CASE_						=    args.prefix if args.prefix else args.padding_text
        SCREAMING_SNAKE_CASE_						=    tokenizer.encode(prefix + prompt_text			, add_special_tokens=__snake_case			, return_tensors='pt'							)
    SCREAMING_SNAKE_CASE_						=    encoded_prompt.to(args.device							)

    if encoded_prompt.size()[-1] == 0:
        SCREAMING_SNAKE_CASE_						=    None
    else:
        SCREAMING_SNAKE_CASE_						=    encoded_prompt

    if args.jit:
        SCREAMING_SNAKE_CASE_						=    ['enable jit']
        SCREAMING_SNAKE_CASE_						=    prepare_jit_inputs(__snake_case			, __snake_case			, __snake_case							)
        torch._C._jit_set_texpr_fuser_enabled(__snake_case							)
        SCREAMING_SNAKE_CASE_						=    False
        if hasattr(__snake_case			, 'forward'							):
            SCREAMING_SNAKE_CASE_						=    inspect.signature(model.forward							)
        else:
            SCREAMING_SNAKE_CASE_						=    inspect.signature(model.__call__							)
        SCREAMING_SNAKE_CASE_						=    tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(__snake_case			, __snake_case							) is not None							)
        SCREAMING_SNAKE_CASE_						=    torch.jit.trace(__snake_case			, __snake_case			, strict=__snake_case							)
        SCREAMING_SNAKE_CASE_						=    torch.jit.freeze(traced_model.eval()							)
        traced_model(*__snake_case							)
        traced_model(*__snake_case							)

        SCREAMING_SNAKE_CASE_						=    _ModelFallbackWrapper(__snake_case			, __snake_case							)

    SCREAMING_SNAKE_CASE_						=    model.generate(
        input_ids=__snake_case			, max_length=args.length + len(encoded_prompt[0]							)			, temperature=args.temperature			, top_k=args.k			, top_p=args.p			, repetition_penalty=args.repetition_penalty			, do_sample=__snake_case			, num_return_sequences=args.num_return_sequences			, )

    # Remove the batch dimension when returning multiple sequences
    if len(output_sequences.shape							) > 2:
        output_sequences.squeeze_()

    SCREAMING_SNAKE_CASE_						=    []

    for generated_sequence_idx, generated_sequence in enumerate(__snake_case							):
        print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""							)
        SCREAMING_SNAKE_CASE_						=    generated_sequence.tolist()

        # Decode text
        SCREAMING_SNAKE_CASE_						=    tokenizer.decode(__snake_case			, clean_up_tokenization_spaces=__snake_case							)

        # Remove all text after the stop token
        SCREAMING_SNAKE_CASE_						=    text[: text.find(args.stop_token							) if args.stop_token else None]

        # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
        SCREAMING_SNAKE_CASE_						=    (
            prompt_text + text[len(tokenizer.decode(encoded_prompt[0]			, clean_up_tokenization_spaces=__snake_case							)							) :]
        )

        generated_sequences.append(__snake_case							)
        print(__snake_case							)

    return generated_sequences


if __name__ == "__main__":
       main()










"""simple docstring"""



import subprocess
from argparse import ArgumentParser
from typing import List, Union

from huggingface_hub.hf_api import HfFolder, create_repo, whoami
from requests.exceptions import HTTPError

from . import BaseTransformersCLICommand

class 			a	(  a_      ):







    @staticmethod
    def      UpperCamelCase_ (     _lowerCamelCase			):
      lowercase				       =			parser.add_parser('login'				,						help='Log in using the same credentials as on huggingface.co'			)
      login_parser.set_defaults(func=lambda _lowerCamelCase			: LoginCommand(_lowerCamelCase			)			)
      lowercase				       =			parser.add_parser('whoami'				,						help='Find out which huggingface.co account you are logged in as.'			)
      whoami_parser.set_defaults(func=lambda _lowerCamelCase			: WhoamiCommand(_lowerCamelCase			)			)
      lowercase				       =			parser.add_parser('logout'				,						help='Log out'			)
      logout_parser.set_defaults(func=lambda _lowerCamelCase			: LogoutCommand(_lowerCamelCase			)			)

      # new system: git-based repo system
      lowercase				       =			parser.add_parser(
          'repo'				,						help='Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos.'				,						)
      lowercase				       =			repo_parser.add_subparsers(
          help='Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands'			)
      lowercase				       =			repo_subparsers.add_parser(
          'create'				,						help='Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co'			)
      repo_create_parser.add_argument(
          'name'				,						type=_lowerCamelCase				,						help='Name for your model\'s repo. Will be namespaced under your username to build the model id.'				,						)
      repo_create_parser.add_argument('--organization'				,						type=_lowerCamelCase				,						help='Optional: organization namespace.'			)
      repo_create_parser.add_argument('-y'				,						'--yes'				,						action='store_true'				,						help='Optional: answer Yes to the prompt'			)
      repo_create_parser.set_defaults(func=lambda _lowerCamelCase			: RepoCreateCommand(_lowerCamelCase			)			)


class 			a	:
    UpperCAmelCase_					:	Union[str, Any]      	="\u001b[1m"
    UpperCAmelCase_					:	Optional[Any]      	="\u001b[31m"
    UpperCAmelCase_					:	List[str]      	="\u001b[90m"
    UpperCAmelCase_					:	str      	="\u001b[0m"







    @classmethod
    def      UpperCamelCase_ (     cls				,						_lowerCamelCase			):
      return F'{cls._bold}{s}{cls._reset}'







    @classmethod
    def      UpperCamelCase_ (     cls				,						_lowerCamelCase			):
      return F'{cls._bold}{cls._red}{s}{cls._reset}'







    @classmethod
    def      UpperCamelCase_ (     cls				,						_lowerCamelCase			):
      return F'{cls._gray}{s}{cls._reset}'


def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[List[Union[str, int]]]						,   __snake_case     :    List[str]       ):





  '''simple docstring'''





  lowercase				       =			[max(len(str(__snake_case       )       ) for x in col       ) for col in zip(*__snake_case						,   __snake_case       )]
  lowercase				       =			('{{:{}}} ' * len(__snake_case       )).format(*__snake_case       )
  lowercase				       =			[]
  lines.append(row_format.format(*__snake_case       )       )
  lines.append(row_format.format(*['-' * w for w in col_widths]       )       )
  for row in rows:
    lines.append(row_format.format(*__snake_case       )       )
  return "\n".join(__snake_case       )

class 			a	:







    def __init__(     self				,						_lowerCamelCase			):
      lowercase				       =			args


class 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      print(
          ANSI.red(
              'ERROR! `huggingface-cli login` uses an outdated login mechanism '
              'that is not compatible with the Hugging Face Hub backend anymore. '
              'Please use `huggingface-cli login instead.'			)			)


class 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      print(
          ANSI.red(
              'WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use '
              '`huggingface-cli whoami` instead.'			)			)
      lowercase				       =			HfFolder.get_token()
      if token is None:
        print('Not logged in'			)
        exit()
      try:
        lowercase       ,	lowercase				       =			whoami(_lowerCamelCase			)
        print(_lowerCamelCase			)
        if orgs:
          print(ANSI.bold('orgs: '			)				,						','.join(_lowerCamelCase			)			)
      except HTTPError as e:
        print(_lowerCamelCase			)
        print(ANSI.red(e.response.text			)			)
        exit(1			)


class 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      print(
          ANSI.red(
              'ERROR! `transformers-cli logout` uses an outdated logout mechanism '
              'that is not compatible with the Hugging Face Hub backend anymore. '
              'Please use `huggingface-cli logout instead.'			)			)


class 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      print(
          ANSI.red(
              'WARNING! Managing repositories through transformers-cli is deprecated. '
              'Please use `huggingface-cli` instead.'			)			)
      lowercase				       =			HfFolder.get_token()
      if token is None:
        print('Not logged in'			)
        exit(1			)
      try:
        lowercase				       =			subprocess.check_output(['git', '--version']			).decode('utf-8'			)
        print(ANSI.gray(stdout.strip()			)			)
      except FileNotFoundError:
        print('Looks like you do not have git installed, please install.'			)

      try:
        lowercase				       =			subprocess.check_output(['git-lfs', '--version']			).decode('utf-8'			)
        print(ANSI.gray(stdout.strip()			)			)
      except FileNotFoundError:
        print(
            ANSI.red(
                'Looks like you do not have git-lfs installed, please install.'
                ' You can install from https://git-lfs.github.com/.'
                ' Then run `git lfs install` (you only have to do this once).'			)			)
      print(''			)

      lowercase       ,	lowercase				       =			whoami(_lowerCamelCase			)
      lowercase				       =			self.args.organization if self.args.organization is not None else user
      lowercase				       =			F'{namespace}/{self.args.name}'
      print(F'You are about to create {ANSI.bold(_lowerCamelCase			)}'			)

      if not self.args.yes:
        lowercase				       =			input('Proceed? [Y/n] '			).lower()
        if not (choice == "" or choice == "y" or choice == "yes"):
          print('Abort'			)
          exit()
      try:
        lowercase				       =			create_repo(_lowerCamelCase				,						name=self.args.name				,						organization=self.args.organization			)
      except HTTPError as e:
        print(_lowerCamelCase			)
        print(ANSI.red(e.response.text			)			)
        exit(1			)
      print('\nYour repo now lives at:'			)
      print(F'  {ANSI.bold(_lowerCamelCase			)}'			)
      print('\nYou can clone it locally with the command below, and commit/push as usual.'			)
      print(F'\n  git clone {url}'			)
      print(''			)


