import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Union[str, Any]    						=	FlaxAutoModelForSeqaSeqLM.from_config(config=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Dict    						=	checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Tuple    						=	'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        __UpperCamelCase  :str    						=	'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        __UpperCamelCase  :Dict    						=	'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        __UpperCamelCase  :Union[str, Any]    						=	'''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  :List[Any]    						=	f"""layers_{str(SCREAMING_SNAKE_CASE			)}"""

        # Self-Attention
        __UpperCamelCase  :List[Any]    						=	tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        __UpperCamelCase  :Any    						=	tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        __UpperCamelCase  :Tuple    						=	tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        __UpperCamelCase  :Optional[Any]    						=	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  :Tuple    						=	tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        __UpperCamelCase  :Tuple    						=	tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            __UpperCamelCase  :Optional[Any]    						=	tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            __UpperCamelCase  :Optional[int]    						=	tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            __UpperCamelCase  :Union[str, Any]    						=	tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        __UpperCamelCase  :Optional[Any]    						=	tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        __UpperCamelCase  :str    						=	tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        __UpperCamelCase  :Any    						=	flax_model.params['''encoder''']['''block'''][str(SCREAMING_SNAKE_CASE			)]['''layer''']
        __UpperCamelCase  :Tuple    						=	tax_attention_key
        __UpperCamelCase  :Union[str, Any]    						=	tax_attention_out
        __UpperCamelCase  :Tuple    						=	tax_attention_query
        __UpperCamelCase  :str    						=	tax_attention_value

        __UpperCamelCase  :Tuple    						=	tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            __UpperCamelCase  :Optional[int]    						=	tax_global_layer_norm

        if split_mlp_wi:
            __UpperCamelCase  :List[str]    						=	tax_mlp_wi_a
            __UpperCamelCase  :str    						=	tax_mlp_wi_a
        else:
            __UpperCamelCase  :Optional[int]    						=	tax_mlp_wi

        __UpperCamelCase  :Optional[Any]    						=	tax_mlp_wo
        __UpperCamelCase  :Union[str, Any]    						=	tax_mlp_layer_norm

        __UpperCamelCase  :Dict    						=	flax_model_encoder_layer_block

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

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

    # Assigning
    __UpperCamelCase  :List[Any]    						=	tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    __UpperCamelCase  :List[Any]    						=	tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers			):
        __UpperCamelCase  :Any    						=	f"""layers_{str(SCREAMING_SNAKE_CASE			)}"""

        # Self-Attention
        __UpperCamelCase  :List[str]    						=	tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        __UpperCamelCase  :Optional[Any]    						=	tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        __UpperCamelCase  :Optional[Any]    						=	tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        __UpperCamelCase  :Optional[int]    						=	tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        __UpperCamelCase  :str    						=	tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        __UpperCamelCase  :Optional[Any]    						=	tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        __UpperCamelCase  :Dict    						=	tax_enc_dec_attention_module['''key''']['''kernel''']
        __UpperCamelCase  :List[str]    						=	tax_enc_dec_attention_module['''out''']['''kernel''']
        __UpperCamelCase  :Optional[Any]    						=	tax_enc_dec_attention_module['''query''']['''kernel''']
        __UpperCamelCase  :Optional[Any]    						=	tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        __UpperCamelCase  :List[Any]    						=	tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            __UpperCamelCase  :Tuple    						=	tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            __UpperCamelCase  :List[str]    						=	tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            __UpperCamelCase  :Optional[Any]    						=	tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        __UpperCamelCase  :Dict    						=	tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        __UpperCamelCase  :str    						=	tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        __UpperCamelCase  :int    						=	flax_model.params['''decoder''']['''block'''][str(SCREAMING_SNAKE_CASE			)]['''layer''']
        __UpperCamelCase  :Optional[int]    						=	tax_attention_key
        __UpperCamelCase  :Dict    						=	tax_attention_out
        __UpperCamelCase  :Dict    						=	tax_attention_query
        __UpperCamelCase  :Tuple    						=	tax_attention_value

        __UpperCamelCase  :List[str]    						=	tax_pre_attention_layer_norm

        __UpperCamelCase  :str    						=	tax_enc_dec_attention_key
        __UpperCamelCase  :int    						=	tax_enc_dec_attention_out
        __UpperCamelCase  :Optional[int]    						=	tax_enc_dec_attention_query
        __UpperCamelCase  :Optional[Any]    						=	tax_enc_dec_attention_value

        __UpperCamelCase  :Dict    						=	tax_cross_layer_norm

        if split_mlp_wi:
            __UpperCamelCase  :str    						=	tax_mlp_wi_a
            __UpperCamelCase  :Dict    						=	tax_mlp_wi_a
        else:
            __UpperCamelCase  :Dict    						=	tax_mlp_wi

        __UpperCamelCase  :Tuple    						=	tax_mlp_wo

        __UpperCamelCase  :str    						=	txa_mlp_layer_norm

        __UpperCamelCase  :str    						=	flax_model_decoder_layer_block

    # Decoder Normalization
    __UpperCamelCase  :Optional[int]    						=	tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    __UpperCamelCase  :Optional[int]    						=	txa_decoder_norm

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

    # Token Embeddings
    __UpperCamelCase  :Optional[Any]    						=	tax_model['''target''']['''token_embedder''']['''embedding''']
    __UpperCamelCase  :str    						=	txa_token_embeddings

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

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


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






import qiskit



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	qiskit.Aer.get_backend('''aer_simulator'''			)

    __UpperCamelCase  :Tuple    						=	qiskit.QuantumCircuit(4						,    2			)
    # encode inputs in qubits 0 and 1
    if bita == 1:
        qc_ha.x(0			)
    if bita == 1:
        qc_ha.x(1			)
    qc_ha.barrier()

    # use cnots to write XOR of the inputs on qubit2
    qc_ha.cx(0						,    2			)
    qc_ha.cx(1						,    2			)

    # use ccx / toffoli gate to write AND of the inputs on qubit3
    qc_ha.ccx(0						,    1						,    3			)
    qc_ha.barrier()

    # extract outputs
    qc_ha.measure(2						,    0			)  # extract XOR value
    qc_ha.measure(3						,    1			)  # extract AND value

    # Execute the circuit on the qasm simulator
    __UpperCamelCase  :Optional[Any]    						=	qiskit.execute(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    shots=1_000			)

    # Return the histogram data of the results of the experiment
    return job.result().get_counts(SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
    __lowercase											=      half_adder(1, 1)
    print(F'Half Adder Output Qubit Counts: {counts}')





