


import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A				:       Dict      =  abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						str:


						'''simple docstring'''






						config.addinivalue_line(
						    "markers"   ,						"is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"			)
						config.addinivalue_line(
						    "markers"   ,						"is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"			)
						config.addinivalue_line("markers"   ,						"is_pipeline_test: mark test to run only when pipelines are tested"			)
						config.addinivalue_line("markers"   ,						"is_staging_test: mark test to run only in the staging environment"			)
						config.addinivalue_line("markers"   ,						"accelerate_tests: mark test that require accelerate"			)
						config.addinivalue_line("markers"   ,						"tool_tests: mark the tool tests that are run on their specific schedule"			)





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						List[str]:


						'''simple docstring'''






						from transformers.testing_utils import pytest_addoption_shared

						pytest_addoption_shared(_lowerCamelCase			)





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						int:


						'''simple docstring'''






						from transformers.testing_utils import pytest_terminal_summary_main

						__snake_case				=		terminalreporter.config.getoption("--make-reports"			)
						if make_reports:
												pytest_terminal_summary_main(_lowerCamelCase   ,						id=_lowerCamelCase			)





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[str]:


						'''simple docstring'''






						if exitstatus == 5:
												__snake_case				=		0


# Doctest custom flag to ignore output.
A				:       Any      =  doctest.register_optionflag('IGNORE_RESULT')

A				:       int      =  doctest.OutputChecker






class      UpperCamelCase(	__lowercase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		Dict  ,     SCREAMING_SNAKE_CASE						:		List[str]    )      ->						Optional[int]:




											'''simple docstring'''

											if IGNORE_RESULT & optionflags:
																	return True
											return OutputChecker.check_output(self  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )




A				:       Optional[int]      =  CustomOutputChecker
A				:       Dict      =  HfDoctestModule


A				:       List[str]      =  HfDocTestParser












'''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)


