


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,
)
lowerCAmelCase__   	=				logging.getLogger(__name__)

lowerCAmelCase__   	=				int(1_00_00)  # Hardcoded max length to avoid infinite loop

lowerCAmelCase__   	=				{
    '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
lowerCAmelCase__   	=				'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       (UpperCamelCase__  :						Optional[Any]		):
       np.random.seed(args.seed		)
       torch.manual_seed(args.seed		)
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed		)
def 							_UpperCAmelCase       (UpperCamelCase__  :						List[str]       ,							UpperCamelCase__  :						Tuple       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Dict		):
       if args.temperature > 0.7:
              logger.info("CTRL typically works better with lower temperatures (and lower top_k)."		)

       _A   : int       					=				tokenizer.encode(UpperCamelCase__       ,							add_special_tokens=UpperCamelCase__		)
       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       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						List[str]       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						List[str]		):
       # kwargs = {"language": None, "mask_token_id": None}

       # Set the language
       _A   : Union[str, Any]       					=				hasattr(model.config       ,							"use_lang_emb"		) and model.config.use_lang_emb
       if hasattr(model.config       ,							"lang2id"		) and use_lang_emb:
              _A   : Optional[Any]       					=				model.config.langaid.keys()
              if args.xlm_language in available_languages:
                     _A   : Tuple       					=				args.xlm_language
              else:
                     _A   : Optional[int]       					=				None
                     while language not in available_languages:
                            _A   : Tuple       					=				input("Using XLM. Select language in " + str(list(UpperCamelCase__		)		) + " >>> "		)

              _A   : List[Any]       					=				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       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						List[Any]		):
       _A   : List[Any]       					=				args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
       _A   : Optional[int]       					=				prefix + prompt_text
       return prompt_text
def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						List[str]       ,							UpperCamelCase__  :						List[str]       ,							UpperCamelCase__  :						Tuple		):
       _A   : Optional[Any]       					=				args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
       _A   : Any       					=				prefix + prompt_text
       return prompt_text


lowerCAmelCase__   	=				{
    'ctrl': prepare_ctrl_input,
    'xlm': prepare_xlm_input,
    'xlnet': prepare_xlnet_input,
    'transfo-xl': prepare_transfoxl_input,
}
def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						int		):
       if length < 0 and max_sequence_length > 0:
              _A   : Optional[int]       					=				max_sequence_length
       elif 0 < max_sequence_length < length:
              _A   : Tuple       					=				max_sequence_length  # No generation bigger than model size
       elif length < 0:
              _A   : Optional[Any]       					=				MAX_LENGTH  # avoid infinite loop
       return length
def 							_UpperCAmelCase       (UpperCamelCase__  :						Tuple		):
       _A   : Dict       					=				None
       if hasattr(UpperCamelCase__       ,							"hidden_size"		):
              _A   : str       					=				model_config.hidden_size
       elif hasattr(UpperCamelCase__       ,							"n_embed"		):
              _A   : List[str]       					=				model_config.n_embed
       elif hasattr(UpperCamelCase__       ,							"n_embd"		):
              _A   : Optional[Any]       					=				model_config.n_embd

       _A   : Optional[Any]       					=				None
       if hasattr(UpperCamelCase__       ,							"num_attention_heads"		):
              _A   : Tuple       					=				model_config.num_attention_heads
       elif hasattr(UpperCamelCase__       ,							"n_head"		):
              _A   : Any       					=				model_config.n_head

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

       _A   : int       					=				int(embedding_size / num_head		)
       if hasattr(UpperCamelCase__       ,							"n_layer"		):
              _A   : Tuple       					=				model_config.n_layer
       elif hasattr(UpperCamelCase__       ,							"num_hidden_layers"		):
              _A   : str       					=				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       (UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Tuple		):
       _A   : Optional[int]       					=				sparse_model_config(model.config		)
       if model.config.model_type == "bloom":
              _A   : Any       					=				tuple(
                  (
                      torch.empty(int(num_attention_heads * batch_size		)       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                      .to(model.dtype		)
                      .to(model.device		),
                      torch.empty(int(num_attention_heads * batch_size		)       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                      .to(model.dtype		)
                      .to(model.device		),
                  )
                  for _ in range(UpperCamelCase__		)		)
       else:
              _A   : Union[str, Any]       					=				tuple(
                  (
                      torch.empty(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                      .to(model.dtype		)
                      .to(model.device		),
                      torch.empty(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                      .to(model.dtype		)
                      .to(model.device		),
                  )
                  for _ in range(UpperCamelCase__		)		)
       return past_key_values
def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						List[str]       ,							UpperCamelCase__  :						Any		):
       _A   : List[str]       					=				len(UpperCamelCase__		)
       _A   : Tuple       					=				tokenizer.batch_encode_plus(UpperCamelCase__       ,							return_tensors="pt"		)
       _A   : Any       					=				dummy_input.to(model.device		)
       if model.config.use_cache:
              _A   : Tuple       					=				generate_past_key_values(UpperCamelCase__       ,							UpperCamelCase__       ,							1		)
       _A   : Any       					=				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   lowerCAmelCase__ (       a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				("_optimized", "_default")


     def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Optional[Any]:
            _A   : Optional[int]       					=				optimized
            _A   : Optional[Any]       					=				default


     def __call__(		self				,		*__lowerCamelCase				,		**__lowerCamelCase)    ->       str:
            if kwargs["past_key_values"] is None and self._default.config.use_cache:
                   _A   : Optional[Any]       					=				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)
            _A   : List[Any]       					=				self._optimized(**__lowerCamelCase)
            _A   : Any       					=				outputs[0]
            _A   : Any       					=				outputs[1]
            _A   : Tuple       					=				CausalLMOutputWithPast(
                loss=__lowerCamelCase				,		logits=__lowerCamelCase				,		past_key_values=__lowerCamelCase				,		hidden_states=__lowerCamelCase				,		attentions=__lowerCamelCase				,		)
            return fixed_output


     def __getattr__(		self				,		__lowerCamelCase)    ->       Union[str, Any]:
            return getattr(self._default				,		__lowerCamelCase)


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		**__lowerCamelCase)    ->       int:
            return self._default.prepare_inputs_for_generation(
                __lowerCamelCase				,		past_key_values=__lowerCamelCase				,		inputs_embeds=__lowerCamelCase				,		use_cache=__lowerCamelCase				,		**__lowerCamelCase)


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Tuple[Tuple[torch.Tensor]]:
            return self._default._reorder_cache(__lowerCamelCase				,		__lowerCamelCase)




def 							_UpperCAmelCase       ():
       _A   : Union[str, Any]       					=				argparse.ArgumentParser()
       parser.add_argument(
           "--model_type"       ,							default=UpperCamelCase__       ,							type=UpperCamelCase__       ,							required=UpperCamelCase__       ,							help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()		)       ,							)
       parser.add_argument(
           "--model_name_or_path"       ,							default=UpperCamelCase__       ,							type=UpperCamelCase__       ,							required=UpperCamelCase__       ,							help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()		)       ,							)

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

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

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

       parser.add_argument("--seed"       ,							type=UpperCamelCase__       ,							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=UpperCamelCase__       ,							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"		)
       _A   : Union[str, Any]       					=				parser.parse_args()

       _A   : List[str]       					=				torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"		)
       _A   : Any       					=				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(UpperCamelCase__		)

       # Initialize the model and tokenizer
       try:
              _A   : Tuple       					=				args.model_type.lower()
              _A   : str       					=				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 :)"		)

       _A   : Tuple       					=				tokenizer_class.from_pretrained(args.model_name_or_path		)
       if tokenizer.pad_token is None:
              _A   : str       					=				tokenizer.eos_token
       _A   : Union[str, Any]       					=				model_class.from_pretrained(args.model_name_or_path		)
       model.to(args.device		)

       if args.fpaa:
              model.half()
       _A   : Dict       					=				getattr(model.config       ,							"max_position_embeddings"       ,							0		)
       _A   : List[Any]       					=				adjust_length_to_model(args.length       ,							max_sequence_length=UpperCamelCase__		)
       logger.info(UpperCamelCase__		)

       _A   : List[Any]       					=				args.prompt if args.prompt else input("Model prompt >>> "		)

       # Different models need different input formatting and/or extra arguments
       _A   : List[str]       					=				args.model_type in PREPROCESSING_FUNCTIONS.keys()
       if requires_preprocessing:
              _A   : List[Any]       					=				PREPROCESSING_FUNCTIONS.get(args.model_type		)
              _A   : Tuple       					=				prepare_input(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)

              if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
                     _A   : Tuple       					=				{"add_space_before_punct_symbol": True}
              else:
                     _A   : Dict       					=				{}

              _A   : List[Any]       					=				tokenizer.encode(
                  UpperCamelCase__       ,							add_special_tokens=UpperCamelCase__       ,							return_tensors="pt"       ,							**UpperCamelCase__		)
       else:
              _A   : int       					=				args.prefix if args.prefix else args.padding_text
              _A   : Any       					=				tokenizer.encode(prefix + prompt_text       ,							add_special_tokens=UpperCamelCase__       ,							return_tensors="pt"		)
       _A   : str       					=				encoded_prompt.to(args.device		)

       if encoded_prompt.size()[-1] == 0:
              _A   : Union[str, Any]       					=				None
       else:
              _A   : Any       					=				encoded_prompt

       if args.jit:
              _A   : Union[str, Any]       					=				["enable jit"]
              _A   : List[Any]       					=				prepare_jit_inputs(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
              torch._C._jit_set_texpr_fuser_enabled(UpperCamelCase__		)
              _A   : Union[str, Any]       					=				False
              if hasattr(UpperCamelCase__       ,							"forward"		):
                     _A   : List[Any]       					=				inspect.signature(model.forward		)
              else:
                     _A   : Union[str, Any]       					=				inspect.signature(model.__call__		)
              _A   : str       					=				tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(UpperCamelCase__       ,							UpperCamelCase__		) is not None		)
              _A   : Any       					=				torch.jit.trace(UpperCamelCase__       ,							UpperCamelCase__       ,							strict=UpperCamelCase__		)
              _A   : Dict       					=				torch.jit.freeze(traced_model.eval()		)
              traced_model(*UpperCamelCase__		)
              traced_model(*UpperCamelCase__		)

              _A   : int       					=				_ModelFallbackWrapper(UpperCamelCase__       ,							UpperCamelCase__		)

       _A   : Union[str, Any]       					=				model.generate(
           input_ids=UpperCamelCase__       ,							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=UpperCamelCase__       ,							num_return_sequences=args.num_return_sequences       ,							)

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

       _A   : List[str]       					=				[]

       for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase__		):
              print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="		)
              _A   : Optional[Any]       					=				generated_sequence.tolist()

              # Decode text
              _A   : List[Any]       					=				tokenizer.decode(UpperCamelCase__       ,							clean_up_tokenization_spaces=UpperCamelCase__		)

              # Remove all text after the stop token
              _A   : int       					=				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
              _A   : int       					=				(
                  prompt_text + text[len(tokenizer.decode(encoded_prompt[0]       ,							clean_up_tokenization_spaces=UpperCamelCase__		)		) :]
              )

              generated_sequences.append(UpperCamelCase__		)
              print(UpperCamelCase__		)

       return generated_sequences


if __name__ == "__main__":
    main()




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__   	=				{
    'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				['DetaImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				[
        'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'DetaForObjectDetection',
        'DetaModel',
        'DetaPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_deta import DetaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deta import (
            DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
            DetaForObjectDetection,
            DetaModel,
            DetaPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
