


import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		if model.config.model_type == "gpt2":
				return model.transformer.h[0].mlp.c_fc
		return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
			import torch
			import torch.nn as nn


			class        _a							(			nn.Module			):

										def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	nn.Module    ,						UpperCamelCase_:	int							)			->   Union[str, Any]:





												"""simple docstring"""







												super().__init__()
												lowercase__            =      module
												lowercase__            =      nn.Sequential(
												    nn.Linear(module.in_features    ,						UpperCamelCase_    ,						bias=UpperCamelCase_							)    ,						nn.Linear(UpperCamelCase_    ,						module.out_features    ,						bias=UpperCamelCase_							)    ,						)
												lowercase__            =      (2.0 / (5 * min(module.in_features    ,						module.out_features							))) ** 0.5
												nn.init.normal_(self.adapter[0].weight    ,						std=UpperCamelCase_							)
												nn.init.zeros_(self.adapter[1].weight							)
												self.adapter.to(module.weight.device							)


										def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	str    ,						*UpperCamelCase_:	int    ,						**UpperCamelCase_:	List[str]							)			->   Union[str, Any]:





												"""simple docstring"""







												return self.module(UpperCamelCase_    ,						*UpperCamelCase_    ,						**UpperCamelCase_							) + self.adapter(UpperCamelCase_							)


@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class        _a							(			unittest.TestCase			):
							# We keep the constants inside the init function and model loading inside setUp function

							# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
							# Therefore here we use only bloom-1b3 to test our module
							_lowercase	:			Union[str, Any]        = '''bigscience/bloom-1b7'''

							# Constant values
							_lowercase	:			List[Any]        = 2.109659552692574

							_lowercase	:			List[str]        = '''Hello my name is'''
							_lowercase	:			Union[str, Any]        = set()
							EXPECTED_OUTPUTS.add('''Hello my name is John and I am a professional photographer. I'''			)
							EXPECTED_OUTPUTS.add('''Hello my name is John.\nI am a friend of your father.\n'''			)
							EXPECTED_OUTPUTS.add('''Hello my name is John Doe, I am a student at the University'''			)
							_lowercase	:			int        = 10

							def       lowerCamelCase_ (							self:	Any							)			->   str:





									"""simple docstring"""







									lowercase__            =      AutoTokenizer.from_pretrained(self.model_name							)


class        _a							(			UpperCamelCase__			):

							def       lowerCamelCase_ (							self:	Tuple							)			->   Tuple:





									"""simple docstring"""







									super().setUp()

									# Models and tokenizer
									lowercase__            =      AutoModelForCausalLM.from_pretrained(
									    self.model_name    ,						torch_dtype=torch.floataa    ,						device_map='''auto'''							)
									lowercase__            =      AutoModelForCausalLM.from_pretrained(self.model_name    ,						load_in_abit=UpperCamelCase_    ,						device_map='''auto'''							)

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   Optional[int]:





									"""simple docstring"""







									del self.model_fpaa
									del self.model_abit

									gc.collect()
									torch.cuda.empty_cache()

							def       lowerCamelCase_ (							self:	List[str]							)			->   str:





									"""simple docstring"""







									lowercase__            =      self.model_abit.config

									self.assertTrue(hasattr(UpperCamelCase_    ,						'''quantization_config'''							)							)

									lowercase__            =      config.to_dict()
									lowercase__            =      config.to_diff_dict()

									lowercase__            =      config.to_json_string()

							def       lowerCamelCase_ (							self:	List[Any]							)			->   Optional[int]:





									"""simple docstring"""







									from bitsandbytes.nn import Paramsabit

									lowercase__            =      self.model_fpaa.get_memory_footprint()
									lowercase__            =      self.model_abit.get_memory_footprint()

									self.assertAlmostEqual(mem_fpaa / mem_abit    ,						self.EXPECTED_RELATIVE_DIFFERENCE							)
									lowercase__            =      get_some_linear_layer(self.model_abit							)
									self.assertTrue(linear.weight.__class__ == Paramsabit							)

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   Tuple:





									"""simple docstring"""







									from transformers import TaPreTrainedModel

									self.model_fpaa.get_memory_footprint()
									self.model_abit.get_memory_footprint()

									for name, module in self.model_abit.named_modules():
											if isinstance(UpperCamelCase_    ,						torch.nn.Linear							):
													if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
															# 4-bit parameters are packed in uint8 variables
															self.assertTrue(module.weight.dtype == torch.uinta							)

							def       lowerCamelCase_ (							self:	Dict							)			->   int:





									"""simple docstring"""







									lowercase__            =      self.tokenizer(self.input_text    ,						return_tensors='''pt'''							)
									lowercase__            =      self.model_abit.generate(input_ids=encoded_input['''input_ids'''].to(0							)    ,						max_new_tokens=10							)

									self.assertIn(self.tokenizer.decode(output_sequences[0]    ,						skip_special_tokens=UpperCamelCase_							)    ,						self.EXPECTED_OUTPUTS							)

							def       lowerCamelCase_ (							self:	List[Any]							)			->   Union[str, Any]:





									"""simple docstring"""







									lowercase__            =      BitsAndBytesConfig()
									lowercase__            =      True

									lowercase__            =      AutoModelForCausalLM.from_pretrained(
									    self.model_name    ,						quantization_config=UpperCamelCase_    ,						device_map='''auto'''							)

									lowercase__            =      self.tokenizer(self.input_text    ,						return_tensors='''pt'''							)
									lowercase__            =      model_abit_from_config.generate(
									    input_ids=encoded_input['''input_ids'''].to(0							)    ,						max_new_tokens=10							)

									self.assertIn(self.tokenizer.decode(output_sequences[0]    ,						skip_special_tokens=UpperCamelCase_							)    ,						self.EXPECTED_OUTPUTS							)

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   Dict:





									"""simple docstring"""







									with self.assertRaises(UpperCamelCase_							), tempfile.TemporaryDirectory() as tmpdirname:
											self.model_abit.save_pretrained(UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	int							)			->   str:





									"""simple docstring"""







									lowercase__            =      BitsAndBytesConfig()

									with self.assertRaises(UpperCamelCase_							):
											lowercase__            =      AutoModelForCausalLM.from_pretrained(
											    self.model_name    ,						quantization_config=UpperCamelCase_    ,						load_in_abit=UpperCamelCase_    ,						device_map='''auto'''    ,						bnb_abit_quant_type='''nf4'''    ,						)

							def       lowerCamelCase_ (							self:	List[str]							)			->   Any:





									"""simple docstring"""







									with self.assertRaises(UpperCamelCase_							):
											# Tries with `str`
											self.model_abit.to('''cpu'''							)

									with self.assertRaises(UpperCamelCase_							):
											# Tries with a `dtype``
											self.model_abit.to(torch.floataa							)

									with self.assertRaises(UpperCamelCase_							):
											# Tries with a `device`
											self.model_abit.to(torch.device('''cuda:0'''							)							)

									with self.assertRaises(UpperCamelCase_							):
											# Tries with a `device`
											self.model_abit.float()

									with self.assertRaises(UpperCamelCase_							):
											# Tries with a `device`
											self.model_abit.half()

									# Test if we did not break anything
									lowercase__            =      self.tokenizer(self.input_text    ,						return_tensors='''pt'''							)

									lowercase__            =      self.model_fpaa.to(torch.floataa							)
									lowercase__            =      self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0							)    ,						max_new_tokens=10							)

									# Check this does not throw an error
									lowercase__            =      self.model_fpaa.to('''cpu'''							)

									# Check this does not throw an error
									lowercase__            =      self.model_fpaa.half()

									# Check this does not throw an error
									lowercase__            =      self.model_fpaa.float()


							def       lowerCamelCase_ (							self:	str							)			->   str:





									"""simple docstring"""







									lowercase__            =      AutoModelForSeqaSeqLM.from_pretrained('''t5-small'''    ,						load_in_abit=UpperCamelCase_    ,						device_map='''auto'''							)
									self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa							)


@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class        _a							(			unittest.TestCase			):

							@classmethod
							def       lowerCamelCase_ (							cls:	Any							)			->   List[Any]:





									"""simple docstring"""







									lowercase__            =      '''t5-small'''
									lowercase__            =      '''google/flan-t5-small'''  # flan-t5 uses dense-act instead of dense-relu-dense
									lowercase__            =      AutoTokenizer.from_pretrained(cls.model_name							)
									lowercase__            =      '''Translate in German: Hello, my dog is cute'''

							def       lowerCamelCase_ (							self:	Optional[int]							)			->   Optional[int]:





									"""simple docstring"""







									gc.collect()
									torch.cuda.empty_cache()

							def       lowerCamelCase_ (							self:	Any							)			->   Dict:





									"""simple docstring"""







									from transformers import TaForConditionalGeneration

									lowercase__            =      TaForConditionalGeneration._keep_in_fpaa_modules
									lowercase__            =      None

									# test with `t5-small`
									lowercase__            =      TaForConditionalGeneration.from_pretrained(self.model_name    ,						load_in_abit=UpperCamelCase_    ,						device_map='''auto'''							)
									lowercase__            =      self.tokenizer(self.input_text    ,						return_tensors='''pt'''							).to(0							)
									lowercase__            =      model.generate(**UpperCamelCase_							)

									# test with `flan-t5-small`
									lowercase__            =      TaForConditionalGeneration.from_pretrained(
									    self.dense_act_model_name    ,						load_in_abit=UpperCamelCase_    ,						device_map='''auto'''							)
									lowercase__            =      self.tokenizer(self.input_text    ,						return_tensors='''pt'''							).to(0							)
									lowercase__            =      model.generate(**UpperCamelCase_							)
									lowercase__            =      modules


							def       lowerCamelCase_ (							self:	int							)			->   Any:





									"""simple docstring"""







									import bitsandbytes as bnb

									from transformers import TaForConditionalGeneration

									# test with `t5-small`
									lowercase__            =      TaForConditionalGeneration.from_pretrained(self.model_name    ,						load_in_abit=UpperCamelCase_    ,						device_map='''auto'''							)

									# there was a bug with decoders - this test checks that it is fixed
									self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q    ,						bnb.nn.Linearabit							)							)

									lowercase__            =      self.tokenizer(self.input_text    ,						return_tensors='''pt'''							).to(0							)
									lowercase__            =      model.generate(**UpperCamelCase_							)

									# test with `flan-t5-small`
									lowercase__            =      TaForConditionalGeneration.from_pretrained(
									    self.dense_act_model_name    ,						load_in_abit=UpperCamelCase_    ,						device_map='''auto'''							)
									lowercase__            =      self.tokenizer(self.input_text    ,						return_tensors='''pt'''							).to(0							)
									lowercase__            =      model.generate(**UpperCamelCase_							)


class        _a							(			UpperCamelCase__			):

							def       lowerCamelCase_ (							self:	Dict							)			->   Optional[Any]:





									"""simple docstring"""







									super().setUp()
									# model_name
									lowercase__            =      '''bigscience/bloom-560m'''
									lowercase__            =      '''t5-small'''

									# Different types of model

									lowercase__            =      AutoModel.from_pretrained(self.model_name    ,						load_in_abit=UpperCamelCase_    ,						device_map='''auto'''							)
									# Sequence classification model
									lowercase__            =      AutoModelForSequenceClassification.from_pretrained(
									    self.model_name    ,						load_in_abit=UpperCamelCase_    ,						device_map='''auto'''							)
									# CausalLM model
									lowercase__            =      AutoModelForCausalLM.from_pretrained(self.model_name    ,						load_in_abit=UpperCamelCase_    ,						device_map='''auto'''							)
									# Seq2seq model
									lowercase__            =      AutoModelForSeqaSeqLM.from_pretrained(
									    self.seq_to_seq_name    ,						load_in_abit=UpperCamelCase_    ,						device_map='''auto'''							)

							def       lowerCamelCase_ (							self:	Any							)			->   Any:





									"""simple docstring"""







									del self.base_model
									del self.sequence_model
									del self.model_abit
									del self.seq_to_seq_model

									gc.collect()
									torch.cuda.empty_cache()


							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   List[str]:





									"""simple docstring"""







									from bitsandbytes.nn import Paramsabit

									self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit							)

									# Other heads should be nn.Parameter
									self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter							)
									self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter							)
									self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter							)


class        _a							(			UpperCamelCase__			):

							def       lowerCamelCase_ (							self:	Dict							)			->   List[str]:





									"""simple docstring"""







									super().setUp()

							def       lowerCamelCase_ (							self:	Any							)			->   List[str]:





									"""simple docstring"""







									del self.pipe

									gc.collect()
									torch.cuda.empty_cache()


							def       lowerCamelCase_ (							self:	Optional[int]							)			->   Dict:





									"""simple docstring"""







									lowercase__            =      pipeline(
									    '''text-generation'''    ,						model=self.model_name    ,						model_kwargs={'''device_map''': '''auto''', '''load_in_4bit''': True, '''torch_dtype''': torch.floataa}    ,						max_new_tokens=self.MAX_NEW_TOKENS    ,						)

									# Real second forward pass
									lowercase__            =      self.pipe(self.input_text							)
									self.assertIn(pipeline_output[0]['''generated_text''']    ,						self.EXPECTED_OUTPUTS							)


@require_torch_multi_gpu
class        _a							(			UpperCamelCase__			):

							def       lowerCamelCase_ (							self:	Dict							)			->   Tuple:





									"""simple docstring"""







									super().setUp()


							def       lowerCamelCase_ (							self:	Any							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      AutoModelForCausalLM.from_pretrained(
									    self.model_name    ,						load_in_abit=UpperCamelCase_    ,						device_map='''balanced'''							)

									# Check correct device map
									self.assertEqual(set(model_parallel.hf_device_map.values()							)    ,						{0, 1}							)

									# Check that inference pass works on the model
									lowercase__            =      self.tokenizer(self.input_text    ,						return_tensors='''pt'''							)

									# Second real batch
									lowercase__            =      model_parallel.generate(input_ids=encoded_input['''input_ids'''].to(0							)    ,						max_new_tokens=10							)
									self.assertIn(self.tokenizer.decode(output_parallel[0]    ,						skip_special_tokens=UpperCamelCase_							)    ,						self.EXPECTED_OUTPUTS							)


class        _a							(			UpperCamelCase__			):

							def       lowerCamelCase_ (							self:	str							)			->   Any:





									"""simple docstring"""







									lowercase__            =      '''facebook/opt-350m'''
									super().setUp()


							def       lowerCamelCase_ (							self:	Dict							)			->   Any:





									"""simple docstring"""







									if version.parse(importlib.metadata.version('''bitsandbytes'''							)							) < version.parse('''0.37.0'''							):
											return

									# Step 1: freeze all parameters
									lowercase__            =      AutoModelForCausalLM.from_pretrained(self.model_name    ,						load_in_abit=UpperCamelCase_							)

									self.assertEqual(set(model.hf_device_map.values()							)    ,						{torch.cuda.current_device()}							)

									for param in model.parameters():
											lowercase__            =      False  # freeze the model - train adapters later
											if param.ndim == 1:
													# cast the small parameters (e.g. layernorm) to fp32 for stability
													lowercase__            =      param.data.to(torch.floataa							)

        # Step 2: add adapters
									for _, module in model.named_modules():
											if "OPTAttention" in repr(type(UpperCamelCase_							)							):
													lowercase__            =      LoRALayer(module.q_proj    ,						rank=16							)
													lowercase__            =      LoRALayer(module.k_proj    ,						rank=16							)
													lowercase__            =      LoRALayer(module.v_proj    ,						rank=16							)

        # Step 3: dummy batch
									lowercase__            =      self.tokenizer('''Test batch '''    ,						return_tensors='''pt'''							).to(0							)

									# Step 4: Check if the gradient is not None
									with torch.cuda.amp.autocast():
											lowercase__            =      model.forward(**UpperCamelCase_							)
											out.logits.norm().backward()

									for module in model.modules():
											if isinstance(UpperCamelCase_    ,						UpperCamelCase_							):
													self.assertTrue(module.adapter[1].weight.grad is not None							)
													self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0							)
											elif isinstance(UpperCamelCase_    ,						nn.Embedding							):
													self.assertTrue(module.weight.grad is None							)


class        _a							(			UpperCamelCase__			):
							_lowercase	:			str        = '''gpt2-xl'''
							_lowercase	:			Optional[int]        = 3.3191854854152187






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()

lowerCAmelCase       =							{
    '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 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_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()     )}.'     )

		lowercase__     ,		lowercase__     ,		lowercase__     ,		lowercase__            =      MODEL_CLASSES[model_type]

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

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

		if compare_with_pt_model:
				lowercase__            =      tf_model(tf_model.dummy_inputs						,				training=SCREAMING_SNAKE_CASE     )  # build the network

				lowercase__            =      torch.load(SCREAMING_SNAKE_CASE						,				map_location='''cpu'''     )
				lowercase__            =      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():
						lowercase__            =      pt_model(**pt_model.dummy_inputs     )

				lowercase__            =      pto[0].numpy()
				lowercase__            =      tfo[0].numpy()
				lowercase__            =      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 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=None						,				SCREAMING_SNAKE_CASE=None						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=False						,				):


		"""simple docstring"""




		if args_model_type is None:
				lowercase__            =      list(MODEL_CLASSES.keys()     )
		else:
				lowercase__            =      [args_model_type]

		for j, model_type in enumerate(SCREAMING_SNAKE_CASE						,				start=1     ):
				print('''=''' * 1_00     )
				print(f' Converting model type {j}/{len(SCREAMING_SNAKE_CASE     )}: {model_type}'     )
				print('''=''' * 1_00     )
				if model_type not in MODEL_CLASSES:
						raise ValueError(f'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()     )}.'     )

				lowercase__     ,		lowercase__     ,		lowercase__     ,		lowercase__     ,		lowercase__            =      MODEL_CLASSES[model_type]

				if model_shortcut_names_or_path is None:
						lowercase__            =      list(aws_model_maps.keys()     )
				if config_shortcut_names_or_path is None:
						lowercase__            =      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('''-''' * 1_00     )
						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
								lowercase__            =      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('''-''' * 1_00     )

						if config_shortcut_name in aws_config_map:
								lowercase__            =      cached_file(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				force_download=not use_cached_models     )
						else:
								lowercase__            =      config_shortcut_name

						if model_shortcut_name in aws_model_maps:
								lowercase__            =      cached_file(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				force_download=not use_cached_models     )
						else:
								lowercase__            =      model_shortcut_name

						if os.path.isfile(SCREAMING_SNAKE_CASE     ):
								lowercase__            =      '''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__":
			lowerCAmelCase       =							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.')
			lowerCAmelCase       =							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,
			)


