

"""simple docstring"""







import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
       import torch


lowerCamelCase_     :     Any    		=						logging.get_logger(__name__)






class    __A       :






 """simple docstring"""




 def __init__(	self				,					__A				,					__A=21				,					__A=[]				,					__A=None		)      ->  Optional[int]:
     a					=model
     a					=trace_batch_nums
     a					=abort_after_batch_num

     # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
     a					=collections.deque([]				,					__SCREAMING_SNAKE_CASE		)
     a					=[]
     a					=0
     a					=0
     a					=False
     a					='''                 '''

     self.analyse_model()

     self.register_forward_hook()


 def    SCREAMING_SNAKE_CASE							(	self				,					__A=None		)      ->  str:

     if frame is not None:
         self.expand_frame(__SCREAMING_SNAKE_CASE		)
     self.frames.append('''\n'''.join(self.frame		)		)
     a					=[]  # start a new frame


 def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Any:
     self.frame.append(__SCREAMING_SNAKE_CASE		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
     print('''\n'''.join(self.frames		)		)
     a					=[]


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
     a					=[]


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
     print(f'''\nDetected inf/nan during batch_number={self.batch_number}'''		)
     print(f'''Last {len(self.frames		)} forward frames:'''		)
     print(f'''{"abs min":8} {"abs max":8} metadata'''		)
     print('''\n'''.join(self.frames		)		)
     print('''\n\n'''		)
     a					=[]


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
     a					={m: name for name, m in self.model.named_modules()}
     # self.longest_module_name = max(len(v) for v in self.module_names.values())


 def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A		)      ->  str:

     if torch.is_tensor(__SCREAMING_SNAKE_CASE		):
         self.expand_frame(get_abs_min_max(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE		)		)
         if detect_overflow(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE		):
             a					=True
     elif var is None:
         self.expand_frame(f'''{"None":>17} {ctx}'''		)
     else:
         self.expand_frame(f'''{"not a tensor":>17} {ctx}'''		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
     self.expand_frame(f'''\n\n{self.prefix} *** Starting batch number={self.batch_number} ***'''		)
     self.expand_frame(f'''{"abs min":8} {"abs max":8} metadata'''		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
     self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''		)


 def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A		)      ->  Tuple:
     self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''		)

     # params
     for name, p in module.named_parameters(recurse=__SCREAMING_SNAKE_CASE		):
         self.analyse_variable(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE		)

     # inputs
     if isinstance(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE		):
         for i, x in enumerate(__SCREAMING_SNAKE_CASE		):
             self.analyse_variable(__SCREAMING_SNAKE_CASE				,					f'''input[{i}]'''		)
     else:
         self.analyse_variable(__SCREAMING_SNAKE_CASE				,					'''input'''		)

     # outputs
     if isinstance(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE		):
         for i, x in enumerate(__SCREAMING_SNAKE_CASE		):
             # possibly a tuple of tuples
             if isinstance(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE		):
                 for j, y in enumerate(__SCREAMING_SNAKE_CASE		):
                     self.analyse_variable(__SCREAMING_SNAKE_CASE				,					f'''output[{i}][{j}]'''		)
             else:
                 self.analyse_variable(__SCREAMING_SNAKE_CASE				,					f'''output[{i}]'''		)
     else:
         self.analyse_variable(__SCREAMING_SNAKE_CASE				,					'''output'''		)

     self.save_frame()


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
     self.model.apply(self._register_forward_hook		)


 def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  str:
     module.register_forward_hook(self.forward_hook		)





 def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A		)      ->  str:
     a					=False

     a					=True if self.batch_number in self.trace_batch_nums else False
     if trace_mode:
         self.reset_saved_frames()

     if self.total_calls == 0:
         self.batch_start_frame()
     self.total_calls += 1

     # count batch numbers - the very first forward hook of the batch will be called when the
     # batch completes - i.e. it gets called very last - we know this batch has finished
     if module == self.model:
         self.batch_number += 1
         a					=True

     self.create_frame(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE		)

     # if last_frame_of_batch:
     #     self.batch_end_frame()

     if trace_mode:
         self.trace_frames()

     if last_frame_of_batch:
         self.batch_start_frame()

     if self.detected_overflow and not trace_mode:
         self.dump_saved_frames()

         # now we can abort, as it's pointless to continue running
         raise ValueError(
             '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
             '''Please scroll up above this traceback to see the activation values prior to this event.'''		)

     # abort after certain batch if requested to do so
     if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
         raise ValueError(
             f'''DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'''
             f''' `abort_after_batch_num={self.abort_after_batch_num}` arg'''		)

def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    a					=var.abs()
    return f'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''
def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    a					=False
    if torch.isnan(lowercase	).any().item():
        a					=True
        print(f'''{ctx} has nans'''	)
    if torch.isinf(lowercase	).any().item():
        a					=True
        print(f'''{ctx} has infs'''	)

    # if needed to monitor large elements can enable the following
    if 0:  # and detected:
        a					=var[torch.ge(var.abs() ,					1_00	)]
        if naaa.numel() > 0:
            print(f'''{ctx}:  n100={naaa.numel()}'''	)
        a					=var[torch.ge(var.abs() ,					10_00	)]
        if naaaa.numel() > 0:
            print(f'''{ctx}: n1000={naaaa.numel()}'''	)
        a					=var[torch.ge(var.abs() ,					1_00_00	)]
        if naaaaa.numel() > 0:
            print(f'''{ctx}: n10000={naaaaa.numel()}'''	)

    if 0:
        print(f'''min={var.min():9.2e} max={var.max():9.2e}'''	)

    if 0:
        print(f'''min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})'''	)

    return detected






class    __A       ( lowerCAmelCase_    ):






 """simple docstring"""




 __lowerCAmelCase				      =						"""underflow_overflow"""
 __lowerCAmelCase				      =						"""tpu_metrics_debug"""



"""simple docstring"""







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    a					=TaConfig.from_pretrained(lowercase	)
    a					=FlaxTaForConditionalGeneration(config=lowercase	)
    a					=checkpoints.load_tax_checkpoint(lowercase	)

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

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

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

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

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

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

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

        # Assigning
        a					=tax_attention_key
        a					=tax_attention_out
        a					=tax_attention_query
        a					=tax_attention_value

        a					=tax_attention_layer_norm

        if split_mlp_wi:
            a					=tax_mlp_wi_a
            a					=tax_mlp_wi_a
        else:
            a					=tax_mlp_wi

        a					=tax_mlp_wo
        a					=tax_mlp_layer_norm

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

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

    # Decoder
    for layer_index in range(config.num_decoder_layers	):
        a					=f'''layers_{str(lowercase	)}'''

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

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

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

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

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

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

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

        # Assigning
        a					=tax_attention_key
        a					=tax_attention_out
        a					=tax_attention_query
        a					=tax_attention_value

        a					=tax_pre_attention_layer_norm

        a					=tax_enc_dec_attention_key
        a					=tax_enc_dec_attention_out
        a					=tax_enc_dec_attention_query
        a					=tax_enc_dec_attention_value

        a					=tax_cross_layer_norm

        if split_mlp_wi:
            a					=tax_mlp_wi_a
            a					=tax_mlp_wi_a
        else:
            a					=tax_mlp_wi

        a					=tax_mlp_wo

        a					=txa_mlp_layer_norm

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

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

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

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

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


if __name__ == "__main__":
       lowerCamelCase_     :     Dict    		=						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
       )
       parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
       parser.add_argument(
           """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
       )
       lowerCamelCase_     :     Optional[int]    		=						parser.parse_args()
       convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)