






import argparse
import os
import re


lowercase    				=   "src/transformers/models/auto"


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowercase    				=   re.compile(r"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
lowercase    				=   re.compile(r"\s*\(\s*\"(\S[^\"]+)\"")





def 		__UpperCAmelCase     (	a_				,	a_ = False):
							with open(a_				,	'r'				,	encoding='utf-8') as f:
														snake_case_           =				f.read()

							snake_case_           =				content.split('\n')
							snake_case_           =				[]
							snake_case_           =				0
							while line_idx < len(a_):
														if _re_intro_mapping.search(lines[line_idx]) is not None:
																					snake_case_           =				len(re.search(R'^(\s*)\S'				,	lines[line_idx]).groups()[0]) + 8
																					# Start of a new mapping!
																					while not lines[line_idx].startswith(' ' * indent + '('):
																												new_lines.append(lines[line_idx])
																												line_idx += 1

																					snake_case_           =				[]
																					while lines[line_idx].strip() != "]":
																												# Blocks either fit in one line or not
																												if lines[line_idx].strip() == "(":
																																			snake_case_           =				line_idx
																																			while not lines[line_idx].startswith(' ' * indent + ')'):
																																										line_idx += 1
																																			blocks.append('\n'.join(lines[start_idx : line_idx + 1]))
																												else:
																																			blocks.append(lines[line_idx])
																												line_idx += 1

																					# Sort blocks by their identifiers
																					snake_case_           =				sorted(a_				,	key=lambda a_: _re_identifier.search(a_).groups()[0])
																					new_lines += blocks
														else:
																					new_lines.append(lines[line_idx])
																					line_idx += 1

							if overwrite:
														with open(a_				,	'w'				,	encoding='utf-8') as f:
																					f.write('\n'.join(a_))
							elif "\n".join(a_) != content:
														return True





def 		__UpperCAmelCase     (	a_ = False):
							snake_case_           =				[os.path.join(a_				,	a_) for f in os.listdir(a_) if f.endswith('.py')]
							snake_case_           =				[sort_auto_mapping(a_				,	overwrite=a_) for fname in fnames]

							if not overwrite and any(a_):
														snake_case_           =				[f for f, d in zip(a_				,	a_) if d]
														raise ValueError(
														    f'''The following files have auto mappings that need sorting: {", ".join(a_)}. Run `make style` to fix'''
														    ' this.')


if __name__ == "__main__":
	lowercase    				=   argparse.ArgumentParser()
	parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
	lowercase    				=   parser.parse_args()

	sort_all_auto_mappings(not args.check_only)













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 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_="attention"):
							snake_case_           =				params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
							snake_case_           =				params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
							snake_case_           =				params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
							snake_case_           =				params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
							return k, o, q, v





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_=False):
							if split_mlp_wi:
														snake_case_           =				params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
														snake_case_           =				params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
														snake_case_           =				(wi_a, wi_a)
							else:
														snake_case_           =				params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

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





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_):
							return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']





def 		__UpperCAmelCase     (	a_				,	*, a_				,	a_):
							snake_case_           =				traverse_util.flatten_dict(variables['target'])
							snake_case_           =				{'/'.join(a_): v for k, v in old.items()}

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

							snake_case_           =				collections.OrderedDict()

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

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

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

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

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

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

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

														snake_case_           =				old['decoder/decoder_norm/scale']
														snake_case_           =				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:
																					snake_case_           =				old['decoder/logits_dense/kernel'].T

							return new





def 		__UpperCAmelCase     (	a_				,	a_):
							snake_case_           =				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:
														snake_case_           =				state_dict['shared.weight']

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

							return state_dict





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_):
							snake_case_           =				checkpoints.load_tax_checkpoint(a_)
							snake_case_           =				convert_tax_to_pytorch(a_				,	num_layers=config.num_layers				,	is_encoder_only=a_)
							snake_case_           =				make_state_dict(a_				,	a_)
							model.load_state_dict(a_				,	strict=a_)





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_ = False):
							snake_case_           =				TaConfig.from_json_file(a_)
							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:
														snake_case_           =				TaEncoderModel(a_)
							else:
														snake_case_           =				TaForConditionalGeneration(a_)

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

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

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


if __name__ == "__main__":
	lowercase    				=   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
	)
	lowercase    				=   parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
	)





