

import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any]       ):





    """simple docstring"""


    return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]="attention"       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						SCREAMING_SNAKE_CASE_									=						np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]       )
    SCREAMING_SNAKE_CASE_									=						k_tmp.reshape(k_tmp.shape[0] ,   k_tmp.shape[1] * k_tmp.shape[2]       )
    SCREAMING_SNAKE_CASE_									=						np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]       )
    SCREAMING_SNAKE_CASE_									=						o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,   o_tmp.shape[2]       )
    SCREAMING_SNAKE_CASE_									=						np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]       )
    SCREAMING_SNAKE_CASE_									=						q_tmp.reshape(q_tmp.shape[0] ,   q_tmp.shape[1] * q_tmp.shape[2]       )
    SCREAMING_SNAKE_CASE_									=						np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]       )
    SCREAMING_SNAKE_CASE_									=						v_tmp.reshape(v_tmp.shape[0] ,   v_tmp.shape[1] * v_tmp.shape[2]       )
    return k, o, q, v


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Optional[int]=False       ):





    """simple docstring"""


    if split_mlp_wi:
        SCREAMING_SNAKE_CASE_									=						params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
        SCREAMING_SNAKE_CASE_									=						params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
        SCREAMING_SNAKE_CASE_									=						(wi_a, wi_a)
    else:
        SCREAMING_SNAKE_CASE_									=						params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		dict ,   *, _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		bool ,   _SCREAMING_SNAKE_CASE					:		bool = False       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						traverse_util.flatten_dict(variables['target']       )
    SCREAMING_SNAKE_CASE_									=						{'/'.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
    SCREAMING_SNAKE_CASE_									=						'encoder/encoder/mlp/wi_0/kernel' in old
    print('Split MLP:' ,   _SCREAMING_SNAKE_CASE       )

    SCREAMING_SNAKE_CASE_									=						collections.OrderedDict()

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

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

        # Block i, layer 1 (MLP).
        SCREAMING_SNAKE_CASE_									=						tax_layer_norm_lookup(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   'encoder' ,   'pre_mlp_layer_norm'       )
        SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						tax_mlp_lookup(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   'encoder' ,   _SCREAMING_SNAKE_CASE       )
        SCREAMING_SNAKE_CASE_									=						layer_norm
        if split_mlp_wi:
            SCREAMING_SNAKE_CASE_									=						wi[0].T
            SCREAMING_SNAKE_CASE_									=						wi[1].T
        else:
            SCREAMING_SNAKE_CASE_									=						wi.T
        SCREAMING_SNAKE_CASE_									=						wo.T
        if scalable_attention:
            # convert the rel_embedding of each layer
            SCREAMING_SNAKE_CASE_									=						tax_relpos_bias_lookup(
                _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   'encoder'       ).T

    SCREAMING_SNAKE_CASE_									=						old['encoder/encoder_norm/scale']

    if not scalable_attention:
        SCREAMING_SNAKE_CASE_									=						tax_relpos_bias_lookup(
            _SCREAMING_SNAKE_CASE ,   0 ,   'encoder'       ).T
        SCREAMING_SNAKE_CASE_									=						tax_relpos_bias_lookup(
            _SCREAMING_SNAKE_CASE ,   0 ,   'decoder'       ).T

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

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

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

            if scalable_attention:
                # convert the rel_embedding of each layer
                SCREAMING_SNAKE_CASE_									=						tax_relpos_bias_lookup(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   'decoder'       ).T

        SCREAMING_SNAKE_CASE_									=						old['decoder/decoder_norm/scale']

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

    return new


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		bool       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						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:
        SCREAMING_SNAKE_CASE_									=						state_dict['shared.weight']

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

    return state_dict


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		List[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						convert_tax_to_pytorch(
        _SCREAMING_SNAKE_CASE ,   num_layers=config.num_layers ,   is_encoder_only=_SCREAMING_SNAKE_CASE ,   scalable_attention=_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						make_state_dict(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
    model.load_state_dict(_SCREAMING_SNAKE_CASE ,   strict=_SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		bool = False ,   _SCREAMING_SNAKE_CASE					:		bool = False ,   ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						MTaConfig.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:
        SCREAMING_SNAKE_CASE_									=						UMTaEncoderModel(_SCREAMING_SNAKE_CASE       )
    else:
        SCREAMING_SNAKE_CASE_									=						UMTaForConditionalGeneration(_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 ,   _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__":
       UpperCamelCase__  :      Optional[Any]			=    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
       )
       parser.add_argument(
           "--scalable_attention",
           action="store_true",
           help="Whether the model uses scaled attention (umt5 model)",
           default=False,
       )
       UpperCamelCase__  :      Tuple			=    parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path,
           args.config_file,
           args.pytorch_dump_path,
           args.is_encoder_only,
           args.scalable_attention,
       )




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		int=7       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						None
    if token is not None:
        SCREAMING_SNAKE_CASE_									=						{'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

    # The id of a workflow (not of a workflow run)
    SCREAMING_SNAKE_CASE_									=						'636036'

    SCREAMING_SNAKE_CASE_									=						f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
    # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
    url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

    SCREAMING_SNAKE_CASE_									=						requests.get(_SCREAMING_SNAKE_CASE ,   headers=_SCREAMING_SNAKE_CASE       ).json()

    return result["workflow_runs"]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						get_daily_ci_runs(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						None
    for workflow_run in workflow_runs:
        if workflow_run["status"] == "completed":
            SCREAMING_SNAKE_CASE_									=						workflow_run['id']
            break

    return workflow_run_id


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Optional[int]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						get_last_daily_ci_runs(_SCREAMING_SNAKE_CASE       )
    if workflow_run_id is not None:
        SCREAMING_SNAKE_CASE_									=						get_artifacts_links(worflow_run_id=_SCREAMING_SNAKE_CASE ,   token=_SCREAMING_SNAKE_CASE       )
        for artifact_name in artifact_names:
            if artifact_name in artifacts_links:
                SCREAMING_SNAKE_CASE_									=						artifacts_links[artifact_name]
                download_artifact(
                    artifact_name=_SCREAMING_SNAKE_CASE ,   artifact_url=_SCREAMING_SNAKE_CASE ,   output_dir=_SCREAMING_SNAKE_CASE ,   token=_SCREAMING_SNAKE_CASE       )





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		List[Any]       ):





    """simple docstring"""


    get_last_daily_ci_artifacts(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

    SCREAMING_SNAKE_CASE_									=						{}
    for artifact_name in artifact_names:
        SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   f"""{artifact_name}.zip"""       )
        if os.path.isfile(_SCREAMING_SNAKE_CASE       ):
            SCREAMING_SNAKE_CASE_									=						{}
            with zipfile.ZipFile(_SCREAMING_SNAKE_CASE       ) as z:
                for filename in z.namelist():
                    if not os.path.isdir(_SCREAMING_SNAKE_CASE       ):
                        # read the file
                        with z.open(_SCREAMING_SNAKE_CASE       ) as f:
                            SCREAMING_SNAKE_CASE_									=						f.read().decode('UTF-8'       )

    return results


