





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
UpperCAmelCase_											=       {
    """/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__       (       UpperCamelCase__     :    List[str]  )     ->	List[str]:



					'''simple docstring'''




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

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

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

															elif groups[0] == "decoder":
																				_snake_case													=			re.sub(R'/mlp/'    ,      R'/2/mlp/'    ,      UpperCamelCase__  )
																				_snake_case													=			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:
																				_snake_case													=			new_key.replace(UpperCamelCase__    ,      UpperCamelCase__  )

										print(F'''{key} -> {new_key}'''  )
										_snake_case													=			s_dict.pop(UpperCamelCase__  )

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

															s_dict.pop(UpperCamelCase__  )

					return s_dict


UpperCAmelCase_											=       {
    """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__       (       UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    List[Any]  )     ->	List[Any]:



					'''simple docstring'''




					import regex as re

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

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

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

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





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



					'''simple docstring'''




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

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

					_snake_case													=			SwitchTransformersForConditionalGeneration(UpperCamelCase__  )

					_snake_case													=			flax_params['target']
					_snake_case													=			flatten_dict(UpperCamelCase__    ,      sep='/'  )
					_snake_case													=			rename_keys(UpperCamelCase__  )
					_snake_case													=			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__":
					UpperCAmelCase_											=       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""")
					UpperCAmelCase_											=       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 cva import destroyAllWindows, imread, imshow, waitKey





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict  )     ->	Optional[Any]:



					'''simple docstring'''




					_snake_case							,       _snake_case													=			img.shape[0], img.shape[1]

					# converting each pixel's color to its negative
					for i in range(UpperCamelCase__  ):
										for j in range(UpperCamelCase__  ):
															_snake_case													=			[255, 255, 255] - img[i][j]

					return img


if __name__ == "__main__":
					# read original image
					UpperCAmelCase_ 							=       imread("""image_data/lena.jpg""", 1)

					# convert to its negative
					UpperCAmelCase_ 							=       convert_to_negative(img)

					# show result image
					imshow("""negative of original image""", img)
					waitKey(0)
					destroyAllWindows()



