'''simple docstring'''




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						(UnCLIPScheduler,)




      def      _A	(       self						:				List[Any]						,					**A						:				Optional[int]       ):
             _UpperCAmelCase     :  Dict							      =    {
                 "num_train_timesteps": 1000,
                 "variance_type": "fixed_small_log",
                 "clip_sample": True,
                 "clip_sample_range": 1.0,
                 "prediction_type": "epsilon",
             }

             config.update(**A       )
             return config




      def      _A	(       self						:				Any       ):
             for timesteps in [1, 5, 100, 1000]:
                    self.check_over_configs(num_train_timesteps=A       )




      def      _A	(       self						:				Any       ):
             for variance in ["fixed_small_log", "learned_range"]:
                    self.check_over_configs(variance_type=A       )




      def      _A	(       self						:				Tuple       ):
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=A       )




      def      _A	(       self						:				Optional[Any]       ):
             for clip_sample_range in [1, 5, 10, 20]:
                    self.check_over_configs(clip_sample_range=A       )




      def      _A	(       self						:				Any       ):
             for prediction_type in ["epsilon", "sample"]:
                    self.check_over_configs(prediction_type=A       )




      def      _A	(       self						:				Dict       ):
             for time_step in [0, 500, 999]:
                    for prev_timestep in [None, 5, 100, 250, 500, 750]:
                           if prev_timestep is not None and prev_timestep >= time_step:
                                  continue

                           self.check_over_forward(time_step=A						,					prev_timestep=A       )




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Tuple							      =    self.scheduler_classes[0]
             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_scheduler_config(variance_type="fixed_small_log"       )
             _UpperCAmelCase     :  Any							      =    scheduler_class(**A       )

             assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.00_00E-10       )       ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0_549_625       )       ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.9_994_987       )       ) < 1E-5




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Optional[Any]							      =    self.scheduler_classes[0]
             _UpperCAmelCase     :  Tuple							      =    self.get_scheduler_config(variance_type="learned_range"       )
             _UpperCAmelCase     :  int							      =    scheduler_class(**A       )

             _UpperCAmelCase     :  int							      =    0.5

             assert scheduler._get_variance(1						,					predicted_variance=A       ) - -10.1_712_790 < 1E-5
             assert scheduler._get_variance(487						,					predicted_variance=A       ) - -5.7_998_052 < 1E-5
             assert scheduler._get_variance(999						,					predicted_variance=A       ) - -0.0_010_011 < 1E-5




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  str							      =    self.scheduler_classes[0]
             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_scheduler_config()
             _UpperCAmelCase     :  Union[str, Any]							      =    scheduler_class(**A       )

             _UpperCAmelCase     :  Union[str, Any]							      =    scheduler.timesteps

             _UpperCAmelCase     :  int							      =    self.dummy_model()
             _UpperCAmelCase     :  Optional[int]							      =    self.dummy_sample_deter
             _UpperCAmelCase     :  Tuple							      =    torch.manual_seed(0       )

             for i, t in enumerate(A       ):
                    # 1. predict noise residual
                    _UpperCAmelCase     :  Dict							      =    model(A						,					A       )

                    # 2. predict previous mean of sample x_t-1
                    _UpperCAmelCase     :  List[str]							      =    scheduler.step(A						,					A						,					A						,					generator=A       ).prev_sample

                    _UpperCAmelCase     :  str							      =    pred_prev_sample

             _UpperCAmelCase     :  Union[str, Any]							      =    torch.sum(torch.abs(A       )       )
             _UpperCAmelCase     :  List[str]							      =    torch.mean(torch.abs(A       )       )

             assert abs(result_sum.item() - 252.2_682_495       ) < 1E-2
             assert abs(result_mean.item() - 0.3_284_743       ) < 1E-3




      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  List[Any]							      =    self.scheduler_classes[0]
             _UpperCAmelCase     :  Dict							      =    self.get_scheduler_config()
             _UpperCAmelCase     :  Dict							      =    scheduler_class(**A       )

             scheduler.set_timesteps(25       )

             _UpperCAmelCase     :  List[str]							      =    scheduler.timesteps

             _UpperCAmelCase     :  Union[str, Any]							      =    self.dummy_model()
             _UpperCAmelCase     :  List[str]							      =    self.dummy_sample_deter
             _UpperCAmelCase     :  Tuple							      =    torch.manual_seed(0       )

             for i, t in enumerate(A       ):
                    # 1. predict noise residual
                    _UpperCAmelCase     :  Any							      =    model(A						,					A       )

                    if i + 1 == timesteps.shape[0]:
                           _UpperCAmelCase     :  List[Any]							      =    None
                    else:
                           _UpperCAmelCase     :  Any							      =    timesteps[i + 1]

                    # 2. predict previous mean of sample x_t-1
                    _UpperCAmelCase     :  Union[str, Any]							      =    scheduler.step(
                        A						,					A						,					A						,					prev_timestep=A						,					generator=A       ).prev_sample

                    _UpperCAmelCase     :  List[Any]							      =    pred_prev_sample

             _UpperCAmelCase     :  Optional[Any]							      =    torch.sum(torch.abs(A       )       )
             _UpperCAmelCase     :  int							      =    torch.mean(torch.abs(A       )       )

             assert abs(result_sum.item() - 258.2_044_983       ) < 1E-2
             assert abs(result_mean.item() - 0.3_362_038       ) < 1E-3




      def      _A	(       self						:				Dict       ):
             pass





      def      _A	(       self						:				int       ):
             pass








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
__A				:    int       =   {
    "/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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			UpperCAmelCase   		=				list(s_dict.keys() )
			for key in keys:
						UpperCAmelCase   		=				R'''.*/layers_(\d+)'''
						UpperCAmelCase   		=				key
						if re.match(UpperCamelCase__			,			UpperCamelCase__ ):
									UpperCAmelCase   		=				re.sub(R'''layers_(\d+)'''			,			R'''block/\1/layer'''			,			UpperCamelCase__ )

						UpperCAmelCase   		=				R'''(encoder|decoder)\/'''

						if re.match(UpperCamelCase__			,			UpperCamelCase__ ):
									UpperCAmelCase   		=				re.match(UpperCamelCase__			,			UpperCamelCase__ ).groups()
									if groups[0] == "encoder":
												UpperCAmelCase   		=				re.sub(R'''/mlp/'''			,			R'''/1/mlp/'''			,			UpperCamelCase__ )
												UpperCAmelCase   		=				re.sub(R'''/pre_mlp_layer_norm/'''			,			R'''/1/layer_norm/'''			,			UpperCamelCase__ )

									elif groups[0] == "decoder":
												UpperCAmelCase   		=				re.sub(R'''/mlp/'''			,			R'''/2/mlp/'''			,			UpperCamelCase__ )
												UpperCAmelCase   		=				re.sub(R'''/pre_mlp_layer_norm/'''			,			R'''/2/layer_norm/'''			,			UpperCamelCase__ )

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

						print(F"""{key} -> {new_key}""" )
						UpperCAmelCase   		=				s_dict.pop(UpperCamelCase__ )

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

									s_dict.pop(UpperCamelCase__ )

			return s_dict


__A				:    Optional[int]       =   {
    "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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			import regex as re

			with open(UpperCamelCase__			,			'''r''' ) as f:
						UpperCAmelCase   		=				f.read()

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

			UpperCAmelCase   		=				re.findall(R'''(.*activations) = \(\'(.*)\',\)'''			,			UpperCamelCase__ )[0]
			UpperCAmelCase   		=				str(activation[1] )

			UpperCAmelCase   		=				num_experts
			UpperCAmelCase   		=				SwitchTransformersConfig(**UpperCamelCase__ )
			return config





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__=None			,			UpperCamelCase__="./"			,			UpperCamelCase__=8 )  ->				List[Any]:







			'''simple docstring'''

			print(F"""Loading flax weights from : {flax_checkpoint_path}""" )
			UpperCAmelCase   		=				checkpoints.load_tax_checkpoint(UpperCamelCase__ )

			if gin_file is not None:
						UpperCAmelCase   		=				convert_gin_to_config(UpperCamelCase__			,			UpperCamelCase__ )
			else:
						UpperCAmelCase   		=				SwitchTransformersConfig.from_pretrained(UpperCamelCase__ )

			UpperCAmelCase   		=				SwitchTransformersForConditionalGeneration(UpperCamelCase__ )

			UpperCAmelCase   		=				flax_params['''target''']
			UpperCAmelCase   		=				flatten_dict(UpperCamelCase__			,			sep='''/''' )
			UpperCAmelCase   		=				rename_keys(UpperCamelCase__ )
			UpperCAmelCase   		=				unflatten_dict(UpperCamelCase__			,			sep='''/''' )

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

			print(F"""Save PyTorch model to {pytorch_dump_path}""" )
			pt_model.save_pretrained(UpperCamelCase__ )


if __name__ == "__main__":
				__A				:    Tuple       =   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")
				__A				:    Tuple       =   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,
				)



