
"""simple docstring"""





import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case="attention"	)							->		List[Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
					_UpperCamelCase							 =  params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
					_UpperCamelCase							 =  params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
					_UpperCamelCase							 =  params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
					return k, o, q, v



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=False	)							->		str:


					"""simple docstring"""





					if split_mlp_wi:
										_UpperCamelCase							 =  params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
										_UpperCamelCase							 =  params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
										_UpperCamelCase							 =  (wi_a, wi_a)
					else:
										_UpperCamelCase							 =  params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

					_UpperCamelCase							 =  params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
					return wi, wo



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		Any:


					"""simple docstring"""





					return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']



def       lowerCamelCase__		(			__snake_case,     *, __snake_case,     __snake_case	)							->		int:


					"""simple docstring"""





					_UpperCamelCase							 =  traverse_util.flatten_dict(variables['''target''']	)
					_UpperCamelCase							 =  {'''/'''.join(UpperCamelCase__	): v for k, v in old.items()}

					# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
					_UpperCamelCase							 =  '''encoder/layers_0/mlp/wi_0/kernel''' in old
					print('''Split MLP:''',     UpperCamelCase__	)

					_UpperCamelCase							 =  collections.OrderedDict()

					# Shared embeddings.
					_UpperCamelCase							 =  old['''token_embedder/embedding''']

					# Encoder.
					for i in range(UpperCamelCase__	):
										# Block i, layer 0 (Self Attention).
										_UpperCamelCase							 =  tax_layer_norm_lookup(UpperCamelCase__,     UpperCamelCase__,     '''encoder''',     '''pre_attention_layer_norm'''	)
										_UpperCamelCase							 =  tax_attention_lookup(UpperCamelCase__,     UpperCamelCase__,     '''encoder''',     '''attention'''	)
										_UpperCamelCase							 =  layer_norm
										_UpperCamelCase							 =  k.T
										_UpperCamelCase							 =  o.T
										_UpperCamelCase							 =  q.T
										_UpperCamelCase							 =  v.T

										# Block i, layer 1 (MLP).
										_UpperCamelCase							 =  tax_layer_norm_lookup(UpperCamelCase__,     UpperCamelCase__,     '''encoder''',     '''pre_mlp_layer_norm'''	)
										_UpperCamelCase							 =  tax_mlp_lookup(UpperCamelCase__,     UpperCamelCase__,     '''encoder''',     UpperCamelCase__	)
										_UpperCamelCase							 =  layer_norm
										if split_mlp_wi:
															_UpperCamelCase							 =  wi[0].T
															_UpperCamelCase							 =  wi[1].T
										else:
															_UpperCamelCase							 =  wi.T
										_UpperCamelCase							 =  wo.T

					_UpperCamelCase							 =  old[
					    '''encoder/relpos_bias/rel_embedding'''
					].T
					_UpperCamelCase							 =  old['''encoder/encoder_norm/scale''']

					if not is_encoder_only:
										# Decoder.
										for i in range(UpperCamelCase__	):
															# Block i, layer 0 (Self Attention).
															_UpperCamelCase							 =  tax_layer_norm_lookup(UpperCamelCase__,     UpperCamelCase__,     '''decoder''',     '''pre_self_attention_layer_norm'''	)
															_UpperCamelCase							 =  tax_attention_lookup(UpperCamelCase__,     UpperCamelCase__,     '''decoder''',     '''self_attention'''	)
															_UpperCamelCase							 =  layer_norm
															_UpperCamelCase							 =  k.T
															_UpperCamelCase							 =  o.T
															_UpperCamelCase							 =  q.T
															_UpperCamelCase							 =  v.T

															# Block i, layer 1 (Cross Attention).
															_UpperCamelCase							 =  tax_layer_norm_lookup(UpperCamelCase__,     UpperCamelCase__,     '''decoder''',     '''pre_cross_attention_layer_norm'''	)
															_UpperCamelCase							 =  tax_attention_lookup(UpperCamelCase__,     UpperCamelCase__,     '''decoder''',     '''encoder_decoder_attention'''	)
															_UpperCamelCase							 =  layer_norm
															_UpperCamelCase							 =  k.T
															_UpperCamelCase							 =  o.T
															_UpperCamelCase							 =  q.T
															_UpperCamelCase							 =  v.T

															# Block i, layer 2 (MLP).
															_UpperCamelCase							 =  tax_layer_norm_lookup(UpperCamelCase__,     UpperCamelCase__,     '''decoder''',     '''pre_mlp_layer_norm'''	)
															_UpperCamelCase							 =  tax_mlp_lookup(UpperCamelCase__,     UpperCamelCase__,     '''decoder''',     UpperCamelCase__	)
															_UpperCamelCase							 =  layer_norm
															if split_mlp_wi:
																				_UpperCamelCase							 =  wi[0].T
																				_UpperCamelCase							 =  wi[1].T
															else:
																				_UpperCamelCase							 =  wi.T
															_UpperCamelCase							 =  wo.T

										_UpperCamelCase							 =  old['''decoder/decoder_norm/scale''']
										_UpperCamelCase							 =  old[
										    '''decoder/relpos_bias/rel_embedding'''
										].T

										# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
										if "decoder/logits_dense/kernel" in old:
															_UpperCamelCase							 =  old['''decoder/logits_dense/kernel'''].T

					return new



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  collections.OrderedDict([(k, torch.from_numpy(v.copy()	)) for (k, v) in converted_params.items()]	)

					# Add what is missing.
					if "encoder.embed_tokens.weight" not in state_dict:
										_UpperCamelCase							 =  state_dict['''shared.weight''']

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															_UpperCamelCase							 =  state_dict['''shared.weight''']

										if "lm_head.weight" not in state_dict:  # For old 1.0 models.
															print('''Using shared word embeddings as lm_head.'''	)
															_UpperCamelCase							 =  state_dict['''shared.weight''']

					return state_dict



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		Any:


					"""simple docstring"""





					_UpperCamelCase							 =  checkpoints.load_tax_checkpoint(UpperCamelCase__	)
					_UpperCamelCase							 =  convert_tax_to_pytorch(UpperCamelCase__,     num_layers=config.num_layers,     is_encoder_only=UpperCamelCase__	)
					_UpperCamelCase							 =  make_state_dict(UpperCamelCase__,     UpperCamelCase__	)
					model.load_state_dict(UpperCamelCase__,     strict=UpperCamelCase__	)



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case = False	)							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  TaConfig.from_json_file(UpperCamelCase__	)
					print(F'''Building PyTorch model from configuration: {config}'''	)
					# Non-v1.1 checkpoints could also use T5Model, but this works for all.
					# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
					if is_encoder_only:
										_UpperCamelCase							 =  TaEncoderModel(UpperCamelCase__	)
					else:
										_UpperCamelCase							 =  TaForConditionalGeneration(UpperCamelCase__	)

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(UpperCamelCase__,     UpperCamelCase__,     UpperCamelCase__,     UpperCamelCase__	)

					# Save pytorch-model
					print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
					model.save_pretrained(UpperCamelCase__	)

					# Verify that we can load the checkpoint.
					model.from_pretrained(UpperCamelCase__	)
					print('''Done'''	)


if __name__ == "__main__":
				_a														=		argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
				# Required parameters
				parser.add_argument(
				    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
				)
				parser.add_argument(
				    """--config_file""",
				    default=None,
				    type=str,
				    required=True,
				    help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
				)
				parser.add_argument(
				    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
				)
				parser.add_argument(
				    """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
				)
				_a														=		parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
				)











"""simple docstring"""
import math

from numpy import inf
from scipy.integrate import quad





def  lowerCamelCase_						(UpperCamelCase__		:		float							):
    if num <= 0:
        raise ValueError('''math domain error'''							)

    return quad(UpperCamelCase__     ,       0     ,       UpperCamelCase__     ,       args=(UpperCamelCase__)							)[0]





def  lowerCamelCase_						(UpperCamelCase__		:		float     ,       UpperCamelCase__		:		float							):
    return math.pow(UpperCamelCase__     ,       z - 1							) * math.exp(-x							)


if __name__ == "__main__":
  from doctest import testmod

  testmod()


