
"""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
_a														=		{
    """/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__		(			__snake_case	)							->		Dict:


					"""simple docstring"""





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

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

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

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

        # 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(__snake_case,     __snake_case	)

										print(F'''{key} -> {new_key}'''	)
										_UpperCamelCase							 =  s_dict.pop(__snake_case	)

					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(__snake_case	):
																				_UpperCamelCase							 =  expert_weihts[idx]
																				print(F'''{key} -> {key.replace("expert/",     "nested fstring"	)}'''	)

															s_dict.pop(__snake_case	)

					return s_dict


_a														=		{
    """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__		(			__snake_case,     __snake_case	)							->		Any:


					"""simple docstring"""





					import regex as re

					with open(__snake_case,     '''r'''	) as f:
										_UpperCamelCase							 =  f.read()

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

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

					_UpperCamelCase							 =  num_experts
					_UpperCamelCase							 =  SwitchTransformersConfig(**__snake_case	)
					return config



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


					"""simple docstring"""





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

					if gin_file is not None:
										_UpperCamelCase							 =  convert_gin_to_config(__snake_case,     __snake_case	)
					else:
										_UpperCamelCase							 =  SwitchTransformersConfig.from_pretrained(__snake_case	)

					_UpperCamelCase							 =  SwitchTransformersForConditionalGeneration(__snake_case	)

					_UpperCamelCase							 =  flax_params['''target''']
					_UpperCamelCase							 =  flatten_dict(__snake_case,     sep='''/'''	)
					_UpperCamelCase							 =  rename_keys(__snake_case	)
					_UpperCamelCase							 =  unflatten_dict(__snake_case,     sep='''/'''	)

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

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


if __name__ == "__main__":
				_a														=		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														=		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,
				)








"""simple docstring"""





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		np.ndarray:


					"""simple docstring"""





					if (ksize % 2) == 0:
										_UpperCamelCase							 =  ksize + 1
					_UpperCamelCase							 =  np.zeros((ksize, ksize),     dtype=np.floataa	)

					# each value
					for y in range(__snake_case	):
										for x in range(__snake_case	):
															# distance from center
															_UpperCamelCase							 =  x - ksize // 2
															_UpperCamelCase							 =  y - ksize // 2

															# degree to radiant
															_UpperCamelCase							 =  theta / 1_80 * np.pi
															_UpperCamelCase							 =  np.cos(_theta	)
															_UpperCamelCase							 =  np.sin(_theta	)

															# get kernel x
															_UpperCamelCase							 =  cos_theta * px + sin_theta * py

															# get kernel y
															_UpperCamelCase							 =  -sin_theta * px + cos_theta * py

															# fill kernel
															_UpperCamelCase							 =  np.exp(
															    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)	) * np.cos(2 * np.pi * _x / lambd + psi	)

					return gabor


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# read original image
				_a														=		imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a														=		cvtColor(img, COLOR_BGR2GRAY)

				# Apply multiple Kernel to detect edges
				_a														=		np.zeros(gray.shape[:2])
				for theta in [0, 30, 60, 90, 120, 150]:
								_a														=		gabor_filter_kernel(10, 8, theta, 10, 0, 0)
								out += filteraD(gray, CV_8UC3, kernel_aa)
				_a														=		out / out.max() * 255
				_a														=		out.astype(np.uinta)

				imshow("""Original""", gray)
				imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

				waitKey(0)






