import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__lowercase											=      logging.get_logger(__name__)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    return field(default_factory=lambda: default						,    metadata=SCREAMING_SNAKE_CASE			)


@dataclass
class 			lowerCamelCase_  :






    '''simple docstring'''






    a__			:		List[str]      			=   list_field(
        default=[]		,			metadata={
            """help""": (
                """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
                """ of all available models"""
            )
        }		,			)

    a__			:		List[int]      			=   list_field(
        default=[8]		,			metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}				)

    a__			:		List[int]      			=   list_field(
        default=[8, 3_2, 1_2_8, 5_1_2]		,			metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}		,			)

    a__			:		bool      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}		,			)
    a__			:		bool      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}		,			)
    a__			:		bool      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}				)
    a__			:		bool      			=   field(default=UpperCAmelCase_		,			metadata={"""help""": """Use FP16 to accelerate inference."""}				)
    a__			:		bool      			=   field(default=UpperCAmelCase_		,			metadata={"""help""": """Benchmark training of model"""}				)
    a__			:		bool      			=   field(default=UpperCAmelCase_		,			metadata={"""help""": """Verbose memory tracing"""}				)
    a__			:		bool      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}		,			)
    a__			:		bool      			=   field(
        default=UpperCAmelCase_		,			metadata={
            """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
        }		,			)
    a__			:		bool      			=   field(default=UpperCAmelCase_		,			metadata={"""help""": """Trace memory line by line"""}				)
    a__			:		bool      			=   field(default=UpperCAmelCase_		,			metadata={"""help""": """Save result to a CSV file"""}				)
    a__			:		bool      			=   field(default=UpperCAmelCase_		,			metadata={"""help""": """Save all print statements in a log file"""}				)
    a__			:		bool      			=   field(default=UpperCAmelCase_		,			metadata={"""help""": """Whether to print environment information"""}				)
    a__			:		bool      			=   field(
        default=UpperCAmelCase_		,			metadata={
            """help""": (
                """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
                """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
                """ for debugging / testing and on TPU."""
            )
        }		,			)
    a__			:		str      			=   field(
        default=F'''inference_time_{round(time()				)}.csv'''		,			metadata={"""help""": """CSV filename used if saving time results to csv."""}		,			)
    a__			:		str      			=   field(
        default=F'''inference_memory_{round(time()				)}.csv'''		,			metadata={"""help""": """CSV filename used if saving memory results to csv."""}		,			)
    a__			:		str      			=   field(
        default=F'''train_time_{round(time()				)}.csv'''		,			metadata={"""help""": """CSV filename used if saving time results to csv for training."""}		,			)
    a__			:		str      			=   field(
        default=F'''train_memory_{round(time()				)}.csv'''		,			metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}		,			)
    a__			:		str      			=   field(
        default=F'''env_info_{round(time()				)}.csv'''		,			metadata={"""help""": """CSV filename used if saving environment information."""}		,			)
    a__			:		str      			=   field(
        default=F'''log_{round(time()				)}.csv'''		,			metadata={"""help""": """Log filename used if print statements are saved in log."""}		,			)
    a__			:		int      			=   field(default=3		,			metadata={"""help""": """Times an experiment will be run."""}				)
    a__			:		bool      			=   field(
        default=UpperCAmelCase_		,			metadata={
            """help""": (
                """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
                """ model weights."""
            )
        }		,			)







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        warnings.warn(
            f"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
            ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
            ''' to benchmark Transformer models.'''				,				__lowercase				,				)







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        return json.dumps(dataclasses.asdict(self)				,				indent=2)







    @property
    def     UpperCamelCase__     (			self) ->			List[str]:
        if len(self.models) <= 0:
            raise ValueError(
                '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
                ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''')
        return self.models







    @property
    def     UpperCamelCase__     (			self) ->			Any:
        if not self.multi_process:
            return False
        elif self.is_tpu:
            logger.info('''Multiprocessing is currently not possible on TPU.''')
            return False
        else:
            return True






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE="attention"			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
    __UpperCamelCase  :Optional[int]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
    __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
    __UpperCamelCase  :Union[str, Any]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
    return k, o, q, v



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







    if split_mlp_wi:
        __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
        __UpperCamelCase  :Dict    						=	params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
        __UpperCamelCase  :Optional[Any]    						=	(wi_a, wi_a)
    else:
        __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

    __UpperCamelCase  :int    						=	params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
    return wi, wo



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







    return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]



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







    __UpperCamelCase  :Tuple    						=	traverse_util.flatten_dict(variables['''target''']			)
    __UpperCamelCase  :Optional[int]    						=	{'''/'''.join(SCREAMING_SNAKE_CASE			): v for k, v in old.items()}

    # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
    __UpperCamelCase  :Union[str, Any]    						=	'''encoder/layers_0/mlp/wi_0/kernel''' in old
    print('''Split MLP:'''						,    SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Optional[Any]    						=	collections.OrderedDict()

    # Shared embeddings.
    __UpperCamelCase  :Any    						=	old['''token_embedder/embedding''']

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

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

    __UpperCamelCase  :Optional[Any]    						=	old[
        '''encoder/relpos_bias/rel_embedding'''
    ].T
    __UpperCamelCase  :Optional[int]    						=	old['''encoder/encoder_norm/scale''']

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

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

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

        __UpperCamelCase  :Optional[int]    						=	old['''decoder/decoder_norm/scale''']
        __UpperCamelCase  :Optional[Any]    						=	old[
            '''decoder/relpos_bias/rel_embedding'''
        ].T

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

    return new



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







    __UpperCamelCase  :Optional[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:
        __UpperCamelCase  :Union[str, Any]    						=	state_dict['''shared.weight''']

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            __UpperCamelCase  :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.'''			)
            __UpperCamelCase  :Optional[int]    						=	state_dict['''shared.weight''']

    return state_dict



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







    __UpperCamelCase  :int    						=	checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :str    						=	convert_tax_to_pytorch(SCREAMING_SNAKE_CASE						,    num_layers=config.num_layers						,    is_encoder_only=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Optional[int]    						=	make_state_dict(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    model.load_state_dict(SCREAMING_SNAKE_CASE						,    strict=SCREAMING_SNAKE_CASE			)



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







    __UpperCamelCase  :Union[str, Any]    						=	TaConfig.from_json_file(SCREAMING_SNAKE_CASE			)
    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:
        __UpperCamelCase  :Dict    						=	TaEncoderModel(SCREAMING_SNAKE_CASE			)
    else:
        __UpperCamelCase  :str    						=	TaForConditionalGeneration(SCREAMING_SNAKE_CASE			)

    # Load weights from tf checkpoint
    load_tax_weights_in_ta(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""			)
    model.save_pretrained(SCREAMING_SNAKE_CASE			)

    # Verify that we can load the checkpoint.
    model.from_pretrained(SCREAMING_SNAKE_CASE			)
    print('''Done'''			)


if __name__ == "__main__":
    __lowercase											=      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
    )
    __lowercase											=      parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )





