



'''simple docstring'''



import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 					UpperCAmelCase   (						A  :				List[Any]					,		A  :				Optional[int]					,		A  :				Optional[int]				):
       return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]
def 					UpperCAmelCase   (						A  :				Any					,		A  :				Union[str, Any]					,		A  :				Tuple					,		A  :				Any="attention"				):
       SCREAMING_SNAKE_CASE					:      int     					=			np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]				)
       SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			k_tmp.reshape(k_tmp.shape[0]					,		k_tmp.shape[1] * k_tmp.shape[2]				)
       SCREAMING_SNAKE_CASE					:      List[str]     					=			np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]				)
       SCREAMING_SNAKE_CASE					:      Optional[int]     					=			o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]					,		o_tmp.shape[2]				)
       SCREAMING_SNAKE_CASE					:      List[str]     					=			np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]				)
       SCREAMING_SNAKE_CASE					:      str     					=			q_tmp.reshape(q_tmp.shape[0]					,		q_tmp.shape[1] * q_tmp.shape[2]				)
       SCREAMING_SNAKE_CASE					:      int     					=			np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]				)
       SCREAMING_SNAKE_CASE					:      Tuple     					=			v_tmp.reshape(v_tmp.shape[0]					,		v_tmp.shape[1] * v_tmp.shape[2]				)
       return k, o, q, v
def 					UpperCAmelCase   (						A  :				Any					,		A  :				List[Any]					,		A  :				Tuple					,		A  :				List[Any]=False				):






       if split_mlp_wi:
              SCREAMING_SNAKE_CASE					:      List[str]     					=			params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
              SCREAMING_SNAKE_CASE					:      int     					=			params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
              SCREAMING_SNAKE_CASE					:      Any     					=			(wi_a, wi_a)
       else:
              SCREAMING_SNAKE_CASE					:      Tuple     					=			params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

       SCREAMING_SNAKE_CASE					:      Optional[int]     					=			params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
       return wi, wo
def 					UpperCAmelCase   (						A  :				Optional[int]					,		A  :				Tuple					,		A  :				List[Any]					,		A  :				int				):
       return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]
def 					UpperCAmelCase   (						A  :				str					,		*, A  :				Optional[int]					,		A  :				Optional[int]					,		A  :				int = False				):
       SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			traverse_util.flatten_dict(variables['''target''']				)
       SCREAMING_SNAKE_CASE					:      int     					=			{'''/'''.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
       SCREAMING_SNAKE_CASE					:      List[str]     					=			'''encoder/encoder/mlp/wi_0/kernel''' in old
       print('''Split MLP:'''					,		a__				)

       SCREAMING_SNAKE_CASE					:      List[str]     					=			collections.OrderedDict()

       # Shared embeddings.
       SCREAMING_SNAKE_CASE					:      int     					=			old['''token_embedder/embedding''']

       # Encoder.
       for i in range(a__				):
              # Block i, layer 0 (Self Attention).
              SCREAMING_SNAKE_CASE					:      List[Any]     					=			tax_layer_norm_lookup(a__					,		a__					,		'''encoder'''					,		'''pre_attention_layer_norm'''				)
              SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Tuple     					=			tax_attention_lookup(a__					,		a__					,		'''encoder'''					,		'''attention'''				)
              SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			layer_norm
              SCREAMING_SNAKE_CASE					:      Dict     					=			k.T
              SCREAMING_SNAKE_CASE					:      Dict     					=			o.T
              SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			q.T
              SCREAMING_SNAKE_CASE					:      Tuple     					=			v.T

              # Block i, layer 1 (MLP).
              SCREAMING_SNAKE_CASE					:      Tuple     					=			tax_layer_norm_lookup(a__					,		a__					,		'''encoder'''					,		'''pre_mlp_layer_norm'''				)
              SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[Any]     					=			tax_mlp_lookup(a__					,		a__					,		'''encoder'''					,		a__				)
              SCREAMING_SNAKE_CASE					:      Dict     					=			layer_norm
              if split_mlp_wi:
                     SCREAMING_SNAKE_CASE					:      int     					=			wi[0].T
                     SCREAMING_SNAKE_CASE					:      Tuple     					=			wi[1].T
              else:
                     SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			wi.T
              SCREAMING_SNAKE_CASE					:      int     					=			wo.T
              if scalable_attention:
                     # convert the rel_embedding of each layer
                     SCREAMING_SNAKE_CASE					:      str     					=			tax_relpos_bias_lookup(
                         a__					,		a__					,		'''encoder'''				).T

       SCREAMING_SNAKE_CASE					:      Optional[int]     					=			old['''encoder/encoder_norm/scale''']

       if not scalable_attention:
              SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tax_relpos_bias_lookup(
                  a__					,		0					,		'''encoder'''				).T
              SCREAMING_SNAKE_CASE					:      Tuple     					=			tax_relpos_bias_lookup(
                  a__					,		0					,		'''decoder'''				).T

       if not is_encoder_only:
              # Decoder.
              for i in range(a__				):
                     # Block i, layer 0 (Self Attention).
                     SCREAMING_SNAKE_CASE					:      Dict     					=			tax_layer_norm_lookup(a__					,		a__					,		'''decoder'''					,		'''pre_self_attention_layer_norm'''				)
                     SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			tax_attention_lookup(a__					,		a__					,		'''decoder'''					,		'''self_attention'''				)
                     SCREAMING_SNAKE_CASE					:      Tuple     					=			layer_norm
                     SCREAMING_SNAKE_CASE					:      List[Any]     					=			k.T
                     SCREAMING_SNAKE_CASE					:      Dict     					=			o.T
                     SCREAMING_SNAKE_CASE					:      Dict     					=			q.T
                     SCREAMING_SNAKE_CASE					:      List[Any]     					=			v.T

                     # Block i, layer 1 (Cross Attention).
                     SCREAMING_SNAKE_CASE					:      List[Any]     					=			tax_layer_norm_lookup(a__					,		a__					,		'''decoder'''					,		'''pre_cross_attention_layer_norm'''				)
                     SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			tax_attention_lookup(a__					,		a__					,		'''decoder'''					,		'''encoder_decoder_attention'''				)
                     SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			layer_norm
                     SCREAMING_SNAKE_CASE					:      List[str]     					=			k.T
                     SCREAMING_SNAKE_CASE					:      Optional[int]     					=			o.T
                     SCREAMING_SNAKE_CASE					:      Dict     					=			q.T
                     SCREAMING_SNAKE_CASE					:      Tuple     					=			v.T

                     # Block i, layer 2 (MLP).
                     SCREAMING_SNAKE_CASE					:      Any     					=			tax_layer_norm_lookup(a__					,		a__					,		'''decoder'''					,		'''pre_mlp_layer_norm'''				)
                     SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			tax_mlp_lookup(a__					,		a__					,		'''decoder'''					,		a__				)
                     SCREAMING_SNAKE_CASE					:      Dict     					=			layer_norm
                     if split_mlp_wi:
                            SCREAMING_SNAKE_CASE					:      int     					=			wi[0].T
                            SCREAMING_SNAKE_CASE					:      List[Any]     					=			wi[1].T
                     else:
                            SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			wi.T
                     SCREAMING_SNAKE_CASE					:      List[Any]     					=			wo.T

                     if scalable_attention:
                            # convert the rel_embedding of each layer
                            SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tax_relpos_bias_lookup(a__					,		a__					,		'''decoder'''				).T

              SCREAMING_SNAKE_CASE					:      Tuple     					=			old['''decoder/decoder_norm/scale''']

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

       return new
def 					UpperCAmelCase   (						A  :				Optional[Any]					,		A  :				Any				):
       SCREAMING_SNAKE_CASE					:      List[Any]     					=			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:
              SCREAMING_SNAKE_CASE					:      List[Any]     					=			state_dict['''shared.weight''']

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     SCREAMING_SNAKE_CASE					:      List[Any]     					=			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.'''				)
                     SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			state_dict['''shared.weight''']

       return state_dict
def 					UpperCAmelCase   (						A  :				str					,		A  :				int					,		A  :				int					,		A  :				Union[str, Any]					,		A  :				Tuple				):
       SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			checkpoints.load_tax_checkpoint(a__				)
       SCREAMING_SNAKE_CASE					:      Optional[int]     					=			convert_tax_to_pytorch(
           a__					,		num_layers=config.num_layers					,		is_encoder_only=a__					,		scalable_attention=a__				)
       SCREAMING_SNAKE_CASE					:      List[Any]     					=			make_state_dict(a__					,		a__				)
       model.load_state_dict(a__					,		strict=a__				)
def 					UpperCAmelCase   (						A  :				int					,		A  :				Optional[int]					,		A  :				str					,		A  :				Optional[Any] = False					,		A  :				List[Any] = False					,		):
       SCREAMING_SNAKE_CASE					:      Any     					=			MTaConfig.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:
              SCREAMING_SNAKE_CASE					:      Any     					=			UMTaEncoderModel(a__				)
       else:
              SCREAMING_SNAKE_CASE					:      str     					=			UMTaForConditionalGeneration(a__				)

       # Load weights from tf checkpoint
       load_tax_weights_in_ta(a__					,		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__":
       lowerCAmelCase_     :       Union[str, Any]								=						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
       )
       parser.add_argument(
           '--scalable_attention',
           action='store_true',
           help='Whether the model uses scaled attention (umt5 model)',
           default=False,
       )
       lowerCAmelCase_     :       int								=						parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path,
           args.config_file,
           args.pytorch_dump_path,
           args.is_encoder_only,
           args.scalable_attention,
       )









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_     :       str								=						{
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Dict								=						['LlamaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       str								=						['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Optional[int]								=						[
							    'LlamaForCausalLM',
							    'LlamaModel',
							    'LlamaPreTrainedModel',
							    'LlamaForSequenceClassification',
							]


if TYPE_CHECKING:
							from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama import LlamaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama_fast import LlamaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
							import sys

							lowerCAmelCase_     :       Optional[Any]								=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




