


"""simple docstring"""



import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
__snake_case             =   logging.getLogger()







def   __lowerCAmelCase						(						lowercase  :  Path						,							lowercase  :  list			)     ->						List[str]:


				"""simple docstring"""





				snake_case   :				Union[str, Any]				 =   '''\n'''.join(lowercase			)
				Path(lowercase			).open("w"			).writelines(lowercase			)


__snake_case             =   "patrickvonplaten/t5-tiny-random"
__snake_case             =   "sshleifer/bart-tiny-random"
__snake_case             =   "sshleifer/tiny-mbart"

__snake_case             =   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







class _lowerCAmelCase    (				__lowerCAmelCase     ):







			def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		List[str]:

							'''simple docstring'''




							snake_case   :				Optional[Any]				 =   Path(self.get_auto_remove_tmp_dir()						) / '''utest_input.source'''
							snake_case   :				Any				 =   input_file_name.parent / '''utest_output.txt'''
							assert not output_file_name.exists()
							snake_case   :				Any				 =   [''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
							_dump_articles(lowerCamelCase__						,     lowerCamelCase__						)

							snake_case   :				Optional[int]				 =   str(Path(self.get_auto_remove_tmp_dir()						) / "scores.json"						)
							snake_case   :				Optional[int]				 =   '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
							snake_case   :				Optional[Any]				 =   F'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

							with patch.object(lowerCamelCase__						,     "argv"						,     lowerCamelCase__						):
											run_generate()
											assert Path(lowerCamelCase__						).exists()
											# os.remove(Path(output_file_name))







			def 			lowerCamelCase		(     self						)     ->		Dict:

							'''simple docstring'''




							self.run_eval_tester(lowerCamelCase__						)







			@parameterized.expand([BART_TINY, MBART_TINY]						)
			@slow
			def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Optional[int]:

							'''simple docstring'''




							self.run_eval_tester(lowerCamelCase__						)







			@parameterized.expand([T5_TINY, MBART_TINY]						)
			@slow
			def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Tuple:

							'''simple docstring'''




							snake_case   :				List[str]				 =   Path(self.get_auto_remove_tmp_dir()						) / '''utest_input.source'''
							snake_case   :				Optional[Any]				 =   input_file_name.parent / '''utest_output.txt'''
							assert not output_file_name.exists()

							snake_case   :				Dict				 =   {
							    '''en''': ['''Machine learning is great, isn\'t it?''', '''I like to eat bananas''', '''Tomorrow is another great day!'''],
							    '''de''': [
							        '''Maschinelles Lernen ist großartig, oder?''',
							        '''Ich esse gerne Bananen''',
							        '''Morgen ist wieder ein toller Tag!''',
							    ],
							}

							snake_case   :				Dict				 =   Path(self.get_auto_remove_tmp_dir()						)
							snake_case   :				List[Any]				 =   str(tmp_dir / "scores.json"						)
							snake_case   :				Dict				 =   str(tmp_dir / "val.target"						)
							_dump_articles(lowerCamelCase__						,     text["en"]						)
							_dump_articles(lowerCamelCase__						,     text["de"]						)
							snake_case   :				Tuple				 =   '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
							snake_case   :				Union[str, Any]				 =   F'\n            run_eval_search.py\n            {model}\n            {str(lowerCamelCase__						)}\n            {str(lowerCamelCase__						)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
							testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"]						)

							with patch.object(lowerCamelCase__						,     "argv"						,     lowerCamelCase__						):
											with CaptureStdout() as cs:
															run_search()
											snake_case   :				Union[str, Any]				 =   [''' num_beams | length_penalty''', model, '''Best score args''']
											snake_case   :				int				 =   ['''Info''']
											if "translation" in task:
															expected_strings.append("bleu"						)
											else:
															expected_strings.extend(lowerCamelCase__						)
											for w in expected_strings:
															assert w in cs.out
											for w in un_expected_strings:
															assert w not in cs.out
											assert Path(lowerCamelCase__						).exists()
											os.remove(Path(lowerCamelCase__						)						)









"""simple docstring"""



import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
__snake_case             =   {
    """/attention/""": """/0/SelfAttention/""",
    """/self_attention/""": """/0/SelfAttention/""",
    """/encoder_decoder_attention/""": """/1/EncDecAttention/""",
    """value""": """v""",
    """query""": """q""",
    """key""": """k""",
    """out""": """o""",
    """pre_self_attention_layer_norm""": """0/layer_norm""",
    """pre_cross_attention_layer_norm""": """1/layer_norm""",
    """pre_attention_layer_norm""": """0/layer_norm""",  # previously 1, but seems wrong
    """token_embedder""": """shared""",
    """encoder_norm""": """final_layer_norm""",
    """decoder_norm""": """final_layer_norm""",
    """relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
    """router/router_weights/w/""": """router/classifier/""",
    """roer/roer_weights/w/""": """router/classifier/""",
    """logits_dense""": """lm_head""",
}







def   __lowerCAmelCase						(						lowercase  :  Optional[int]			)     ->						List[str]:


    """simple docstring"""





    snake_case   :				Optional[Any]				 =   list(s_dict.keys()			)
    for key in keys:
        snake_case   :				Any				 =   R".*/layers_(\d+)"
        snake_case   :				Tuple				 =   key
        if re.match(lowercase						,							lowercase			):
            snake_case   :				List[str]				 =   re.sub(R"layers_(\d+)"						,							R"block/\1/layer"						,							lowercase			)

        snake_case   :				Union[str, Any]				 =   R"(encoder|decoder)\/"

        if re.match(lowercase						,							lowercase			):
            snake_case   :				Any				 =   re.match(lowercase						,							lowercase			).groups()
            if groups[0] == "encoder":
                snake_case   :				Union[str, Any]				 =   re.sub(R"/mlp/"						,							R"/1/mlp/"						,							lowercase			)
                snake_case   :				int				 =   re.sub(R"/pre_mlp_layer_norm/"						,							R"/1/layer_norm/"						,							lowercase			)

            elif groups[0] == "decoder":
                snake_case   :				str				 =   re.sub(R"/mlp/"						,							R"/2/mlp/"						,							lowercase			)
                snake_case   :				List[str]				 =   re.sub(R"/pre_mlp_layer_norm/"						,							R"/2/layer_norm/"						,							lowercase			)

        # 2. Convert other classic mappings
        for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
            if old_key in new_key:
                snake_case   :				int				 =   new_key.replace(lowercase						,							lowercase			)

        print(F'{key} -> {new_key}'			)
        snake_case   :				Optional[Any]				 =   s_dict.pop(lowercase			)

    if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        snake_case   :				int				 =   s_dict[
            "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
        ].T
    if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        snake_case   :				Optional[Any]				 =   s_dict[
            "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
        ].T

    # 3. Take extra care of the EXPERTS layer
    for key in list(s_dict.keys()			):
        if "expert" in key:
            snake_case   :				Tuple				 =   s_dict[key].shape[0]
            snake_case   :				int				 =   s_dict[key]
            for idx in range(lowercase			):
                snake_case   :				List[str]				 =   expert_weihts[idx]
                print(F'{key} -> {key.replace("expert/"						,							"nested fstring"			)}'			)

            s_dict.pop(lowercase			)

    return s_dict


__snake_case             =   {
    """NUM_ENCODER_LAYERS""": """num_layers""",
    """NUM_DECODER_LAYERS""": """num_decoder_layers""",
    """NUM_HEADS""": """num_heads""",
    """HEAD_DIM""": """d_kv""",
    """EMBED_DIM""": """d_model""",
    """MLP_DIM""": """d_ff""",
    """NUM_SELECTED_EXPERTS""": """num_selected_experts""",
    """NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
    """NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
    """dense.MlpBlock.activations""": """feed_forward_proj""",
}







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Optional[Any]			)     ->						int:


    """simple docstring"""





    import regex as re

    with open(lowercase						,							"r"			) as f:
        snake_case   :				List[str]				 =   f.read()

    snake_case   :				Tuple				 =   re.findall(R"(.*) = ([0-9.]*)"						,							lowercase			)
    snake_case   :				Any				 =   {}
    for param, value in regex_match:
        if param in GIN_TO_CONFIG_MAPPING and value != "":
            snake_case   :				Tuple				 =   float(lowercase			) if "." in value else int(lowercase			)

    snake_case   :				List[str]				 =   re.findall(R"(.*activations) = \(\'(.*)\',\)"						,							lowercase			)[0]
    snake_case   :				List[Any]				 =   str(activation[1]			)

    snake_case   :				Optional[Any]				 =   num_experts
    snake_case   :				List[Any]				 =   SwitchTransformersConfig(**lowercase			)
    return config







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  Tuple						,							lowercase  :  Union[str, Any]=None						,							lowercase  :  Any="./"						,							lowercase  :  int=8			)     ->						Dict:


    """simple docstring"""





    print(F'Loading flax weights from : {flax_checkpoint_path}'			)
    snake_case   :				Union[str, Any]				 =   checkpoints.load_tax_checkpoint(lowercase			)

    if gin_file is not None:
        snake_case   :				List[str]				 =   convert_gin_to_config(lowercase						,							lowercase			)
    else:
        snake_case   :				str				 =   SwitchTransformersConfig.from_pretrained(lowercase			)

    snake_case   :				Any				 =   SwitchTransformersForConditionalGeneration(lowercase			)

    snake_case   :				Optional[Any]				 =   flax_params["target"]
    snake_case   :				Optional[int]				 =   flatten_dict(lowercase						,							sep="/"			)
    snake_case   :				Optional[Any]				 =   rename_keys(lowercase			)
    snake_case   :				List[str]				 =   unflatten_dict(lowercase						,							sep="/"			)

    # Load the flax params in the PT model
    load_flax_weights_in_pytorch_model(lowercase						,							lowercase			)

    print(F'Save PyTorch model to {pytorch_dump_path}'			)
    pt_model.save_pretrained(lowercase			)


if __name__ == "__main__":
  __snake_case             =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--switch_t5x_checkpoint_path""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"""
          """ model architecture. If not provided, a `gin_file` has to be provided."""
      ),
  )
  parser.add_argument(
      """--gin_file""",
      default=None,
      type=str,
      required=False,
      help="""Path to the gin config file. If not provided, a `config_file` has to be passed   """,
  )
  parser.add_argument(
      """--config_name""", default=None, type=str, required=False, help="""Config name of SwitchTransformers model."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output pytorch model."""
  )
  parser.add_argument("""--num_experts""", default=8, type=int, required=False, help="""Number of experts""")
  __snake_case             =   parser.parse_args()
  convert_flax_checkpoint_to_pytorch(
      args.switch_tax_checkpoint_path,
      args.config_name,
      args.gin_file,
      args.pytorch_dump_folder_path,
      args.num_experts,
  )





