





'''simple docstring'''






import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu




class  _A (					nn.Module		):

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


					"""simple docstring"""

					super().__init__()
					__snake_case					:      List[Any]							      =     nn.Linear(3  ,      4			)
					__snake_case					:      str							      =     nn.BatchNormad(4			)
					__snake_case					:      Optional[Any]							      =     nn.Linear(4  ,      5			)

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   Dict			)					->    List[str]:


					"""simple docstring"""

					return self.lineara(self.batchnorm(self.lineara(__magic_name__			)			)			)





class  _A (					__lowercase		):

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   Tuple  ,      *__magic_name__					:   Dict  ,      **__magic_name__					:   Optional[Any]			)					->    Tuple:


					"""simple docstring"""

					return (args[0] + 1,) + args[1:], kwargs





class  _A (					__lowercase		):

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   Tuple			)					->    Union[str, Any]:


					"""simple docstring"""

					return output + 1





class  _A (					unittest.TestCase		):

		def 	lowercase__     (   self					:   Dict			)					->    Any:


					"""simple docstring"""

					__snake_case					:      int							      =     ModelForTest()
					__snake_case					:      Tuple							      =     ModelHook()

					add_hook_to_module(__magic_name__  ,      __magic_name__			)
					self.assertEqual(test_model._hf_hook  ,      __magic_name__			)
					self.assertTrue(hasattr(__magic_name__  ,      """_old_forward"""			)			)

					# Check adding the hook did not change the name or the signature
					self.assertEqual(test_model.forward.__name__  ,      """forward"""			)
					self.assertListEqual(list(inspect.signature(test_model.forward			).parameters			)  ,      ["""x"""]			)

					remove_hook_from_module(__magic_name__			)
					self.assertFalse(hasattr(__magic_name__  ,      """_hf_hook"""			)			)
					self.assertFalse(hasattr(__magic_name__  ,      """_old_forward"""			)			)

		def 	lowercase__     (   self					:   Tuple			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     ModelForTest()
					__snake_case					:      Optional[int]							      =     ModelHook()

					add_hook_to_module(__magic_name__  ,      __magic_name__			)
					add_hook_to_module(__magic_name__  ,      __magic_name__  ,      append=__magic_name__			)

					self.assertEqual(isinstance(test_model._hf_hook  ,      __magic_name__			)  ,      __magic_name__			)
					self.assertEqual(len(test_model._hf_hook.hooks			)  ,      2			)
					self.assertTrue(hasattr(__magic_name__  ,      """_old_forward"""			)			)

					# Check adding the hook did not change the name or the signature
					self.assertEqual(test_model.forward.__name__  ,      """forward"""			)
					self.assertListEqual(list(inspect.signature(test_model.forward			).parameters			)  ,      ["""x"""]			)

					remove_hook_from_module(__magic_name__			)
					self.assertFalse(hasattr(__magic_name__  ,      """_hf_hook"""			)			)
					self.assertFalse(hasattr(__magic_name__  ,      """_old_forward"""			)			)

		def 	lowercase__     (   self					:   str			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     ModelForTest()
					__snake_case					:      Any							      =     torch.randn(2  ,      3			)
					__snake_case					:      str							      =     test_model(x + 1			)
					__snake_case					:      int							      =     test_model(x + 2			)

					__snake_case					:      Union[str, Any]							      =     PreForwardHook()
					add_hook_to_module(__magic_name__  ,      __magic_name__			)
					__snake_case					:      int							      =     test_model(__magic_name__			)
					self.assertTrue(torch.allclose(__magic_name__  ,      __magic_name__  ,      atol=1E-5			)			)

					# Attaching a hook to a model when it already has one replaces, does not chain
					__snake_case					:      Optional[int]							      =     PreForwardHook()
					add_hook_to_module(__magic_name__  ,      __magic_name__			)
					__snake_case					:      List[Any]							      =     test_model(__magic_name__			)
					self.assertTrue(torch.allclose(__magic_name__  ,      __magic_name__  ,      atol=1E-5			)			)

					# You need to use the sequential hook to chain two or more hooks
					__snake_case					:      Optional[int]							      =     SequentialHook(PreForwardHook()  ,      PreForwardHook()			)
					add_hook_to_module(__magic_name__  ,      __magic_name__			)

					__snake_case					:      List[str]							      =     test_model(__magic_name__			)
					assert torch.allclose(__magic_name__  ,      __magic_name__  ,      atol=1E-5			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     ModelForTest()
					__snake_case					:      str							      =     torch.randn(2  ,      3			)
					__snake_case					:      Any							      =     test_model(__magic_name__			)

					__snake_case					:      Any							      =     PostForwardHook()
					add_hook_to_module(__magic_name__  ,      __magic_name__			)
					__snake_case					:      Any							      =     test_model(__magic_name__			)
					self.assertTrue(torch.allclose(__magic_name__  ,      output + 1  ,      atol=1E-5			)			)

					# Attaching a hook to a model when it already has one replaces, does not chain
					__snake_case					:      Any							      =     PostForwardHook()
					add_hook_to_module(__magic_name__  ,      __magic_name__			)
					__snake_case					:      Dict							      =     test_model(__magic_name__			)
					self.assertTrue(torch.allclose(__magic_name__  ,      output + 1  ,      atol=1E-5			)			)

					# You need to use the sequential hook to chain two or more hooks
					__snake_case					:      str							      =     SequentialHook(PostForwardHook()  ,      PostForwardHook()			)
					add_hook_to_module(__magic_name__  ,      __magic_name__			)

					__snake_case					:      int							      =     test_model(__magic_name__			)
					assert torch.allclose(__magic_name__  ,      output + 2  ,      atol=1E-5			)

		def 	lowercase__     (   self					:   str			)					->    int:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     ModelForTest()
					__snake_case					:      int							      =     torch.randn(2  ,      3			)
					__snake_case					:      Any							      =     test_model(__magic_name__			)

					__snake_case					:      Dict							      =     PostForwardHook()
					add_hook_to_module(__magic_name__  ,      __magic_name__			)
					__snake_case					:      List[Any]							      =     test_model(__magic_name__			)
					self.assertTrue(torch.allclose(__magic_name__  ,      output + 1			)			)
					self.assertTrue(outputa.requires_grad			)

					__snake_case					:      Dict							      =     True
					__snake_case					:      int							      =     test_model(__magic_name__			)
					self.assertFalse(outputa.requires_grad			)

		@require_multi_gpu
		def 	lowercase__     (   self					:   Tuple			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     ModelForTest()
					# Everything is on CPU
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)

					# This will move each submodule on different devices
					add_hook_to_module(model.lineara  ,      AlignDevicesHook(execution_device=0			)			)
					add_hook_to_module(model.batchnorm  ,      AlignDevicesHook(execution_device=0			)			)
					add_hook_to_module(model.lineara  ,      AlignDevicesHook(execution_device=1			)			)

					self.assertEqual(model.lineara.weight.device  ,      torch.device(0			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device(0			)			)
					self.assertEqual(model.batchnorm.running_mean.device  ,      torch.device(0			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device(1			)			)

					# We can still make a forward pass. The input does not need to be on any particular device
					__snake_case					:      Tuple							      =     torch.randn(2  ,      3			)
					__snake_case					:      Union[str, Any]							      =     model(__magic_name__			)
					self.assertEqual(output.device  ,      torch.device(1			)			)

					# We can add a general hook to put back output on same device as input.
					add_hook_to_module(__magic_name__  ,      AlignDevicesHook(io_same_device=__magic_name__			)			)
					__snake_case					:      Tuple							      =     torch.randn(2  ,      3			).to(0			)
					__snake_case					:      Any							      =     model(__magic_name__			)
					self.assertEqual(output.device  ,      torch.device(0			)			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    str:


					"""simple docstring"""

					__snake_case					:      int							      =     ModelForTest()

					# Everything is on CPU
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)

					# This will move each submodule on different devices
					__snake_case					:      List[str]							      =     {"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True}

					add_hook_to_module(model.lineara  ,      AlignDevicesHook(**__magic_name__			)			)
					add_hook_to_module(model.batchnorm  ,      AlignDevicesHook(**__magic_name__			)			)
					add_hook_to_module(model.lineara  ,      AlignDevicesHook(**__magic_name__			)			)

					# Parameters have been offloaded, so on the meta device
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""meta"""			)			)
					# Buffers are not included in the offload by default, so are on the execution device
					__snake_case					:      Any							      =     torch.device(hook_kwargs["""execution_device"""]			)
					self.assertEqual(model.batchnorm.running_mean.device  ,      __magic_name__			)

					__snake_case					:      Dict							      =     torch.randn(2  ,      3			)
					__snake_case					:      Any							      =     model(__magic_name__			)
					self.assertEqual(output.device  ,      __magic_name__			)

					# Removing hooks loads back the weights in the model.
					remove_hook_from_module(model.lineara			)
					remove_hook_from_module(model.batchnorm			)
					remove_hook_from_module(model.lineara			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)

					# Now test with buffers included in the offload
					__snake_case					:      int							      =     {
					    """execution_device""": 0 if torch.cuda.is_available() else """cpu""",
					    """offload""": True,
					    """offload_buffers""": True,
					}

					add_hook_to_module(model.lineara  ,      AlignDevicesHook(**__magic_name__			)			)
					add_hook_to_module(model.batchnorm  ,      AlignDevicesHook(**__magic_name__			)			)
					add_hook_to_module(model.lineara  ,      AlignDevicesHook(**__magic_name__			)			)

					# Parameters have been offloaded, so on the meta device, buffers included
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.batchnorm.running_mean.device  ,      torch.device("""meta"""			)			)

					__snake_case					:      str							      =     torch.randn(2  ,      3			)
					__snake_case					:      str							      =     model(__magic_name__			)
					self.assertEqual(output.device  ,      __magic_name__			)

					# Removing hooks loads back the weights in the model.
					remove_hook_from_module(model.lineara			)
					remove_hook_from_module(model.batchnorm			)
					remove_hook_from_module(model.lineara			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)

		def 	lowercase__     (   self					:   Dict			)					->    str:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     ModelForTest()

					# Everything is on CPU
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)

					# This will move each submodule on different devices
					__snake_case					:      Union[str, Any]							      =     0 if torch.cuda.is_available() else """cpu"""
					attach_align_device_hook(__magic_name__  ,      execution_device=__magic_name__  ,      offload=__magic_name__			)

					# Parameters have been offloaded, so on the meta device
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""meta"""			)			)
					# Buffers are not included in the offload by default, so are on the execution device
					__snake_case					:      Union[str, Any]							      =     torch.device(__magic_name__			)
					self.assertEqual(model.batchnorm.running_mean.device  ,      __magic_name__			)

					__snake_case					:      Optional[int]							      =     torch.randn(2  ,      3			)
					__snake_case					:      Dict							      =     model(__magic_name__			)
					self.assertEqual(output.device  ,      __magic_name__			)

					# Removing hooks loads back the weights in the model.
					remove_hook_from_submodules(__magic_name__			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)

					# Now test with buffers included in the offload
					attach_align_device_hook(__magic_name__  ,      execution_device=__magic_name__  ,      offload=__magic_name__  ,      offload_buffers=__magic_name__			)

					# Parameters have been offloaded, so on the meta device, buffers included
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.batchnorm.running_mean.device  ,      torch.device("""meta"""			)			)

					__snake_case					:      Dict							      =     torch.randn(2  ,      3			)
					__snake_case					:      Optional[int]							      =     model(__magic_name__			)
					self.assertEqual(output.device  ,      __magic_name__			)

					# Removing hooks loads back the weights in the model.
					remove_hook_from_submodules(__magic_name__			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)

		def 	lowercase__     (   self					:   Any			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     ModelForTest()

					# Everything is on CPU
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)

					# This will move each submodule on different devices
					__snake_case					:      str							      =     0 if torch.cuda.is_available() else """cpu"""
					attach_align_device_hook(
					    __magic_name__  ,      execution_device=__magic_name__  ,      offload=__magic_name__  ,      weights_map=model.state_dict()			)

					# Parameters have been offloaded, so on the meta device
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""meta"""			)			)
					# Buffers are not included in the offload by default, so are on the execution device
					__snake_case					:      List[str]							      =     torch.device(__magic_name__			)
					self.assertEqual(model.batchnorm.running_mean.device  ,      __magic_name__			)

					__snake_case					:      Tuple							      =     torch.randn(2  ,      3			)
					__snake_case					:      Optional[Any]							      =     model(__magic_name__			)
					self.assertEqual(output.device  ,      __magic_name__			)

					# Removing hooks loads back the weights in the model.
					remove_hook_from_submodules(__magic_name__			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)

					# Now test with buffers included in the offload
					attach_align_device_hook(
					    __magic_name__  ,      execution_device=__magic_name__  ,      offload=__magic_name__  ,      weights_map=model.state_dict()  ,      offload_buffers=__magic_name__  ,      )

					# Parameters have been offloaded, so on the meta device, buffers included
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""meta"""			)			)
					self.assertEqual(model.batchnorm.running_mean.device  ,      torch.device("""meta"""			)			)

					__snake_case					:      List[str]							      =     torch.randn(2  ,      3			)
					__snake_case					:      Dict							      =     model(__magic_name__			)
					self.assertEqual(output.device  ,      __magic_name__			)

					# Removing hooks loads back the weights in the model.
					remove_hook_from_submodules(__magic_name__			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.batchnorm.weight.device  ,      torch.device("""cpu"""			)			)
					self.assertEqual(model.lineara.weight.device  ,      torch.device("""cpu"""			)			)









'''simple docstring'''






import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase					    =	logging.get_logger(__name__)

__UpperCamelCase					    =	[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 					_a						(       _lowerCamelCase ) ->				Union[str, Any]:



			"""simple docstring"""


			if k == "embeddings.weight":
						return "shared.weight"

			for parlai_name, hf_name in PATTERNS:
						__snake_case					:      List[str]							      =     k.replace(_lowerCamelCase    ,	_lowerCamelCase )

			if k.startswith("""encoder""" ):
						__snake_case					:      Optional[int]							      =     k.replace(""".attn"""    ,	""".self_attn""" )
						__snake_case					:      Tuple							      =     k.replace("""norm1"""    ,	"""self_attn_layer_norm""" )
						__snake_case					:      List[str]							      =     k.replace("""norm2"""    ,	"""final_layer_norm""" )
			elif k.startswith("""decoder""" ):
						__snake_case					:      List[Any]							      =     k.replace("""norm1"""    ,	"""self_attn_layer_norm""" )
						__snake_case					:      str							      =     k.replace("""norm2"""    ,	"""encoder_attn_layer_norm""" )
						__snake_case					:      Optional[int]							      =     k.replace("""norm3"""    ,	"""final_layer_norm""" )
			return k







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     [
			    """model.encoder.layernorm_embedding.weight""",
			    """model.encoder.layernorm_embedding.bias""",
			    """model.decoder.layernorm_embedding.weight""",
			    """model.decoder.layernorm_embedding.bias""",
			]
			for k in keys:
						__snake_case					:      Optional[Any]							      =     sd.pop(_lowerCamelCase )
						__snake_case					:      List[str]							      =     k.replace("""layernorm_embedding"""    ,	"""layer_norm""" )
						assert new_k not in sd
						__snake_case					:      Union[str, Any]							      =     v


__UpperCamelCase					    =	["START"]







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     torch.load(_lowerCamelCase    ,	map_location="""cpu""" )
			__snake_case					:      Dict							      =     model["""model"""]
			__snake_case					:      Optional[int]							      =     BlenderbotConfig.from_json_file(_lowerCamelCase )
			__snake_case					:      Union[str, Any]							      =     BlenderbotForConditionalGeneration(_lowerCamelCase )
			__snake_case					:      List[Any]							      =     m.model.state_dict().keys()
			__snake_case					:      int							      =     []
			__snake_case					:      Union[str, Any]							      =     {}
			for k, v in sd.items():
						if k in IGNORE_KEYS:
									continue

						__snake_case					:      Optional[int]							      =     rename_state_dict_key(_lowerCamelCase )
						if new_k not in valid_keys:
									failures.append([k, new_k] )
						else:
									__snake_case					:      str							      =     v
			if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
						rename_layernorm_keys(_lowerCamelCase )
			m.model.load_state_dict(_lowerCamelCase    ,	strict=_lowerCamelCase )
			m.half()
			m.save_pretrained(_lowerCamelCase )


if __name__ == "__main__":
	__UpperCamelCase					    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
	parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
	parser.add_argument(
	    "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
	)
	__UpperCamelCase					    =	parser.parse_args()
	convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


