
"""simple docstring"""




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     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		Optional[Any]							,		UpperCamelCase     :		int							,		UpperCamelCase     :		Tuple="attention"     )     ->						Union[str, Any]:
    a__		=			params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
    a__		=			params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
    a__		=			params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
    a__		=			params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
    return k, o, q, v



def     __magic_name__		(					UpperCamelCase     :		Union[str, Any]							,		UpperCamelCase     :		Any							,		UpperCamelCase     :		List[str]							,		UpperCamelCase     :		int=False     )     ->						Dict:
    if split_mlp_wi:
        a__		=			params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
        a__		=			params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
        a__		=			(wi_a, wi_a)
    else:
        a__		=			params[f'{prefix}/layers_{i}/mlp/wi/kernel']

    a__		=			params[f'{prefix}/layers_{i}/mlp/wo/kernel']
    return wi, wo



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		Any							,		UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		List[Any]     )     ->						Optional[int]:
    return params[f'{prefix}/layers_{i}/{layer_name}/scale']



def     __magic_name__		(					UpperCamelCase     :		str							,		*, UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		Dict     )     ->						Optional[int]:
    a__		=			traverse_util.flatten_dict(variables['target']     )
    a__		=			{'/'.join(UpperCamelCase     ): v for k, v in old.items()}

    # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
    a__		=			'encoder/layers_0/mlp/wi_0/kernel' in old
    print('Split MLP:'							,		UpperCamelCase     )

    a__		=			collections.OrderedDict()

    # Shared embeddings.
    a__		=			old['token_embedder/embedding']

    # Encoder.
    for i in range(UpperCamelCase     ):
        # Block i, layer 0 (Self Attention).
        a__		=			tax_layer_norm_lookup(UpperCamelCase							,		UpperCamelCase							,		'encoder'							,		'pre_attention_layer_norm'     )
        a__	,     a__	,     a__	,     a__		=			tax_attention_lookup(UpperCamelCase							,		UpperCamelCase							,		'encoder'							,		'attention'     )
        a__		=			layer_norm
        a__		=			k.T
        a__		=			o.T
        a__		=			q.T
        a__		=			v.T

        # Block i, layer 1 (MLP).
        a__		=			tax_layer_norm_lookup(UpperCamelCase							,		UpperCamelCase							,		'encoder'							,		'pre_mlp_layer_norm'     )
        a__	,     a__		=			tax_mlp_lookup(UpperCamelCase							,		UpperCamelCase							,		'encoder'							,		UpperCamelCase     )
        a__		=			layer_norm
        if split_mlp_wi:
            a__		=			wi[0].T
            a__		=			wi[1].T
        else:
            a__		=			wi.T
        a__		=			wo.T

    a__		=			old[
        'encoder/relpos_bias/rel_embedding'
    ].T
    a__		=			old['encoder/encoder_norm/scale']

    if not is_encoder_only:
        # Decoder.
        for i in range(UpperCamelCase     ):
            # Block i, layer 0 (Self Attention).
            a__		=			tax_layer_norm_lookup(UpperCamelCase							,		UpperCamelCase							,		'decoder'							,		'pre_self_attention_layer_norm'     )
            a__	,     a__	,     a__	,     a__		=			tax_attention_lookup(UpperCamelCase							,		UpperCamelCase							,		'decoder'							,		'self_attention'     )
            a__		=			layer_norm
            a__		=			k.T
            a__		=			o.T
            a__		=			q.T
            a__		=			v.T

            # Block i, layer 1 (Cross Attention).
            a__		=			tax_layer_norm_lookup(UpperCamelCase							,		UpperCamelCase							,		'decoder'							,		'pre_cross_attention_layer_norm'     )
            a__	,     a__	,     a__	,     a__		=			tax_attention_lookup(UpperCamelCase							,		UpperCamelCase							,		'decoder'							,		'encoder_decoder_attention'     )
            a__		=			layer_norm
            a__		=			k.T
            a__		=			o.T
            a__		=			q.T
            a__		=			v.T

            # Block i, layer 2 (MLP).
            a__		=			tax_layer_norm_lookup(UpperCamelCase							,		UpperCamelCase							,		'decoder'							,		'pre_mlp_layer_norm'     )
            a__	,     a__		=			tax_mlp_lookup(UpperCamelCase							,		UpperCamelCase							,		'decoder'							,		UpperCamelCase     )
            a__		=			layer_norm
            if split_mlp_wi:
                a__		=			wi[0].T
                a__		=			wi[1].T
            else:
                a__		=			wi.T
            a__		=			wo.T

        a__		=			old['decoder/decoder_norm/scale']
        a__		=			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:
            a__		=			old['decoder/logits_dense/kernel'].T

    return new



def     __magic_name__		(					UpperCamelCase     :		List[str]							,		UpperCamelCase     :		List[Any]     )     ->						List[Any]:
    a__		=			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:
        a__		=			state_dict['shared.weight']

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

    return state_dict



def     __magic_name__		(					UpperCamelCase     :		Dict							,		UpperCamelCase     :		Optional[int]							,		UpperCamelCase     :		Union[str, Any]							,		UpperCamelCase     :		List[str]     )     ->						Optional[int]:
    a__		=			checkpoints.load_tax_checkpoint(UpperCamelCase     )
    a__		=			convert_tax_to_pytorch(UpperCamelCase							,		num_layers=config.num_layers							,		is_encoder_only=UpperCamelCase     )
    a__		=			make_state_dict(UpperCamelCase							,		UpperCamelCase     )
    model.load_state_dict(UpperCamelCase							,		strict=UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		Dict							,		UpperCamelCase     :		Tuple							,		UpperCamelCase     :		Tuple							,		UpperCamelCase     :		Union[str, Any] = False     )     ->						Union[str, Any]:
    a__		=			TaConfig.from_json_file(UpperCamelCase     )
    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:
        a__		=			TaEncoderModel(UpperCamelCase     )
    else:
        a__		=			TaForConditionalGeneration(UpperCamelCase     )

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

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

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


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








'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						__snake_case						 =   [0] * len(a							)
						__snake_case						 =   []
						__snake_case						 =   []
						__snake_case						 =   0

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(a							)							):
												if indegree[i] == 0:
																		queue.append(a							)

						while queue:
												__snake_case						 =   queue.pop(0							)
												cnt += 1
												topo.append(a							)
												for x in graph[vertex]:
																		indegree[x] -= 1
																		if indegree[x] == 0:
																								queue.append(a							)

						if cnt != len(a							):
												print('Cycle exists'							)
						else:
												print(a							)


# Adjacency List of Graph
_lowercase  =			{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}






topological_sort(graph)

