import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command






class   __lowercase       (     unittest.TestCase     ):
						lowercase       			=  inspect.getfile(accelerate.test_utils     )
						lowercase       			=  os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ['scripts', 'test_cli.py']     )

						lowercase       			=  ['accelerate', 'launch']
						lowercase       			=  Path.home() / '.cache/huggingface/accelerate'
						lowercase       			=  'default_config.yaml'
						lowercase       			=  config_folder / config_file
						lowercase       			=  config_folder / '_default_config.yaml'

						lowercase       			=  Path('tests/test_configs'     )

						@classmethod
						def 							__a		(						cls					:   int )				->		str:


									'''simple docstring'''

									if cls.config_path.is_file():
												cls.config_path.rename(cls.changed_path )

						@classmethod
						def 							__a		(						cls					:   Tuple )				->		Tuple:


									'''simple docstring'''

									if cls.changed_path.is_file():
												cls.changed_path.rename(cls.config_path )

						def 							__a		(						self					:   Tuple )				->		Any:


									'''simple docstring'''

									lowercase       		=		self.base_cmd
									if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
												cmd += ["--multi_gpu"]
									execute_subprocess_async(cmd + [self.test_file_path]				,				env=os.environ.copy() )

						def 							__a		(						self					:   Dict )				->		Any:


									'''simple docstring'''

									for config in sorted(self.test_config_path.glob('''**/*.yaml''' ) ):
												with self.subTest(config_file=UpperCamelCase_ ):
															execute_subprocess_async(
															    self.base_cmd + ['''--config_file''', str(UpperCamelCase_ ), self.test_file_path]				,				env=os.environ.copy() )



						def 							__a		(						self					:   Dict )				->		int:


									'''simple docstring'''

									execute_subprocess_async(['''accelerate''', '''test''']				,				env=os.environ.copy() )






class   __lowercase       (     unittest.TestCase     ):
						lowercase       			=  'test-tpu'
						lowercase       			=  'us-central1-a'
						lowercase       			=  'ls'
						lowercase       			=  ['accelerate', 'tpu-config']
						lowercase       			=  'cd /usr/share'
						lowercase       			=  'tests/test_samples/test_command_file.sh'
						lowercase       			=  'Running gcloud compute tpus tpu-vm ssh'

						def 							__a		(						self					:   Tuple )				->		Any:


									'''simple docstring'''

									lowercase       		=		run_command(
									    self.cmd
									    + ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug''']				,				return_stdout=UpperCamelCase_				,				)
									self.assertIn(
									    f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'				,				UpperCamelCase_				,				)

						def 							__a		(						self					:   List[Any] )				->		Optional[int]:


									'''simple docstring'''

									lowercase       		=		run_command(
									    self.cmd
									    + [
									        '''--config_file''',
									        '''tests/test_configs/0_12_0.yaml''',
									        '''--command''',
									        self.command,
									        '''--tpu_zone''',
									        self.tpu_zone,
									        '''--tpu_name''',
									        self.tpu_name,
									        '''--debug''',
									    ]				,				return_stdout=UpperCamelCase_				,				)
									self.assertIn(
									    f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'				,				UpperCamelCase_				,				)

						def 							__a		(						self					:   Any )				->		List[str]:


									'''simple docstring'''

									lowercase       		=		run_command(
									    self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug''']				,				return_stdout=UpperCamelCase_ )
									self.assertIn(
									    f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all'				,				UpperCamelCase_				,				)

						def 							__a		(						self					:   int )				->		Tuple:


									'''simple docstring'''

									lowercase       		=		run_command(
									    self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug''']				,				return_stdout=UpperCamelCase_				,				)
									self.assertIn(
									    f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'				,				UpperCamelCase_				,				)

						def 							__a		(						self					:   Optional[Any] )				->		List[str]:


									'''simple docstring'''

									lowercase       		=		run_command(
									    self.cmd
									    + [
									        '''--config_file''',
									        '''tests/test_configs/latest.yaml''',
									        '''--command''',
									        self.command,
									        '''--command''',
									        '''echo \"Hello World\"''',
									        '''--debug''',
									    ]				,				return_stdout=UpperCamelCase_				,				)
									self.assertIn(
									    f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all'				,				UpperCamelCase_				,				)

						def 							__a		(						self					:   Tuple )				->		Tuple:


									'''simple docstring'''

									lowercase       		=		run_command(
									    self.cmd
									    + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug''']				,				return_stdout=UpperCamelCase_				,				)
									self.assertIn(
									    f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all'				,				UpperCamelCase_				,				)

						def 							__a		(						self					:   int )				->		str:


									'''simple docstring'''

									lowercase       		=		run_command(
									    self.cmd
									    + [
									        '''--config_file''',
									        '''tests/test_configs/0_12_0.yaml''',
									        '''--command_file''',
									        self.command_file,
									        '''--tpu_zone''',
									        self.tpu_zone,
									        '''--tpu_name''',
									        self.tpu_name,
									        '''--debug''',
									    ]				,				return_stdout=UpperCamelCase_				,				)
									self.assertIn(
									    f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all'				,				UpperCamelCase_				,				)

						def 							__a		(						self					:   Union[str, Any] )				->		Optional[int]:


									'''simple docstring'''

									lowercase       		=		run_command(
									    self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug''']				,				return_stdout=UpperCamelCase_				,				)
									self.assertIn(
									    f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all'				,				UpperCamelCase_				,				)



						def 							__a		(						self					:   Optional[int] )				->		List[Any]:


									'''simple docstring'''

									lowercase       		=		run_command(
									    self.cmd
									    + [
									        '''--config_file''',
									        '''tests/test_configs/latest.yaml''',
									        '''--install_accelerate''',
									        '''--accelerate_version''',
									        '''12.0.0''',
									        '''--debug''',
									    ]				,				return_stdout=UpperCamelCase_				,				)
									self.assertIn(
									    f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all'				,				UpperCamelCase_				,				)













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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
  raise Exception("requires fairseq >= 1.0.0a")

logging.set_verbosity_info()
__a   :							Union[str, Any]						= logging.get_logger(__name__)

__a   :							Tuple						= "Hello world! cécé herlolip"



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      str      ,					__lowercase :      bool     )    ->   List[Any]:





    """simple docstring"""

    __A       =						FairseqRobertaModel.from_pretrained(__lowercase     )
    roberta.eval()  # disable dropout
    __A       =						roberta.model.encoder.sentence_encoder
    __A       =						XLMRobertaConfig(
        vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings      ,					hidden_size=roberta.cfg.model.encoder_embed_dim      ,					num_hidden_layers=roberta.cfg.model.encoder_layers      ,					num_attention_heads=roberta.cfg.model.encoder_attention_heads      ,					intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim      ,					max_position_embeddings=5_1_4      ,					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 RoBERTa config:"""      ,					__lowercase     )

    __A       =						XLMRobertaXLForSequenceClassification(__lowercase     ) if classification_head else XLMRobertaXLForMaskedLM(__lowercase     )
    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.layer_norm.weight
    __A       =						roberta_sent_encoder.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]

        __A       =						layer.attention
        __A       =						roberta_layer.self_attn_layer_norm.weight
        __A       =						roberta_layer.self_attn_layer_norm.bias

        # 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

        # this one is final layer norm
        __A       =						roberta_layer.final_layer_norm.weight
        __A       =						roberta_layer.final_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
        # 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(__lowercase     ).unsqueeze(0     )  # batch of size 1

    __A       =						model(__lowercase     )[0]
    if classification_head:
        __A       =						roberta.model.classification_heads["""mnli"""](roberta.extract_features(__lowercase     )     )
    else:
        __A       =						roberta.model(__lowercase     )[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(__lowercase      ,					__lowercase      ,					atol=1E-3     )
    print("""Do both models output the same tensors?"""      ,					"""🔥""" if success else """💩"""     )
    if not success:
        raise Exception("""Something went wRoNg"""     )

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


if __name__ == "__main__":
  __a   :							List[Any]						= 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."
  )
  __a   :							Dict						= parser.parse_args()
  convert_xlm_roberta_xl_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )




