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
lowerCamelCase_    :		Dict							=	{
    "/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(     __lowerCamelCase						):
     # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
     # the original model
     __a		 =       list(s_dict.keys()						)
     for key in keys:
          __a		 =       r'''.*/layers_(\d+)'''
          __a		 =       key
          if re.match(__lowerCamelCase				,    __lowerCamelCase						):
               __a		 =       re.sub(r'layers_(\d+)'				,    r'block/\1/layer'				,    __lowerCamelCase						)

          __a		 =       r'''(encoder|decoder)\/'''

          if re.match(__lowerCamelCase				,    __lowerCamelCase						):
               __a		 =       re.match(__lowerCamelCase				,    __lowerCamelCase						).groups()
               if groups[0] == "encoder":
                    __a		 =       re.sub(r'/mlp/'				,    r'/1/mlp/'				,    __lowerCamelCase						)
                    __a		 =       re.sub(r'/pre_mlp_layer_norm/'				,    r'/1/layer_norm/'				,    __lowerCamelCase						)

               elif groups[0] == "decoder":
                    __a		 =       re.sub(r'/mlp/'				,    r'/2/mlp/'				,    __lowerCamelCase						)
                    __a		 =       re.sub(r'/pre_mlp_layer_norm/'				,    r'/2/layer_norm/'				,    __lowerCamelCase						)

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

          print(f'''{key} -> {new_key}'''						)
          __a		 =       s_dict.pop(__lowerCamelCase						)

     if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
          __a		 =       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:
          __a		 =       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:
               __a		 =       s_dict[key].shape[0]
               __a		 =       s_dict[key]
               for idx in range(__lowerCamelCase						):
                    __a		 =       expert_weihts[idx]
                    print(f'''{key} -> {key.replace("expert/"				,    "nested fstring"						)}'''						)

               s_dict.pop(__lowerCamelCase						)

     return s_dict


lowerCamelCase_    :		List[Any]							=	{
    "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(     __lowerCamelCase				,    __lowerCamelCase						):
     # Convert a google style config to the hugging face fromat
     import regex as re

     with open(__lowerCamelCase				,    'r'						) as f:
          __a		 =       f.read()

     __a		 =       re.findall(r'(.*) = ([0-9.]*)'				,    __lowerCamelCase						)
     __a		 =       {}
     for param, value in regex_match:
          if param in GIN_TO_CONFIG_MAPPING and value != "":
               __a		 =       float(__lowerCamelCase						) if '''.''' in value else int(__lowerCamelCase						)

     __a		 =       re.findall(r'(.*activations) = \(\'(.*)\',\)'				,    __lowerCamelCase						)[0]
     __a		 =       str(activation[1]						)

     __a		 =       num_experts
     __a		 =       SwitchTransformersConfig(**__lowerCamelCase						)
     return config
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase=None				,    __lowerCamelCase="./"				,    __lowerCamelCase=8						):
     # Initialise PyTorch model

     print(f'''Loading flax weights from : {flax_checkpoint_path}'''						)
     __a		 =       checkpoints.load_tax_checkpoint(__lowerCamelCase						)

     if gin_file is not None:
          __a		 =       convert_gin_to_config(__lowerCamelCase				,    __lowerCamelCase						)
     else:
          __a		 =       SwitchTransformersConfig.from_pretrained(__lowerCamelCase						)

     __a		 =       SwitchTransformersForConditionalGeneration(__lowerCamelCase						)

     __a		 =       flax_params['''target''']
     __a		 =       flatten_dict(__lowerCamelCase				,    sep='/'						)
     __a		 =       rename_keys(__lowerCamelCase						)
     __a		 =       unflatten_dict(__lowerCamelCase				,    sep='/'						)

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

     print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
     pt_model.save_pretrained(__lowerCamelCase						)


if __name__ == "__main__":
 lowerCamelCase_    :		Dict							=	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""")
 lowerCamelCase_    :		Optional[Any]							=	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,
 )





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf

	from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments


@require_tf
class 						a__							(       unittest.TestCase		):



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Optional[Any]:
								for model_result in results.values():
													for batch_size, sequence_length in zip(model_result['bs']				,						model_result['ss']   ):
																		__a		 =       model_result['result'][batch_size][sequence_length]
																		self.assertIsNotNone(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sgugger/tiny-distilbert-classification'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						only_pretrain_model=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result   )
								self.check_results_dict_not_empty(results.memory_train_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result   )
								self.check_results_dict_not_empty(results.memory_train_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       'patrickvonplaten/t5-tiny-random'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						configs=[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'   )   ) == 0				,						'Cannot do xla on CPU.'   )
			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						use_xla=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       'sshleifer/tiny-gpt2'
								with tempfile.TemporaryDirectory() as tmp_dir:
													__a		 =       TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,						inference=UpperCAmelCase				,						save_to_csv=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						inference_time_csv_file=os.path.join(UpperCAmelCase				,						'inf_time.csv'   )				,						inference_memory_csv_file=os.path.join(UpperCAmelCase				,						'inf_mem.csv'   )				,						env_info_csv_file=os.path.join(UpperCAmelCase				,						'env.csv'   )				,						multi_process=UpperCAmelCase				,						)
													__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
													benchmark.run()
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'inf_time.csv'   )   ).exists()   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'inf_mem.csv'   )   ).exists()   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'env.csv'   )   ).exists()   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       'sshleifer/tiny-gpt2'

								def _check_summary_is_not_empty(UpperCAmelCase   ):
													self.assertTrue(hasattr(UpperCAmelCase				,						'sequential'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'cumulative'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'current'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'total'   )   )

								with tempfile.TemporaryDirectory() as tmp_dir:
													__a		 =       TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						log_filename=os.path.join(UpperCAmelCase				,						'log.txt'   )				,						log_print=UpperCAmelCase				,						trace_memory_line_by_line=UpperCAmelCase				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
													__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
													__a		 =       benchmark.run()
													_check_summary_is_not_empty(result.inference_summary   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'log.txt'   )   ).exists()   )




