
import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin







def 				UpperCAmelCase_(    a__=2 ,						a__=True ,						a__=False   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = Accelerator(step_scheduler_with_optimizer=a__ ,						split_batches=a__   )
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = torch.nn.Linear(2 ,						4   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.optim.AdamW(model.parameters() ,						lr=1.0   )
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = torch.optim.lr_scheduler.OneCycleLR(a__ ,						max_lr=0.01 ,						steps_per_epoch=2 ,						epochs=1   )
    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Any				      = accelerator.prepare(a__ ,						a__ ,						a__   )

    # Optimizer has stepped
    scheduler.step()
    if step_scheduler_with_optimizer or (num_processes == 1):
        assert (
            scheduler.scheduler.last_epoch == num_processes
        ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
    else:
        assert (
            scheduler.scheduler.last_epoch != num_processes
        ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""







def 				UpperCAmelCase_(    a__=2 ,						a__=True ,						a__=False   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[Any]				      = Accelerator(step_scheduler_with_optimizer=a__ ,						split_batches=a__   )
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.nn.Linear(2 ,						4   )
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.optim.AdamW(model.parameters() ,						lr=1.0   )
    SCREAMING_SNAKE_CASE				:				Dict				      = torch.optim.lr_scheduler.LambdaLR(a__ ,						lr_lambda=lambda a__   : 1 - n / 10   )
    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[str]				      = accelerator.prepare(a__ ,						a__ ,						a__   )

    # Optimizer has stepped
    SCREAMING_SNAKE_CASE				:				Dict				      = False
    scheduler.step()
    SCREAMING_SNAKE_CASE				:				str				      = 1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

    # Optimizer has not stepped
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        SCREAMING_SNAKE_CASE				:				List[str]				      = 1 - 2 / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""







def 				UpperCAmelCase_(    a__ = 2   ):



    """simple docstring"""



    from transformers import get_linear_schedule_with_warmup

    SCREAMING_SNAKE_CASE				:				str				      = [1, 2, 4]
    for num_steps in steps:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = GradientAccumulationPlugin(num_steps=a__ ,						adjust_scheduler=num_steps > 1   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = Accelerator(gradient_accumulation_plugin=a__   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.nn.Linear(2 ,						4   )
        SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.optim.AdamW(model.parameters() ,						lr=10.0   )
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = get_linear_schedule_with_warmup(optimizer=a__ ,						num_warmup_steps=0 ,						num_training_steps=20   )

        SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[Any]				      = accelerator.prepare(a__ ,						a__ ,						a__   )

        for i in range(10 * num_steps   ):
            with accelerator.accumulate(a__   ):
                optimizer.step()
                scheduler.step()

            if i == (10 * num_steps - 2):
                assert (
                    scheduler.get_last_lr()[0] != 0
                ), F"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
        assert (
            scheduler.get_last_lr()[0] == 0
        ), F"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
        GradientState._reset_state()






@require_cpu
class     a_       (			unittest.TestCase							):



       """simple docstring"""






       def 	__lowerCAmelCase     (		self   )					->str:
           debug_launcher(partial(_lowerCamelCase				,       num_processes=1   )				,       num_processes=1   )
           debug_launcher(partial(_lowerCamelCase				,       num_processes=1				,       split_batches=_lowerCamelCase   )				,       num_processes=1   )






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           debug_launcher(partial(_lowerCamelCase				,       num_processes=1   )				,       num_processes=1   )
           debug_launcher(partial(_lowerCamelCase				,       num_processes=1				,       split_batches=_lowerCamelCase   )				,       num_processes=1   )






       def 	__lowerCAmelCase     (		self   )					->int:
           debug_launcher(partial(_lowerCamelCase				,       num_processes=1				,       step_scheduler_with_optimizer=_lowerCamelCase   )				,       num_processes=1   )






       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           debug_launcher(partial(_lowerCamelCase				,       num_processes=1				,       step_scheduler_with_optimizer=_lowerCamelCase   )				,       num_processes=1   )






       def 	__lowerCAmelCase     (		self   )					->List[str]:
           AcceleratorState._reset_state(_lowerCamelCase   )
           debug_launcher(_lowerCamelCase   )
           debug_launcher(partial(_lowerCamelCase				,       num_processes=1				,       split_batches=_lowerCamelCase   )				,       num_processes=1   )






       def 	__lowerCAmelCase     (		self   )					->Union[str, Any]:
           AcceleratorState._reset_state(_lowerCamelCase   )
           debug_launcher(_lowerCamelCase   )
           debug_launcher(partial(_lowerCamelCase				,       num_processes=1				,       split_batches=_lowerCamelCase   )				,       num_processes=1   )






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           AcceleratorState._reset_state(_lowerCamelCase   )
           debug_launcher(partial(_lowerCamelCase				,       step_scheduler_with_optimizer=_lowerCamelCase   )   )






       def 	__lowerCAmelCase     (		self   )					->Any:
           AcceleratorState._reset_state(_lowerCamelCase   )
           debug_launcher(partial(_lowerCamelCase				,       step_scheduler_with_optimizer=_lowerCamelCase   )   )






       @require_huggingface_suite
       def 	__lowerCAmelCase     (		self   )					->str:
           AcceleratorState._reset_state(_lowerCamelCase   )
           debug_launcher(partial(_lowerCamelCase				,       num_processes=1   )   )
           debug_launcher(_lowerCamelCase   )


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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()
a__				:      Dict    		=     logging.get_logger(__name__)

a__				:      str    		=     '''Hello world! cécé herlolip'''







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[str]				      = os.path.split(a__   )
    SCREAMING_SNAKE_CASE				:				Any				      = DataaVecTextModel.from_pretrained(
        a__ ,						checkpoint_file=a__   )
    dataavec.eval()  # disable dropout
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec.models[0]
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_model.encoder.sentence_encoder
    SCREAMING_SNAKE_CASE				:				List[str]				      = DataaVecTextConfig(
        vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings ,						hidden_size=dataavec_model.args.encoder_embed_dim ,						num_hidden_layers=dataavec_model.args.encoder_layers ,						num_attention_heads=dataavec_model.args.encoder_attention_heads ,						intermediate_size=dataavec_model.args.encoder_ffn_embed_dim ,						max_position_embeddings=514 ,						type_vocab_size=1 ,						layer_norm_eps=1e-5 ,						)
    if classification_head:
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
    print('''Our BERT config:''' ,						a__   )

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = DataaVecTextForSequenceClassification(a__   ) if classification_head else DataaVecTextForMaskedLM(a__   )
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec_sent_encoder.embed_tokens.weight
    SCREAMING_SNAKE_CASE				:				int				      = dataavec_sent_encoder.embed_positions.weight
    SCREAMING_SNAKE_CASE				:				int				      = torch.zeros_like(
        model.dataavec_text.embeddings.token_type_embeddings.weight   )  # just zero them out b/c data2vec doesn't use them.
    SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_sent_encoder.layernorm_embedding.weight
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_sent_encoder.layernorm_embedding.bias

    for i in range(config.num_hidden_layers   ):
        # Encoder: start of layer
        SCREAMING_SNAKE_CASE				:				BertLayer				      = model.dataavec_text.encoder.layer[i]
        SCREAMING_SNAKE_CASE				:				TransformerSentenceEncoderLayer				      = dataavec_sent_encoder.layers[i]

        # self attention
        SCREAMING_SNAKE_CASE				:				BertSelfAttention				      = layer.attention.self
        assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )
        assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )
        assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )

        SCREAMING_SNAKE_CASE				:				str				      = dataavec_layer.self_attn.q_proj.weight
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_layer.self_attn.q_proj.bias
        SCREAMING_SNAKE_CASE				:				str				      = dataavec_layer.self_attn.k_proj.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_layer.self_attn.k_proj.bias
        SCREAMING_SNAKE_CASE				:				List[Any]				      = dataavec_layer.self_attn.v_proj.weight
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.self_attn.v_proj.bias

        # self-attention output
        SCREAMING_SNAKE_CASE				:				BertSelfOutput				      = layer.attention.output
        assert (
            self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
        ), F"""Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				List[Any]				      = dataavec_layer.self_attn.out_proj.weight
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_layer.self_attn.out_proj.bias
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.bias

        # intermediate
        SCREAMING_SNAKE_CASE				:				BertIntermediate				      = layer.intermediate
        assert (
            intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
        ), F"""Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_layer.fca.bias

        # output
        SCREAMING_SNAKE_CASE				:				BertOutput				      = layer.output
        assert (
            bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
        ), F"""Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				int				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.fca.bias
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.final_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_layer.final_layer_norm.bias
        # end of layer

    if classification_head:
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].dense.weight
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].dense.bias
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec.model.classification_heads['''mnli'''].out_proj.weight
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].out_proj.bias
    else:
        # LM Head
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_model.encoder.lm_head.dense.weight
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_model.encoder.lm_head.dense.bias
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_model.encoder.lm_head.layer_norm.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_model.encoder.lm_head.layer_norm.bias
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_model.encoder.lm_head.weight
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_model.encoder.lm_head.bias

    # Let's check that we get the same results.
    SCREAMING_SNAKE_CASE				:				torch.Tensor				      = dataavec.encode(a__   ).unsqueeze(0   )  # batch of size 1

    SCREAMING_SNAKE_CASE				:				List[str]				      = model(a__   )[0]
    if classification_head:
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(a__   )   )
    else:
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_model(a__   )[0]
    print(our_output.shape ,						their_output.shape   )
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.max(torch.abs(our_output - their_output   )   ).item()
    print(F"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.allclose(a__ ,						a__ ,						atol=1e-3   )
    print('''Do both models output the same tensors?''' ,						'''🔥''' if success else '''💩'''   )
    if not success:
        raise Exception('''Something went wRoNg'''   )

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


if __name__ == "__main__":
      a__				:      str    		=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--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.'''
      )
      a__				:      int    		=     parser.parse_args()
      convert_dataavec_checkpoint_to_pytorch(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )
