



'''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()

UpperCAmelCase     		=   {
    '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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						int=False							,		_SCREAMING_SNAKE_CASE					:						str=True							)      ->						Tuple:







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

	lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     MODEL_CLASSES[model_type]

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

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

	if compare_with_pt_model:
		lowerCAmelCase   =     tf_model(tf_model.dummy_inputs							,		training=_SCREAMING_SNAKE_CASE							)  # build the network

		lowerCAmelCase   =     torch.load(_SCREAMING_SNAKE_CASE							,		map_location="""cpu"""							)
		lowerCAmelCase   =     pt_model_class.from_pretrained(
		    pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE							,		config=_SCREAMING_SNAKE_CASE							,		state_dict=_SCREAMING_SNAKE_CASE							)

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

		lowerCAmelCase   =     pto[0].numpy()
		lowerCAmelCase   =     tfo[0].numpy()
		lowerCAmelCase   =     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(_SCREAMING_SNAKE_CASE							,		save_format="""h5"""							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Optional[int]=None							,		_SCREAMING_SNAKE_CASE					:						int=None							,		_SCREAMING_SNAKE_CASE					:						Dict=False							,		_SCREAMING_SNAKE_CASE					:						str=False							,		_SCREAMING_SNAKE_CASE					:						Union[str, Any]=False							,		_SCREAMING_SNAKE_CASE					:						Dict=False							,		)      ->						int:







	"""simple docstring"""
	if args_model_type is None:
		lowerCAmelCase   =     list(MODEL_CLASSES.keys()							)
	else:
		lowerCAmelCase   =     [args_model_type]

	for j, model_type in enumerate(_SCREAMING_SNAKE_CASE							,		start=1							):
		print("""=""" * 100							)
		print(f' Converting model type {j}/{len(_SCREAMING_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()							)}.'							)

		lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     MODEL_CLASSES[model_type]

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

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

			if config_shortcut_name in aws_config_map:
				lowerCAmelCase   =     cached_file(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		force_download=not use_cached_models							)
			else:
				lowerCAmelCase   =     config_shortcut_name

			if model_shortcut_name in aws_model_maps:
				lowerCAmelCase   =     cached_file(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		force_download=not use_cached_models							)
			else:
				lowerCAmelCase   =     model_shortcut_name

			if os.path.isfile(_SCREAMING_SNAKE_CASE							):
				lowerCAmelCase   =     """converted_model"""

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


if __name__ == "__main__":
				UpperCAmelCase     		=   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.')
				UpperCAmelCase     		=   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 time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
				import torch

				from transformers.generation import (
				    MaxLengthCriteria,
				    MaxNewTokensCriteria,
				    MaxTimeCriteria,
				    StoppingCriteriaList,
				    validate_stopping_criteria,
				)

@require_torch
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase   =     3
							lowerCAmelCase   =     250

							lowerCAmelCase   =     ids_tensor((batch_size, length)     ,	A_				)
							lowerCAmelCase   =     torch.ones((batch_size, length)     ,	device=A_     ,	dtype=torch.float				) / length
							return input_ids, scores


						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)

							lowerCAmelCase   =     StoppingCriteriaList(
							    [
							        MaxLengthCriteria(max_length=10				),
							        MaxTimeCriteria(max_time=0.1				),
							    ]				)

							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     MaxLengthCriteria(max_length=10				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     MaxNewTokensCriteria(start_length=5     ,	max_new_tokens=5				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)

							lowerCAmelCase   =     StoppingCriteriaList([criteria]				)
							self.assertEqual(criteria_list.max_length     ,	10				)


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)

							lowerCAmelCase   =     MaxTimeCriteria(max_time=0.1				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase   =     MaxTimeCriteria(max_time=0.1     ,	initial_timestamp=time.time() - 0.2				)
							self.assertTrue(criteria(A_     ,	A_				)				)




						def     __snake_case   ( self				)     ->     Optional[int]:
							validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)     ,	10				)

							with self.assertWarns(A_				):
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)     ,	11				)

							lowerCAmelCase   =     validate_stopping_criteria(StoppingCriteriaList()     ,	11				)

							self.assertEqual(len(A_				)     ,	1				)