
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()
__magic_name__  :  Tuple     			=   logging.get_logger(__name__)

__magic_name__  :  int     			=   'Hello world! cécé herlolip'





def        lowerCAmelCase       (				snake_case__   :      str				,					snake_case__   :      str				,					snake_case__   :      bool )->			int:
    A_								=					FairseqRobertaModel.from_pretrained(snake_case__ )
    roberta.eval()  # disable dropout
    A_								=					roberta.model.encoder.sentence_encoder
    A_								=					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_								=					roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
    print("Our BERT config:"				,					snake_case__ )

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

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

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

        # self attention
        A_								=					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_								=					roberta_layer.self_attn.q_proj.weight
        A_								=					roberta_layer.self_attn.q_proj.bias
        A_								=					roberta_layer.self_attn.k_proj.weight
        A_								=					roberta_layer.self_attn.k_proj.bias
        A_								=					roberta_layer.self_attn.v_proj.weight
        A_								=					roberta_layer.self_attn.v_proj.bias

        # self-attention output
        A_								=					layer.attention.output
        assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
        A_								=					roberta_layer.self_attn.out_proj.weight
        A_								=					roberta_layer.self_attn.out_proj.bias
        A_								=					roberta_layer.self_attn_layer_norm.weight
        A_								=					roberta_layer.self_attn_layer_norm.bias

        # intermediate
        A_								=					layer.intermediate
        assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
        A_								=					roberta_layer.fca.weight
        A_								=					roberta_layer.fca.bias

        # output
        A_								=					layer.output
        assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
        A_								=					roberta_layer.fca.weight
        A_								=					roberta_layer.fca.bias
        A_								=					roberta_layer.final_layer_norm.weight
        A_								=					roberta_layer.final_layer_norm.bias
        # end of layer

    if classification_head:
        A_								=					roberta.model.classification_heads["mnli"].dense.weight
        A_								=					roberta.model.classification_heads["mnli"].dense.bias
        A_								=					roberta.model.classification_heads["mnli"].out_proj.weight
        A_								=					roberta.model.classification_heads["mnli"].out_proj.bias
    else:
        # LM Head
        A_								=					roberta.model.encoder.lm_head.dense.weight
        A_								=					roberta.model.encoder.lm_head.dense.bias
        A_								=					roberta.model.encoder.lm_head.layer_norm.weight
        A_								=					roberta.model.encoder.lm_head.layer_norm.bias
        A_								=					roberta.model.encoder.lm_head.weight
        A_								=					roberta.model.encoder.lm_head.bias

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

    A_								=					model(snake_case__ )[0]
    if classification_head:
        A_								=					roberta.model.classification_heads["mnli"](roberta.extract_features(snake_case__ ) )
    else:
        A_								=					roberta.model(snake_case__ )[0]
    print(our_output.shape				,					their_output.shape )
    A_								=					torch.max(torch.abs(our_output - their_output ) ).item()
    print(f'max_absolute_diff = {max_absolute_diff}' )  # ~ 1e-7
    A_								=					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__":
     __magic_name__  :  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.'
     )
     __magic_name__  :  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()






