



"""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()

A    =			{
    '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__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__=False				,      lowerCamelCase__=True			) ->							int:
							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         =					MODEL_CLASSES[model_type]

							# Initialise TF model
							if config_file in aws_config_map:
														A         =					cached_file(lowerCamelCase__				,      lowerCamelCase__				,      force_download=not use_cached_models			)
							A         =					config_class.from_json_file(lowerCamelCase__			)
							A         =					True
							A         =					True
							print(f"""Building TensorFlow model from configuration: {config}"""			)
							A         =					model_class(lowerCamelCase__			)

							# Load weights from tf checkpoint
							if pytorch_checkpoint_path in aws_config_map.keys():
														A         =					cached_file(
														    lowerCamelCase__				,      lowerCamelCase__				,      force_download=not use_cached_models			)
							# Load PyTorch checkpoint in tf2 model:
							A         =					load_pytorch_checkpoint_in_tfa_model(lowerCamelCase__				,      lowerCamelCase__			)

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

														A         =					torch.load(lowerCamelCase__				,      map_location='cpu'			)
														A         =					pt_model_class.from_pretrained(
														    pretrained_model_name_or_path=lowerCamelCase__				,      config=lowerCamelCase__				,      state_dict=lowerCamelCase__			)

														with torch.no_grad():
																					A         =					pt_model(**pt_model.dummy_inputs			)

														A         =					pto[0].numpy()
														A         =					tfo[0].numpy()
														A         =					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(lowerCamelCase__				,      save_format='h5'			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=False				,      lowerCamelCase__=False				,      lowerCamelCase__=False				,      lowerCamelCase__=False				,      ) ->							Union[str, Any]:
							if args_model_type is None:
														A         =					list(MODEL_CLASSES.keys()			)
							else:
														A         =					[args_model_type]

							for j, model_type in enumerate(lowerCamelCase__				,      start=1			):
														print('=' * 100			)
														print(f""" Converting model type {j}/{len(lowerCamelCase__			)}: {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         =					MODEL_CLASSES[model_type]

														if model_shortcut_names_or_path is None:
																					A         =					list(aws_model_maps.keys()			)
														if config_shortcut_names_or_path is None:
																					A         =					model_shortcut_names_or_path

														for i, (model_shortcut_name, config_shortcut_name) in enumerate(
														    zip(lowerCamelCase__				,      lowerCamelCase__			)				,      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         =					model_shortcut_name
																					elif only_convert_finetuned_models:
																												print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""			)
																												continue
																					print(
																					    f"""    Converting checkpoint {i}/{len(lowerCamelCase__			)}: {model_shortcut_name} - model_type {model_type}"""			)
																					print('-' * 100			)

																					if config_shortcut_name in aws_config_map:
																												A         =					cached_file(lowerCamelCase__				,      lowerCamelCase__				,      force_download=not use_cached_models			)
																					else:
																												A         =					config_shortcut_name

																					if model_shortcut_name in aws_model_maps:
																												A         =					cached_file(lowerCamelCase__				,      lowerCamelCase__				,      force_download=not use_cached_models			)
																					else:
																												A         =					model_shortcut_name

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

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


if __name__ == "__main__":
					A    =			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.')
					A    =			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"""







from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

A    =			TypeVar('T')






class      UpperCAmelCase__		(      Generic[T]     ):
		lowerCAmelCase_       :	deque[T]  # Cache store of keys
		lowerCAmelCase_       :	set[T]  # References of the keys in cache
		lowerCAmelCase_       :	int 							=     10  # Maximum capacity of cache



		def __init__( self  :    int      ,							snake_case  :    int				)			->						None:






									'''simple docstring'''

									A         =					deque()
									A         =					set()
									if not n:
																A         =					sys.maxsize
									elif n < 0:
																raise ValueError('n should be an integer greater than 0.'				)
									else:
																A         =					n



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    T				)			->						None:






									'''simple docstring'''

									if x not in self.key_reference:
																if len(self.dq_store				) == LRUCache._MAX_CAPACITY:
																							A         =					self.dq_store.pop()
																							self.key_reference.remove(snake_case				)
									else:
																self.dq_store.remove(snake_case				)

									self.dq_store.appendleft(snake_case				)
									self.key_reference.add(snake_case				)



		def 					A_		( self  :    Dict				)			->						None:






									'''simple docstring'''

									for k in self.dq_store:
																print(snake_case				)



		def __repr__( self  :    int				)			->						str:






									'''simple docstring'''

									return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store				)}"""


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							A    =			LRUCache(4)
							lru_cache.refer('A')
							lru_cache.refer(2)
							lru_cache.refer(3)
							lru_cache.refer('A')
							lru_cache.refer(4)
							lru_cache.refer(5)
							lru_cache.display()

							print(lru_cache)
							assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"



