
'''simple docstring'''




import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
      import numpy as np
      import torch

      from . import (
          AlbertForPreTraining,
          BartForConditionalGeneration,
          BertForPreTraining,
          BertForQuestionAnswering,
          BertForSequenceClassification,
          CamembertForMaskedLM,
          CTRLLMHeadModel,
          DistilBertForMaskedLM,
          DistilBertForQuestionAnswering,
          DPRContextEncoder,
          DPRQuestionEncoder,
          DPRReader,
          ElectraForPreTraining,
          FlaubertWithLMHeadModel,
          GPTaLMHeadModel,
          LayoutLMForMaskedLM,
          LxmertForPreTraining,
          LxmertVisualFeatureEncoder,
          OpenAIGPTLMHeadModel,
          RobertaForMaskedLM,
          RobertaForSequenceClassification,
          TaForConditionalGeneration,
          TransfoXLLMHeadModel,
          XLMRobertaForMaskedLM,
          XLMWithLMHeadModel,
          XLNetLMHeadModel,
      )


logging.set_verbosity_info()

lowercase			:				str    		=			{
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=False		,				snake_case__=True						):




    '''simple docstring'''



    if model_type not in MODEL_CLASSES:
        raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()						)}.'						)

    A,  A,  A,  A       :   List[Any]					=						MODEL_CLASSES[model_type]

    # Initialise TF model
    if config_file in aws_config_map:
        A       :   Any					=						cached_file(snake_case__		,				snake_case__		,				force_download=not use_cached_models						)
    A       :   List[Any]					=						config_class.from_json_file(snake_case__						)
    A       :   int					=						True
    A       :   str					=						True
    print(F'Building TensorFlow model from configuration: {config}'						)
    A       :   List[str]					=						model_class(snake_case__						)

    # Load weights from tf checkpoint
    if pytorch_checkpoint_path in aws_config_map.keys():
        A       :   Tuple					=						cached_file(
            snake_case__		,				snake_case__		,				force_download=not use_cached_models						)
    # Load PyTorch checkpoint in tf2 model:
    A       :   List[str]					=						load_pytorch_checkpoint_in_tfa_model(snake_case__		,				snake_case__						)

    if compare_with_pt_model:
        A       :   Dict					=						tf_model(tf_model.dummy_inputs		,				training=snake_case__						)  # build the network

        A       :   List[Any]					=						torch.load(snake_case__		,				map_location='''cpu'''						)
        A       :   Dict					=						pt_model_class.from_pretrained(
            pretrained_model_name_or_path=snake_case__		,				config=snake_case__		,				state_dict=snake_case__						)

        with torch.no_grad():
            A       :   Union[str, Any]					=						pt_model(**pt_model.dummy_inputs						)

        A       :   Tuple					=						pto[0].numpy()
        A       :   List[str]					=						tfo[0].numpy()
        A       :   Tuple					=						np.amax(np.abs(np_pt - np_tf						)						)
        print(F'Max absolute difference between models outputs {diff}'						)
        assert diff <= 2E-2, F'Error, model absolute difference is >2e-2: {diff}'

    # Save pytorch-model
    print(F'Save TensorFlow model to {tf_dump_path}'						)
    tf_model.save_weights(snake_case__		,				save_format='''h5'''						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=False		,				snake_case__=False		,				snake_case__=False		,				snake_case__=False		,				):




    '''simple docstring'''



    if args_model_type is None:
        A       :   int					=						list(MODEL_CLASSES.keys()						)
    else:
        A       :   Any					=						[args_model_type]

    for j, model_type in enumerate(snake_case__		,				start=1						):
        print('''=''' * 100						)
        print(F' Converting model type {j}/{len(snake_case__						)}: {model_type}'						)
        print('''=''' * 100						)
        if model_type not in MODEL_CLASSES:
            raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()						)}.'						)

        A,  A,  A,  A,  A       :   Optional[int]					=						MODEL_CLASSES[model_type]

        if model_shortcut_names_or_path is None:
            A       :   Any					=						list(aws_model_maps.keys()						)
        if config_shortcut_names_or_path is None:
            A       :   List[Any]					=						model_shortcut_names_or_path

        for i, (model_shortcut_name, config_shortcut_name) in enumerate(
            zip(snake_case__		,				snake_case__						)		,				start=1						):
            print('''-''' * 100						)
            if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                if not only_convert_finetuned_models:
                    print(F'    Skipping finetuned checkpoint {model_shortcut_name}'						)
                    continue
                A       :   Tuple					=						model_shortcut_name
            elif only_convert_finetuned_models:
                print(F'    Skipping not finetuned checkpoint {model_shortcut_name}'						)
                continue
            print(
                F'    Converting checkpoint {i}/{len(snake_case__						)}: {model_shortcut_name} - model_type {model_type}'						)
            print('''-''' * 100						)

            if config_shortcut_name in aws_config_map:
                A       :   int					=						cached_file(snake_case__		,				snake_case__		,				force_download=not use_cached_models						)
            else:
                A       :   Union[str, Any]					=						config_shortcut_name

            if model_shortcut_name in aws_model_maps:
                A       :   Optional[int]					=						cached_file(snake_case__		,				snake_case__		,				force_download=not use_cached_models						)
            else:
                A       :   List[Any]					=						model_shortcut_name

            if os.path.isfile(snake_case__						):
                A       :   Any					=						'''converted_model'''

            convert_pt_checkpoint_to_tf(
                model_type=snake_case__		,				pytorch_checkpoint_path=snake_case__		,				config_file=snake_case__		,				tf_dump_path=os.path.join(snake_case__		,				model_shortcut_name + '''-tf_model.h5'''						)		,				compare_with_pt_model=snake_case__		,				)
            if remove_cached_files:
                os.remove(snake_case__						)
                os.remove(snake_case__						)


if __name__ == "__main__":
      lowercase			:				Dict    		=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
      )
      parser.add_argument(
          '--model_type',
          default=None,
          type=str,
          help=(
              f'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
              'convert all the models from AWS.'
          ),
      )
      parser.add_argument(
          '--pytorch_checkpoint_path',
          default=None,
          type=str,
          help=(
              'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
              'If not given, will download and convert all the checkpoints from AWS.'
          ),
      )
      parser.add_argument(
          '--config_file',
          default=None,
          type=str,
          help=(
              'The config json file corresponding to the pre-trained model. \n'
              'This specifies the model architecture. If not given and '
              '--pytorch_checkpoint_path is not given or is a shortcut name '
              'use the configuration associated to the shortcut name on the AWS'
          ),
      )
      parser.add_argument(
          '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
      )
      parser.add_argument(
          '--use_cached_models',
          action='store_true',
          help='Use cached models if possible instead of updating to latest checkpoint versions.',
      )
      parser.add_argument(
          '--remove_cached_files',
          action='store_true',
          help='Remove pytorch models after conversion (save memory when converting in batches).',
      )
      parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
      lowercase			:				Optional[Any]    		=			parser.parse_args()

      # if args.pytorch_checkpoint_path is not None:
      #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
      #                                 args.pytorch_checkpoint_path,
      #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
      #                                 args.tf_dump_path,
      #                                 compare_with_pt_model=args.compare_with_pt_model,
      #                                 use_cached_models=args.use_cached_models)
      # else:
      convert_all_pt_checkpoints_to_tf(
          args.model_type.lower() if args.model_type is not None else None,
          args.tf_dump_path,
          model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
          if args.pytorch_checkpoint_path is not None
          else None,
          config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
          compare_with_pt_model=args.compare_with_pt_model,
          use_cached_models=args.use_cached_models,
          remove_cached_files=args.remove_cached_files,
          only_convert_finetuned_models=args.only_convert_finetuned_models,
      )






'''simple docstring'''




import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


lowercase			:				Dict    		=			logging.get_logger(__name__)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=False						):




    '''simple docstring'''



    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            '''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'''
            ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
            ''' instructions.'''						)
        raise

    if not is_sharded:
        A       :   Union[str, Any]					=						os.path.abspath(snake_case__						)
        logger.info(F'Loading PyTorch weights from {pt_path}'						)

        A       :   Any					=						torch.load(snake_case__		,				map_location='''cpu'''						)
        logger.info(F'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()						):,} parameters.'						)

        A       :   List[str]					=						convert_pytorch_state_dict_to_flax(snake_case__		,				snake_case__						)
    else:
        # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
        A       :   Any					=						convert_pytorch_sharded_state_dict_to_flax(snake_case__		,				snake_case__						)
    return flax_state_dict







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				):




    '''simple docstring'''



    def is_key_or_prefix_key_in_dict(snake_case__						) -> bool:
        return len(set(snake_case__						) & {key, (model_prefix,) + key}						) > 0

    # layer norm
    A       :   Union[str, Any]					=						pt_tuple_key[:-1] + ('''scale''',)
    if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(snake_case__						):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer mean
    A       :   Tuple					=						pt_tuple_key[:-1] + ('''mean''',)
    if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(snake_case__						):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer var
    A       :   Dict					=						pt_tuple_key[:-1] + ('''var''',)
    if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(snake_case__						):
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    A       :   Any					=						pt_tuple_key[:-1] + ('''embedding''',)
    if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(snake_case__						):
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    A       :   Optional[Any]					=						pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(snake_case__						):
        A       :   List[Any]					=						pt_tensor.transpose(2		,				3		,				1		,				0						)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    A       :   Optional[int]					=						pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(snake_case__						):
        A       :   str					=						pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    A       :   Dict					=						pt_tuple_key[:-1] + ('''weight''',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    A       :   List[Any]					=						pt_tuple_key[:-1] + ('''bias''',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    A       :   Dict					=						None
    if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
        A       :   List[Any]					=						pt_tuple_key[-2] + '''_g'''
    elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
        A       :   List[str]					=						pt_tuple_key[-2] + '''_v'''
    if name is not None:
        A       :   int					=						pt_tuple_key[:-3] + (name,)
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						{k: v.numpy() for k, v in pt_state_dict.items()}

    A       :   int					=						flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers
    if "params" in flax_model.params:
        A       :   List[str]					=						flax_model.params['''params''']
    else:
        A       :   Dict					=						flax_model.params
    A       :   List[Any]					=						flatten_dict(snake_case__						)

    # add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
        A       :   List[str]					=						flatten_dict(flax_model.params['''batch_stats''']						)
        random_flax_state_dict.update(snake_case__						)

    A       :   int					=						{}

    A       :   List[str]					=						(model_prefix not in flax_model_params) and (
        model_prefix in {k.split('''.'''						)[0] for k in pt_state_dict.keys()}
    )
    A       :   int					=						(model_prefix in flax_model_params) and (
        model_prefix not in {k.split('''.'''						)[0] for k in pt_state_dict.keys()}
    )

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        A       :   str					=						tuple(pt_key.split('''.'''						)						)

        # remove base model prefix if necessary
        A       :   Union[str, Any]					=						pt_tuple_key[0] == model_prefix
        if load_model_with_head_into_base_model and has_base_model_prefix:
            A       :   Any					=						pt_tuple_key[1:]

        # Correctly rename weight parameters
        A,  A       :   Dict					=						rename_key_and_reshape_tensor(
            snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						)

        # add model prefix if necessary
        A       :   Any					=						(model_prefix,) + flax_key in random_flax_state_dict
        if load_base_model_into_model_with_head and require_base_model_prefix:
            A       :   int					=						(model_prefix,) + flax_key

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                    F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'						)

        # add batch stats if the model contains batchnorm layers
        if "batch_stats" in flax_model.params:
            if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                A       :   Tuple					=						jnp.asarray(snake_case__						)
                continue
            # remove num_batches_tracked key
            if "num_batches_tracked" in flax_key[-1]:
                flax_state_dict.pop(snake_case__		,				snake_case__						)
                continue

            # also add unexpected weight so that warning is thrown
            A       :   List[str]					=						jnp.asarray(snake_case__						)

        else:
            # also add unexpected weight so that warning is thrown
            A       :   Optional[Any]					=						jnp.asarray(snake_case__						)

    return unflatten_dict(snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    import torch

    # Load the index
    A       :   Union[str, Any]					=						{}
    for shard_file in shard_filenames:
        # load using msgpack utils
        A       :   List[str]					=						torch.load(snake_case__						)
        A       :   int					=						{k: v.numpy() for k, v in pt_state_dict.items()}

        A       :   Tuple					=						flax_model.base_model_prefix

        # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
        if "batch_stats" in flax_model.params:
            A       :   Optional[int]					=						flax_model.params['''params''']

            A       :   List[Any]					=						flatten_dict(snake_case__						)
            random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']						)						)
        else:
            A       :   Dict					=						flax_model.params
            A       :   Tuple					=						flatten_dict(snake_case__						)

        A       :   List[str]					=						(model_prefix not in flax_model_params) and (
            model_prefix in {k.split('''.'''						)[0] for k in pt_state_dict.keys()}
        )
        A       :   List[str]					=						(model_prefix in flax_model_params) and (
            model_prefix not in {k.split('''.'''						)[0] for k in pt_state_dict.keys()}
        )
        # Need to change some parameters name to match Flax names
        for pt_key, pt_tensor in pt_state_dict.items():
            A       :   int					=						tuple(pt_key.split('''.'''						)						)

            # remove base model prefix if necessary
            A       :   List[str]					=						pt_tuple_key[0] == model_prefix
            if load_model_with_head_into_base_model and has_base_model_prefix:
                A       :   Union[str, Any]					=						pt_tuple_key[1:]

            # Correctly rename weight parameters
            A,  A       :   Any					=						rename_key_and_reshape_tensor(
                snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						)
            # add model prefix if necessary
            A       :   int					=						(model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                A       :   int					=						(model_prefix,) + flax_key

            if flax_key in random_flax_state_dict:
                if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                        F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'						)

            # add batch stats if the model contains batchnorm layers
            if "batch_stats" in flax_model.params:
                if "mean" in flax_key[-1]:
                    A       :   Optional[int]					=						jnp.asarray(snake_case__						)
                    continue
                if "var" in flax_key[-1]:
                    A       :   Optional[int]					=						jnp.asarray(snake_case__						)
                    continue
                # remove num_batches_tracked key
                if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(snake_case__		,				snake_case__						)
                    continue

                # also add unexpected weight so that warning is thrown
                A       :   Optional[Any]					=						jnp.asarray(snake_case__						)

            else:
                # also add unexpected weight so that warning is thrown
                A       :   Optional[Any]					=						jnp.asarray(snake_case__						)
    return unflatten_dict(snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						os.path.abspath(snake_case__						)
    logger.info(F'Loading Flax weights from {flax_checkpoint_path}'						)

    # import correct flax class
    A       :   List[str]					=						getattr(snake_case__		,				'''Flax''' + model.__class__.__name__						)

    # load flax weight dict
    with open(snake_case__		,				'''rb'''						) as state_f:
        try:
            A       :   int					=						from_bytes(snake_case__		,				state_f.read()						)
        except UnpicklingError:
            raise EnvironmentError(F'Unable to convert {flax_checkpoint_path} to Flax deserializable object. '						)

    return load_flax_weights_in_pytorch_model(snake_case__		,				snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            '''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'''
            ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
            ''' instructions.'''						)
        raise

    # check if we have bf16 weights
    A       :   List[str]					=						flatten_dict(jax.tree_util.tree_map(lambda snake_case__						: x.dtype == jnp.bfloataa		,				snake_case__						)						).values()
    if any(snake_case__						):
        # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
        # and bf16 is not fully supported in PT yet.
        logger.warning(
            '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
            '''before loading those in PyTorch model.'''						)
        A       :   Optional[Any]					=						jax.tree_util.tree_map(
            lambda snake_case__						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params		,				snake_case__						)

    A       :   Union[str, Any]					=						flatten_dict(snake_case__						)
    A       :   List[Any]					=						pt_model.state_dict()

    A       :   Union[str, Any]					=						(pt_model.base_model_prefix in flax_state) and (
        pt_model.base_model_prefix not in {k.split('''.'''						)[0] for k in pt_model_dict.keys()}
    )
    A       :   Tuple					=						(pt_model.base_model_prefix not in flax_state) and (
        pt_model.base_model_prefix in {k.split('''.'''						)[0] for k in pt_model_dict.keys()}
    )

    # keep track of unexpected & missing keys
    A       :   int					=						[]
    A       :   Any					=						set(pt_model_dict.keys()						)

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        A       :   Union[str, Any]					=						flax_key_tuple[0] == pt_model.base_model_prefix
        A       :   int					=						'''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple						) in pt_model_dict

        # adapt flax_key to prepare for loading from/to base model only
        if load_model_with_head_into_base_model and has_base_model_prefix:
            A       :   List[str]					=						flax_key_tuple[1:]
        elif load_base_model_into_model_with_head and require_base_model_prefix:
            A       :   Optional[Any]					=						(pt_model.base_model_prefix,) + flax_key_tuple

        # rename flax weights to PyTorch format
        if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(snake_case__						) not in pt_model_dict:
            # conv layer
            A       :   Optional[int]					=						flax_key_tuple[:-1] + ('''weight''',)
            A       :   Optional[int]					=						jnp.transpose(snake_case__		,				(3, 2, 0, 1)						)
        elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case__						) not in pt_model_dict:
            # linear layer
            A       :   Tuple					=						flax_key_tuple[:-1] + ('''weight''',)
            A       :   Tuple					=						flax_tensor.T
        elif flax_key_tuple[-1] in ["scale", "embedding"]:
            A       :   Optional[int]					=						flax_key_tuple[:-1] + ('''weight''',)

        # adding batch stats from flax batch norm to pt
        elif "mean" in flax_key_tuple[-1]:
            A       :   Tuple					=						flax_key_tuple[:-1] + ('''running_mean''',)
        elif "var" in flax_key_tuple[-1]:
            A       :   Tuple					=						flax_key_tuple[:-1] + ('''running_var''',)

        if "batch_stats" in flax_state:
            A       :   List[Any]					=						'''.'''.join(flax_key_tuple[1:]						)  # Remove the params/batch_stats header
        else:
            A       :   Union[str, Any]					=						'''.'''.join(snake_case__						)

        # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
        A       :   int					=						{}
        # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
        for key in pt_model_dict:
            A       :   Optional[int]					=						key.split('''.'''						)
            A       :   Dict					=						None
            if key_components[-3::2] == ["parametrizations", "original0"]:
                A       :   List[str]					=						key_components[-2] + '''_g'''
            elif key_components[-3::2] == ["parametrizations", "original1"]:
                A       :   List[Any]					=						key_components[-2] + '''_v'''
            if name is not None:
                A       :   str					=						key_components[:-3] + [name]
                A       :   Optional[Any]					=						'''.'''.join(snake_case__						)
                A       :   Optional[Any]					=						key

        if flax_key in special_pt_names:
            A       :   Optional[Any]					=						special_pt_names[flax_key]

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
                    F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'						)
            else:
                # add weight to pytorch dict
                A       :   Dict					=						np.asarray(snake_case__						) if not isinstance(snake_case__		,				np.ndarray						) else flax_tensor
                A       :   Dict					=						torch.from_numpy(snake_case__						)
                # remove from missing keys
                missing_keys.remove(snake_case__						)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(snake_case__						)

    pt_model.load_state_dict(snake_case__						)

    # re-transform missing_keys to list
    A       :   List[Any]					=						list(snake_case__						)

    if len(snake_case__						) > 0:
        logger.warning(
            '''Some weights of the Flax model were not used when initializing the PyTorch model'''
            F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
            F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
            ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
            F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
            ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
            ''' FlaxBertForSequenceClassification model).'''						)
    else:
        logger.warning(F'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'						)
    if len(snake_case__						) > 0:
        logger.warning(
            F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
            F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
            ''' use it for predictions and inference.'''						)
    else:
        logger.warning(
            F'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
            '''If your task is similar to the task the model of the checkpoint was trained on, '''
            F'you can already use {pt_model.__class__.__name__} for predictions without further training.'						)

    return pt_model




