





"""simple docstring"""




import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
   raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
lowerCamelCase_	:		Tuple         =   logging.get_logger(__name__)

lowerCamelCase_	:		int         =   'Hello world! cécé herlolip'





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Optional[int]									=    FairseqRobertaModel.from_pretrained(snake_case__   )
  roberta.eval()  # disable dropout
  A_    :			int									=    roberta.model.encoder.sentence_encoder
  A_    :			Optional[int]									=    RobertaConfig(
      vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings					,   hidden_size=roberta.args.encoder_embed_dim					,   num_hidden_layers=roberta.args.encoder_layers					,   num_attention_heads=roberta.args.encoder_attention_heads					,   intermediate_size=roberta.args.encoder_ffn_embed_dim					,   max_position_embeddings=514					,   type_vocab_size=1					,   layer_norm_eps=1E-5					,   )
  if classification_head:
    A_    :			Any									=    roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
  print('Our BERT config:'					,   snake_case__   )

  A_    :			Tuple									=    RobertaForSequenceClassification(snake_case__   ) if classification_head else RobertaForMaskedLM(snake_case__   )
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  A_    :			Tuple									=    roberta_sent_encoder.embed_tokens.weight
  A_    :			int									=    roberta_sent_encoder.embed_positions.weight
  A_    :			Tuple									=    torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight   )  # just zero them out b/c RoBERTa doesn't use them.
  A_    :			Optional[Any]									=    roberta_sent_encoder.emb_layer_norm.weight
  A_    :			Optional[int]									=    roberta_sent_encoder.emb_layer_norm.bias

  for i in range(config.num_hidden_layers   ):
    # Encoder: start of layer
    A_    :			Optional[int]									=    model.roberta.encoder.layer[i]
    A_    :			Union[str, Any]									=    roberta_sent_encoder.layers[i]

    # self attention
    A_    :			Optional[int]									=    layer.attention.self
    assert (
        roberta_layer.self_attn.k_proj.weight.data.shape
        == roberta_layer.self_attn.q_proj.weight.data.shape
        == roberta_layer.self_attn.v_proj.weight.data.shape
        == torch.Size((config.hidden_size, config.hidden_size)   )
    )

    A_    :			List[str]									=    roberta_layer.self_attn.q_proj.weight
    A_    :			Tuple									=    roberta_layer.self_attn.q_proj.bias
    A_    :			str									=    roberta_layer.self_attn.k_proj.weight
    A_    :			Optional[int]									=    roberta_layer.self_attn.k_proj.bias
    A_    :			int									=    roberta_layer.self_attn.v_proj.weight
    A_    :			List[Any]									=    roberta_layer.self_attn.v_proj.bias

    # self-attention output
    A_    :			Optional[int]									=    layer.attention.output
    assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
    A_    :			List[Any]									=    roberta_layer.self_attn.out_proj.weight
    A_    :			Optional[Any]									=    roberta_layer.self_attn.out_proj.bias
    A_    :			Optional[Any]									=    roberta_layer.self_attn_layer_norm.weight
    A_    :			str									=    roberta_layer.self_attn_layer_norm.bias

    # intermediate
    A_    :			Union[str, Any]									=    layer.intermediate
    assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
    A_    :			Optional[int]									=    roberta_layer.fca.weight
    A_    :			str									=    roberta_layer.fca.bias

    # output
    A_    :			Optional[Any]									=    layer.output
    assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
    A_    :			Optional[int]									=    roberta_layer.fca.weight
    A_    :			Tuple									=    roberta_layer.fca.bias
    A_    :			Optional[int]									=    roberta_layer.final_layer_norm.weight
    A_    :			str									=    roberta_layer.final_layer_norm.bias
    # end of layer

  if classification_head:
    A_    :			Any									=    roberta.model.classification_heads['mnli'].dense.weight
    A_    :			Union[str, Any]									=    roberta.model.classification_heads['mnli'].dense.bias
    A_    :			str									=    roberta.model.classification_heads['mnli'].out_proj.weight
    A_    :			int									=    roberta.model.classification_heads['mnli'].out_proj.bias
  else:
    # LM Head
    A_    :			List[Any]									=    roberta.model.encoder.lm_head.dense.weight
    A_    :			Tuple									=    roberta.model.encoder.lm_head.dense.bias
    A_    :			int									=    roberta.model.encoder.lm_head.layer_norm.weight
    A_    :			Optional[int]									=    roberta.model.encoder.lm_head.layer_norm.bias
    A_    :			Any									=    roberta.model.encoder.lm_head.weight
    A_    :			List[str]									=    roberta.model.encoder.lm_head.bias

  # Let's check that we get the same results.
  A_    :			Optional[Any]									=    roberta.encode(snake_case__   ).unsqueeze(0   )  # batch of size 1

  A_    :			Any									=    model(snake_case__   )[0]
  if classification_head:
    A_    :			Any									=    roberta.model.classification_heads['mnli'](roberta.extract_features(snake_case__   )   )
  else:
    A_    :			Dict									=    roberta.model(snake_case__   )[0]
  print(our_output.shape					,   their_output.shape   )
  A_    :			Optional[int]									=    torch.max(torch.abs(our_output - their_output   )   ).item()
  print(f"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
  A_    :			List[Any]									=    torch.allclose(snake_case__					,   snake_case__					,   atol=1E-3   )
  print('Do both models output the same tensors?'					,   '🔥' if success else '💩'   )
  if not success:
    raise Exception('Something went wRoNg'   )

  pathlib.Path(snake_case__   ).mkdir(parents=snake_case__					,   exist_ok=snake_case__   )
  print(f"""Saving model to {pytorch_dump_folder_path}"""   )
  model.save_pretrained(snake_case__   )


if __name__ == "__main__":
   lowerCamelCase_	:		Tuple         =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   parser.add_argument(
       '--classification_head', action='store_true', help='Whether to convert a final classification head.'
   )
   lowerCamelCase_	:		str         =   parser.parse_args()
   convert_roberta_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )

import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
__magic_name__  :  List[str]     			=   logging.getLogger(__name__)





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			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        lowerCAmelCase       (				)->			int:
    A_								=					argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path"				,					default=snake_case__				,					type=snake_case__				,					required=snake_case__				,					)
    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("--penalty_alpha"				,					type=snake_case__				,					default=0.0 )
    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(
        "--fp16"				,					action="store_true"				,					help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"				,					)
    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(snake_case__ )

    # Initialize the model and tokenizer
    A_								=					AutoTokenizer.from_pretrained(args.model_name_or_path )
    A_								=					AutoModelForCausalLM.from_pretrained(args.model_name_or_path )

    # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
    # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
    model.to(args.device )

    if args.fpaa:
        model.half()

    logger.info(snake_case__ )
    A_								=					args.prompt if args.prompt else input("Model prompt >>> " )

    A_								=					tokenizer(snake_case__				,					return_tensors="pt"				,					add_special_tokens=snake_case__ )
    A_								=					{key: value.to(args.device ) for key, value in inputs.items()}

    A_								=					model.generate(
        **snake_case__				,					max_length=args.length + len(inputs["input_ids"][0] )				,					penalty_alpha=args.penalty_alpha				,					top_k=args.k				,					)

    A_								=					[]
    for generated_sequence_idx, generated_sequence in enumerate(snake_case__ ):
        print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===' )
        A_								=					generated_sequence.tolist()

        # Decode text
        A_								=					tokenizer.decode(snake_case__				,					clean_up_tokenization_spaces=snake_case__				,					add_special_tokens=snake_case__ )

        # 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(inputs["input_ids"][0]				,					clean_up_tokenization_spaces=snake_case__ ) ) :]
        )

        generated_sequences.append(snake_case__ )
        print(snake_case__ )

    return generated_sequences


if __name__ == "__main__":
     main()






