
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_						: str         =  {
    """/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        A__					(						lowerCamelCase						)  ->	Optional[int]:
				# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
				# the original model
				UpperCamelCase_:							Union[str, Any]	=						list(s_dict.keys()						)
				for key in keys:
								UpperCamelCase_:							int	=						r""".*/layers_(\d+)"""
								UpperCamelCase_:							Any	=						key
								if re.match(lowerCamelCase     ,    lowerCamelCase						):
												UpperCamelCase_:							Tuple	=						re.sub(r"""layers_(\d+)"""     ,    r"""block/\1/layer"""     ,    lowerCamelCase						)

								UpperCamelCase_:							Dict	=						r"""(encoder|decoder)\/"""

								if re.match(lowerCamelCase     ,    lowerCamelCase						):
												UpperCamelCase_:							Any	=						re.match(lowerCamelCase     ,    lowerCamelCase						).groups()
												if groups[0] == "encoder":
																UpperCamelCase_:							Union[str, Any]	=						re.sub(r"""/mlp/"""     ,    r"""/1/mlp/"""     ,    lowerCamelCase						)
																UpperCamelCase_:							Dict	=						re.sub(r"""/pre_mlp_layer_norm/"""     ,    r"""/1/layer_norm/"""     ,    lowerCamelCase						)

												elif groups[0] == "decoder":
																UpperCamelCase_:							Optional[int]	=						re.sub(r"""/mlp/"""     ,    r"""/2/mlp/"""     ,    lowerCamelCase						)
																UpperCamelCase_:							Optional[Any]	=						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:
																UpperCamelCase_:							Any	=						new_key.replace(lowerCamelCase     ,    lowerCamelCase						)

								print(F'''{key} -> {new_key}'''						)
								UpperCamelCase_:							List[Any]	=						s_dict.pop(lowerCamelCase						)

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

												s_dict.pop(lowerCamelCase						)

				return s_dict


lowerCamelCase_						: Union[str, 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        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Tuple:
				# Convert a google style config to the hugging face fromat
				import regex as re

				with open(lowerCamelCase     ,    """r"""						) as f:
								UpperCamelCase_:							Union[str, Any]	=						f.read()

				UpperCamelCase_:							Optional[Any]	=						re.findall(r"""(.*) = ([0-9.]*)"""     ,    lowerCamelCase						)
				UpperCamelCase_:							Dict	=						{}
				for param, value in regex_match:
								if param in GIN_TO_CONFIG_MAPPING and value != "":
												UpperCamelCase_:							Dict	=						float(lowerCamelCase						) if """.""" in value else int(lowerCamelCase						)

				UpperCamelCase_:							Any	=						re.findall(r"""(.*activations) = \(\'(.*)\',\)"""     ,    lowerCamelCase						)[0]
				UpperCamelCase_:							Tuple	=						str(activation[1]						)

				UpperCamelCase_:							Any	=						num_experts
				UpperCamelCase_:							List[str]	=						SwitchTransformersConfig(**lowerCamelCase						)
				return config







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=None     ,    lowerCamelCase="./"     ,    lowerCamelCase=8						)  ->	List[str]:
				# Initialise PyTorch model

				print(F'''Loading flax weights from : {flax_checkpoint_path}'''						)
				UpperCamelCase_:							Optional[int]	=						checkpoints.load_tax_checkpoint(lowerCamelCase						)

				if gin_file is not None:
								UpperCamelCase_:							List[Any]	=						convert_gin_to_config(lowerCamelCase     ,    lowerCamelCase						)
				else:
								UpperCamelCase_:							Optional[int]	=						SwitchTransformersConfig.from_pretrained(lowerCamelCase						)

				UpperCamelCase_:							List[str]	=						SwitchTransformersForConditionalGeneration(lowerCamelCase						)

				UpperCamelCase_:							Union[str, Any]	=						flax_params["""target"""]
				UpperCamelCase_:							int	=						flatten_dict(lowerCamelCase     ,    sep="""/"""						)
				UpperCamelCase_:							Tuple	=						rename_keys(lowerCamelCase						)
				UpperCamelCase_:							Optional[Any]	=						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_						: List[Any]         =  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_						: int         =  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,
							)








from manim import *

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Dict	=						Rectangle(height=0.5						,	width=0.5      )
							UpperCamelCase_:							Dict	=						Rectangle(height=0.46						,	width=0.46      ).set_stroke(width=0      )

							UpperCamelCase_:							Tuple	=						[mem.copy() for i in range(6      )]
							UpperCamelCase_:							List[str]	=						[mem.copy() for i in range(6      )]
							UpperCamelCase_:							List[str]	=						VGroup(*snake_case_      ).arrange(snake_case_						,	buff=0      )
							UpperCamelCase_:							Tuple	=						VGroup(*snake_case_      ).arrange(snake_case_						,	buff=0      )
							UpperCamelCase_:							Union[str, Any]	=						VGroup(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0      )
							UpperCamelCase_:							Optional[Any]	=						Text("""CPU"""						,	font_size=24      )
							UpperCamelCase_:							int	=						Group(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0.5						,	aligned_edge=snake_case_      )
							cpu.move_to([-2.5, -0.5, 0]      )
							self.add(snake_case_      )

							UpperCamelCase_:							Optional[int]	=						[mem.copy() for i in range(1      )]
							UpperCamelCase_:							Dict	=						VGroup(*snake_case_      ).arrange(snake_case_						,	buff=0      )
							UpperCamelCase_:							Optional[int]	=						Text("""GPU"""						,	font_size=24      )
							UpperCamelCase_:							Optional[int]	=						Group(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0.5						,	aligned_edge=snake_case_      )
							gpu.align_to(snake_case_						,	snake_case_      )
							gpu.set_x(gpu.get_x() - 1      )

							self.add(snake_case_      )

							UpperCamelCase_:							Dict	=						[mem.copy() for i in range(6      )]
							UpperCamelCase_:							List[str]	=						VGroup(*snake_case_      ).arrange(snake_case_						,	buff=0      )

							UpperCamelCase_:							Any	=						Text("""Model"""						,	font_size=24      )
							UpperCamelCase_:							Optional[Any]	=						Group(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0.5						,	aligned_edge=snake_case_      )
							model.move_to([3, -1.0, 0]      )

							self.play(
							    Create(snake_case_						,	run_time=1      )						,	Create(snake_case_						,	run_time=1      )						,	Create(snake_case_						,	run_time=1      )						,	)

							UpperCamelCase_:							List[Any]	=						MarkupText(
							    f'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'''						,	font_size=24						,	)

							UpperCamelCase_:							Optional[Any]	=						Square(side_length=2.2      )
							key.move_to([-5, 2, 0]      )

							UpperCamelCase_:							Union[str, Any]	=						MarkupText(
							    f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''						,	font_size=18						,	)

							key_text.move_to([-5, 2.4, 0]      )

							step_a.move_to([2, 2, 0]      )
							self.play(Write(snake_case_						,	run_time=2.5      )						,	Write(snake_case_      )						,	Write(snake_case_      )      )

							self.add(snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						[]
							UpperCamelCase_:							Union[str, Any]	=						[]
							UpperCamelCase_:							Tuple	=						[]
							for i, rect in enumerate(snake_case_      ):
											UpperCamelCase_:							Tuple	=						Rectangle(height=0.46						,	width=0.46      ).set_stroke(width=0.0      ).set_fill(snake_case_						,	opacity=0.7      )
											cpu_target.move_to(snake_case_      )
											cpu_target.generate_target()
											UpperCamelCase_:							int	=						0.46 / 4
											UpperCamelCase_:							Optional[int]	=						0.46 / 3

											if i == 0:
															cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      )						,	buff=0.02						,	direction=snake_case_      )
															cpu_target.target.set_x(cpu_target.target.get_x() + 0.1      )
											elif i == 3:
															cpu_target.target.next_to(cpu_targs[0].target						,	direction=snake_case_						,	buff=0.0      )
											else:
															cpu_target.target.next_to(cpu_targs[i - 1].target						,	direction=snake_case_						,	buff=0.0      )
											cpu_targs.append(snake_case_      )

											first_animations.append(rect.animate(run_time=0.5      ).set_stroke(snake_case_      )      )
											second_animations.append(MoveToTarget(snake_case_						,	run_time=1.5      )      )

							self.play(*snake_case_      )
							self.play(*snake_case_      )

							self.wait()






