




"""simple docstring"""



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,
)
SCREAMING_SNAKE_CASE							=			logging.getLogger(__name__)

SCREAMING_SNAKE_CASE							=			int(1_0000)  # Hardcoded max length to avoid infinite loop

SCREAMING_SNAKE_CASE							=			{
    "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
SCREAMING_SNAKE_CASE							=			"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     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						Union[str, Any]:
 np.random.seed(args.seed		)
 torch.manual_seed(args.seed		)
 if args.n_gpu > 0:
  torch.cuda.manual_seed_all(args.seed		)







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_		)		->						Tuple:
 if args.temperature > 0.7:
  logger.info("CTRL typically works better with lower temperatures (and lower top_k)."		)

 A__			=							tokenizer.encode(lowercase_     ,					add_special_tokens=lowercase_		)
 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     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_		)		->						Any:
 # kwargs = {"language": None, "mask_token_id": None}

 # Set the language
 A__			=							hasattr(model.config     ,					"use_lang_emb"		) and model.config.use_lang_emb
 if hasattr(model.config     ,					"lang2id"		) and use_lang_emb:
  A__			=							model.config.langaid.keys()
  if args.xlm_language in available_languages:
   A__			=							args.xlm_language
  else:
   A__			=							None
   while language not in available_languages:
    A__			=							input("Using XLM. Select language in " + str(list(lowercase_		)		) + " >>> "		)

  A__			=							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     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_		)		->						int:
 A__			=							args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
 A__			=							prefix + prompt_text
 return prompt_text







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_		)		->						str:
 A__			=							args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
 A__			=							prefix + prompt_text
 return prompt_text


SCREAMING_SNAKE_CASE							=			{
    "ctrl": prepare_ctrl_input,
    "xlm": prepare_xlm_input,
    "xlnet": prepare_xlnet_input,
    "transfo-xl": prepare_transfoxl_input,
}







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						List[str]:
 if length < 0 and max_sequence_length > 0:
  A__			=							max_sequence_length
 elif 0 < max_sequence_length < length:
  A__			=							max_sequence_length  # No generation bigger than model size
 elif length < 0:
  A__			=							MAX_LENGTH  # avoid infinite loop
 return length







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						List[Any]:
 A__			=							None
 if hasattr(lowercase_     ,					"hidden_size"		):
  A__			=							model_config.hidden_size
 elif hasattr(lowercase_     ,					"n_embed"		):
  A__			=							model_config.n_embed
 elif hasattr(lowercase_     ,					"n_embd"		):
  A__			=							model_config.n_embd

 A__			=							None
 if hasattr(lowercase_     ,					"num_attention_heads"		):
  A__			=							model_config.num_attention_heads
 elif hasattr(lowercase_     ,					"n_head"		):
  A__			=							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(embedding_size / num_head		)
 if hasattr(lowercase_     ,					"n_layer"		):
  A__			=							model_config.n_layer
 elif hasattr(lowercase_     ,					"num_hidden_layers"		):
  A__			=							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     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						Tuple:
 A__, A__, A__			=							sparse_model_config(model.config		)
 if model.config.model_type == "bloom":
  A__			=							tuple(
      (
          torch.empty(int(num_attention_heads * batch_size		)     ,					lowercase_     ,					lowercase_		)
          .to(model.dtype		)
          .to(model.device		),
          torch.empty(int(num_attention_heads * batch_size		)     ,					lowercase_     ,					lowercase_		)
          .to(model.dtype		)
          .to(model.device		),
      )
      for _ in range(lowercase_		)		)
 else:
  A__			=							tuple(
      (
          torch.empty(lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_		)
          .to(model.dtype		)
          .to(model.device		),
          torch.empty(lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_		)
          .to(model.dtype		)
          .to(model.device		),
      )
      for _ in range(lowercase_		)		)
 return past_key_values







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						Any:
 A__			=							len(lowercase_		)
 A__			=							tokenizer.batch_encode_plus(lowercase_     ,					return_tensors="pt"		)
 A__			=							dummy_input.to(model.device		)
 if model.config.use_cache:
  A__			=							generate_past_key_values(lowercase_     ,					lowercase_     ,					1		)
 A__			=							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       UpperCAmelCase_    (		A_				):
    lowercase__		   = ('''_optimized''', '''_default''')




    def __init__(       self		:			Tuple      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			Union[str, Any] ) ->				Any:




     '''simple docstring'''

     A__			=							optimized
     A__			=							default




    def __call__(       self		:			int      ,					*snake_case_		:			int      ,					**snake_case_		:			List[str] ) ->				List[Any]:




     '''simple docstring'''

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




    def __getattr__(       self		:			List[str]      ,					snake_case_		:			Optional[int] ) ->				Optional[int]:




     '''simple docstring'''

     return getattr(self._default      ,					snake_case_ )




    def 				__magic_name__				(       self		:			int      ,					snake_case_		:			int      ,					snake_case_		:			Optional[int]=None      ,					snake_case_		:			Optional[int]=None      ,					snake_case_		:			List[str]=None      ,					**snake_case_		:			Tuple ) ->				Any:




     '''simple docstring'''

     return self._default.prepare_inputs_for_generation(
         snake_case_      ,					past_key_values=snake_case_      ,					inputs_embeds=snake_case_      ,					use_cache=snake_case_      ,					**snake_case_ )




    def 				__magic_name__				(       self		:			List[Any]      ,					snake_case_		:			Tuple[Tuple[torch.Tensor]]      ,					snake_case_		:			torch.Tensor ) ->				Tuple[Tuple[torch.Tensor]]:




     '''simple docstring'''

     return self._default._reorder_cache(snake_case_      ,					snake_case_ )







def     _SCREAMING_SNAKE_CASE  (				)		->						Dict:
 A__			=							argparse.ArgumentParser()
 parser.add_argument(
     "--model_type"     ,					default=lowercase_     ,					type=lowercase_     ,					required=lowercase_     ,					help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()		)     ,					)
 parser.add_argument(
     "--model_name_or_path"     ,					default=lowercase_     ,					type=lowercase_     ,					required=lowercase_     ,					help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()		)     ,					)

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

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

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

 parser.add_argument("--seed"     ,					type=lowercase_     ,					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=lowercase_     ,					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__			=							parser.parse_args()

 A__			=							torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"		)
 A__			=							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(lowercase_		)

 # Initialize the model and tokenizer
 try:
  A__			=							args.model_type.lower()
  A__, A__			=							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__			=							tokenizer_class.from_pretrained(args.model_name_or_path		)
 if tokenizer.pad_token is None:
  A__			=							tokenizer.eos_token
 A__			=							model_class.from_pretrained(args.model_name_or_path		)
 model.to(args.device		)

 if args.fpaa:
  model.half()
 A__			=							getattr(model.config     ,					"max_position_embeddings"     ,					0		)
 A__			=							adjust_length_to_model(args.length     ,					max_sequence_length=lowercase_		)
 logger.info(lowercase_		)

 A__			=							args.prompt if args.prompt else input("Model prompt >>> "		)

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

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

  A__			=							tokenizer.encode(
      lowercase_     ,					add_special_tokens=lowercase_     ,					return_tensors="pt"     ,					**lowercase_		)
 else:
  A__			=							args.prefix if args.prefix else args.padding_text
  A__			=							tokenizer.encode(prefix + prompt_text     ,					add_special_tokens=lowercase_     ,					return_tensors="pt"		)
 A__			=							encoded_prompt.to(args.device		)

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

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

  A__			=							_ModelFallbackWrapper(lowercase_     ,					lowercase_		)

 A__			=							model.generate(
     input_ids=lowercase_     ,					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=lowercase_     ,					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__			=							[]

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

  # Decode text
  A__			=							tokenizer.decode(lowercase_     ,					clean_up_tokenization_spaces=lowercase_		)

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

  generated_sequences.append(lowercase_		)
  print(lowercase_		)

 return generated_sequences


if __name__ == "__main__":
      main()











"""simple docstring"""



import numpy as np
import qiskit







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						qiskit.QuantumCircuit:
 A__			=							qiskit.QuantumCircuit(num_qubits + 1		)

 # First, let's deal with the case in which oracle is balanced
 if case == "balanced":
  # First generate a random number that tells us which CNOTs to
  # wrap in X-gates:
  A__			=							np.random.randint(1     ,					2**num_qubits		)
  # Next, format 'b' as a binary string of length 'n', padded with zeros:
  A__			=							format(lowercase_     ,					f"""0{num_qubits}b"""		)
  # Next, we place the first X-gates. Each digit in our binary string
  # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
  # we apply an X-gate to that qubit:
  for index, bit in enumerate(lowercase_		):
   if bit == "1":
    oracle_qc.x(lowercase_		)
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
  for index in range(lowercase_		):
   oracle_qc.cx(lowercase_     ,					lowercase_		)
  # Next, place the final X-gates
  for index, bit in enumerate(lowercase_		):
   if bit == "1":
    oracle_qc.x(lowercase_		)

    # Case in which oracle is constant
 if case == "constant":
  # First decide what the fixed output of the oracle will be
  # (either always 0 or always 1)
  A__			=							np.random.randint(2		)
  if output == 1:
   oracle_qc.x(lowercase_		)

 A__			=							oracle_qc.to_gate()
 A__			=							"Oracle"  # To show when we display the circuit
 return oracle_gate







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						qiskit.QuantumCircuit:
 A__			=							qiskit.QuantumCircuit(num_qubits + 1     ,					lowercase_		)
 # Set up the output qubit:
 dj_circuit.x(lowercase_		)
 dj_circuit.h(lowercase_		)
 # And set up the input register:
 for qubit in range(lowercase_		):
  dj_circuit.h(lowercase_		)
 # Let's append the oracle gate to our circuit:
 dj_circuit.append(lowercase_     ,					range(num_qubits + 1		)		)
 # Finally, perform the H-gates again and measure:
 for qubit in range(lowercase_		):
  dj_circuit.h(lowercase_		)

 for i in range(lowercase_		):
  dj_circuit.measure(lowercase_     ,					lowercase_		)

 return dj_circuit







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						qiskit.result.counts.Counts:
 A__			=							qiskit.Aer.get_backend("aer_simulator"		)

 A__			=							dj_oracle(lowercase_     ,					lowercase_		)
 A__			=							dj_algorithm(lowercase_     ,					lowercase_		)

 # Execute the circuit on the simulator
 A__			=							qiskit.execute(lowercase_     ,					lowercase_     ,					shots=10_00		)

 # Return the histogram data of the results of the experiment.
 return job.result().get_counts(lowercase_		)


if __name__ == "__main__":
      print(f'Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}')
      print(f'Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}')





