
'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def 		A_(     A						:		str		,     A						:		List[Any]		,     A						:		Optional[Any]):
							UpperCamelCase   			=     AutoConfig.from_pretrained(A)
							UpperCamelCase   			=     FlaxAutoModelForSeqaSeqLM.from_config(config=A)
							UpperCamelCase   			=     checkpoints.load_tax_checkpoint(A)

							UpperCamelCase   			=     'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

							if config.model_type == "t5":
														UpperCamelCase   			=     'SelfAttention'
							if config.model_type == "longt5" and config.encoder_attention_type == "local":
														UpperCamelCase   			=     'LocalSelfAttention'
							elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														UpperCamelCase   			=     'TransientGlobalSelfAttention'
							else:
														raise ValueError(
														    'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
														    ' attribute with a value from [\'local\', \'transient-global].')

							# Encoder
							for layer_index in range(config.num_layers):
														UpperCamelCase   			=     f'''layers_{str(A)}'''

														# Self-Attention
														UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
														UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
														UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
														UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

														# Global input layer norm
														if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																					UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

														# Layer Normalization
														UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

														if split_mlp_wi:
																					UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
																					UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
														else:
																					UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

														UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

														# Layer Normalization
														UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

														# Assigning
														UpperCamelCase   			=     flax_model.params['encoder']['block'][str(A)]['layer']
														UpperCamelCase   			=     tax_attention_key
														UpperCamelCase   			=     tax_attention_out
														UpperCamelCase   			=     tax_attention_query
														UpperCamelCase   			=     tax_attention_value

														UpperCamelCase   			=     tax_attention_layer_norm

														# Global input layer norm
														if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																					UpperCamelCase   			=     tax_global_layer_norm

														if split_mlp_wi:
																					UpperCamelCase   			=     tax_mlp_wi_a
																					UpperCamelCase   			=     tax_mlp_wi_a
														else:
																					UpperCamelCase   			=     tax_mlp_wi

														UpperCamelCase   			=     tax_mlp_wo
														UpperCamelCase   			=     tax_mlp_layer_norm

														UpperCamelCase   			=     flax_model_encoder_layer_block

							# Only for layer 0:
							UpperCamelCase   			=     tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
							UpperCamelCase   			=     tax_encoder_rel_embedding

							# Side/global relative position_bias + layer norm
							if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														UpperCamelCase   			=     tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
														UpperCamelCase   			=     tax_encoder_global_rel_embedding

							# Assigning
							UpperCamelCase   			=     tax_model['target']['encoder']['encoder_norm']['scale']
							UpperCamelCase   			=     tax_encoder_norm

							# Decoder
							for layer_index in range(config.num_layers):
														UpperCamelCase   			=     f'''layers_{str(A)}'''

														# Self-Attention
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

														# Layer Normalization
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
														    'scale'
														]

														# Encoder-Decoder-Attention
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
														UpperCamelCase   			=     tax_enc_dec_attention_module['key']['kernel']
														UpperCamelCase   			=     tax_enc_dec_attention_module['out']['kernel']
														UpperCamelCase   			=     tax_enc_dec_attention_module['query']['kernel']
														UpperCamelCase   			=     tax_enc_dec_attention_module['value']['kernel']

														# Layer Normalization
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

														# MLP
														if split_mlp_wi:
																					UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
																					UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
														else:
																					UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

														# Layer Normalization
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

														# Assigning
														UpperCamelCase   			=     flax_model.params['decoder']['block'][str(A)]['layer']
														UpperCamelCase   			=     tax_attention_key
														UpperCamelCase   			=     tax_attention_out
														UpperCamelCase   			=     tax_attention_query
														UpperCamelCase   			=     tax_attention_value

														UpperCamelCase   			=     tax_pre_attention_layer_norm

														UpperCamelCase   			=     tax_enc_dec_attention_key
														UpperCamelCase   			=     tax_enc_dec_attention_out
														UpperCamelCase   			=     tax_enc_dec_attention_query
														UpperCamelCase   			=     tax_enc_dec_attention_value

														UpperCamelCase   			=     tax_cross_layer_norm

														if split_mlp_wi:
																					UpperCamelCase   			=     tax_mlp_wi_a
																					UpperCamelCase   			=     tax_mlp_wi_a
														else:
																					UpperCamelCase   			=     tax_mlp_wi

														UpperCamelCase   			=     tax_mlp_wo

														UpperCamelCase   			=     txa_mlp_layer_norm

														UpperCamelCase   			=     flax_model_decoder_layer_block

							# Decoder Normalization
							UpperCamelCase   			=     tax_model['target']['decoder']['decoder_norm']['scale']
							UpperCamelCase   			=     txa_decoder_norm

							# Only for layer 0:
							UpperCamelCase   			=     tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
							UpperCamelCase   			=     tax_decoder_rel_embedding

							# Token Embeddings
							UpperCamelCase   			=     tax_model['target']['token_embedder']['embedding']
							UpperCamelCase   			=     txa_token_embeddings

							# LM Head (only in v1.1 and LongT5 checkpoints)
							if "logits_dense" in tax_model["target"]["decoder"]:
														UpperCamelCase   			=     tax_model['target']['decoder']['logits_dense']['kernel']

							flax_model.save_pretrained(A)
							print('T5X Model was sucessfully converted!')


if __name__ == "__main__":
						lowerCAmelCase					:    Tuple           = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
						)
						parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
						parser.add_argument(
						    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
						)
						lowerCAmelCase					:    int           = parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase							:   List[str]						=						logging.get_logger(__name__)

_lowercase							:   int						=						[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]





def 	_lowerCAmelCase					( UpperCamelCase__:  str    )			-> int:





	"""simple docstring"""







	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		A             =					k.replace(UpperCamelCase__				,       UpperCamelCase__    )

	if k.startswith("""encoder"""    ):
		A             =					k.replace(""".attn"""				,       """.self_attn"""    )
		A             =					k.replace("""norm1"""				,       """self_attn_layer_norm"""    )
		A             =					k.replace("""norm2"""				,       """final_layer_norm"""    )
	elif k.startswith("""decoder"""    ):
		A             =					k.replace("""norm1"""				,       """self_attn_layer_norm"""    )
		A             =					k.replace("""norm2"""				,       """encoder_attn_layer_norm"""    )
		A             =					k.replace("""norm3"""				,       """final_layer_norm"""    )
	return k





def 	_lowerCAmelCase					( UpperCamelCase__:  Tuple    )			-> str:





	"""simple docstring"""







	A             =					[
	    """model.encoder.layernorm_embedding.weight""",
	    """model.encoder.layernorm_embedding.bias""",
	    """model.decoder.layernorm_embedding.weight""",
	    """model.decoder.layernorm_embedding.bias""",
	]
	for k in keys:
		A             =					sd.pop(UpperCamelCase__    )
		A             =					k.replace("""layernorm_embedding"""				,       """layer_norm"""    )
		assert new_k not in sd
		A             =					v


_lowercase							:   List[Any]						=						["START"]





@torch.no_grad()
def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  Optional[int]    )			-> int:





	"""simple docstring"""







	A             =					torch.load(UpperCamelCase__				,       map_location="""cpu"""    )
	A             =					model["""model"""]
	A             =					BlenderbotConfig.from_json_file(UpperCamelCase__    )
	A             =					BlenderbotForConditionalGeneration(UpperCamelCase__    )
	A             =					m.model.state_dict().keys()
	A             =					[]
	A             =					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		A             =					rename_state_dict_key(UpperCamelCase__    )
		if new_k not in valid_keys:
			failures.append([k, new_k]    )
		else:
			A             =					v
	if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
		rename_layernorm_keys(UpperCamelCase__    )
	m.model.load_state_dict(UpperCamelCase__				,       strict=UpperCamelCase__    )
	m.half()
	m.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
				_lowercase							:   str						=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
				parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
				parser.add_argument(
				    "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
				)
				_lowercase							:   List[str]						=						parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



