




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase      			=						{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						['SqueezeBertTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'SqueezeBertForMaskedLM',
     'SqueezeBertForMultipleChoice',
     'SqueezeBertForQuestionAnswering',
     'SqueezeBertForSequenceClassification',
     'SqueezeBertForTokenClassification',
     'SqueezeBertModel',
     'SqueezeBertModule',
     'SqueezeBertPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_squeezebert import (
     SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SqueezeBertConfig,
     SqueezeBertOnnxConfig,
 )
 from .tokenization_squeezebert import SqueezeBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_squeezebert import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
      SqueezeBertModule,
      SqueezeBertPreTrainedModel,
  )

else:
 import sys

 _lowerCamelCase      			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						str				)  ->							str:
    UpperCAmelCase_										=					AutoConfig.from_pretrained(__UpperCamelCase				)
    UpperCAmelCase_										=					FlaxAutoModelForSeqaSeqLM.from_config(config=__UpperCamelCase				)
    UpperCAmelCase_										=					checkpoints.load_tax_checkpoint(__UpperCamelCase				)

    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(__UpperCamelCase				)}'

        # 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(__UpperCamelCase				)]['''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(__UpperCamelCase				)}'

        # 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(__UpperCamelCase				)]['''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(__UpperCamelCase				)
    print('''T5X Model was sucessfully converted!'''				)


if __name__ == "__main__":
 _lowerCamelCase      			=						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      			=						parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





