


'''simple docstring'''






import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


_A			  =  {
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def A_       (					__SCREAMING_SNAKE_CASE			:       str							)  ->	Optional[Any]:
					assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
					assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
					if args.mlm:
										assert os.path.isfile(args.token_counts							)
										assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
					else:
										assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

					assert args.teacher_type == args.student_type or (
					    args.student_type == "distilbert" and args.teacher_type == "bert"
					)
					assert os.path.isfile(args.student_config							)
					if args.student_pretrained_weights is not None:
										assert os.path.isfile(args.student_pretrained_weights							)

					if args.freeze_token_type_embds:
										assert args.student_type in ["roberta"]

					assert args.alpha_ce >= 0.0
					assert args.alpha_mlm >= 0.0
					assert args.alpha_clm >= 0.0
					assert args.alpha_mse >= 0.0
					assert args.alpha_cos >= 0.0
					assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def A_       (					__SCREAMING_SNAKE_CASE			:       Optional[Any]						, __SCREAMING_SNAKE_CASE			:       Union[str, Any]							)  ->	List[Any]:



					if args.student_type == "roberta":
										__SCREAMING_SNAKE_CASE       :				int       =			False
					elif args.student_type == "gpt2":
										__SCREAMING_SNAKE_CASE       :				List[str]       =			False


def A_       (					__SCREAMING_SNAKE_CASE			:       List[Any]						, __SCREAMING_SNAKE_CASE			:       List[Any]							)  ->	List[Any]:



					if args.student_type == "roberta":
										__SCREAMING_SNAKE_CASE       :				List[Any]       =			False


def A_       (					)  ->	str:
					__SCREAMING_SNAKE_CASE       :				Tuple       =			argparse.ArgumentParser(description='''Training'''							)
					parser.add_argument('''--force'''						, action='''store_true'''						, help='''Overwrite dump_path if it already exists.'''							)

					parser.add_argument(
					    '''--dump_path'''						, type=__SCREAMING_SNAKE_CASE						, required=__SCREAMING_SNAKE_CASE						, help='''The output directory (log, checkpoints, parameters, etc.)'''							)
					parser.add_argument(
					    '''--data_file'''						, type=__SCREAMING_SNAKE_CASE						, required=__SCREAMING_SNAKE_CASE						, help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''						, )

					parser.add_argument(
					    '''--student_type'''						, type=__SCREAMING_SNAKE_CASE						, choices=['''distilbert''', '''roberta''', '''gpt2''']						, required=__SCREAMING_SNAKE_CASE						, help='''The student type (DistilBERT, RoBERTa).'''						, )
					parser.add_argument('''--student_config'''						, type=__SCREAMING_SNAKE_CASE						, required=__SCREAMING_SNAKE_CASE						, help='''Path to the student configuration.'''							)
					parser.add_argument(
					    '''--student_pretrained_weights'''						, default=__SCREAMING_SNAKE_CASE						, type=__SCREAMING_SNAKE_CASE						, help='''Load student initialization checkpoint.'''							)

					parser.add_argument(
					    '''--teacher_type'''						, choices=['''bert''', '''roberta''', '''gpt2''']						, required=__SCREAMING_SNAKE_CASE						, help='''Teacher type (BERT, RoBERTa).'''							)
					parser.add_argument('''--teacher_name'''						, type=__SCREAMING_SNAKE_CASE						, required=__SCREAMING_SNAKE_CASE						, help='''The teacher model.'''							)

					parser.add_argument('''--temperature'''						, default=2.0						, type=__SCREAMING_SNAKE_CASE						, help='''Temperature for the softmax temperature.'''							)
					parser.add_argument(
					    '''--alpha_ce'''						, default=0.5						, type=__SCREAMING_SNAKE_CASE						, help='''Linear weight for the distillation loss. Must be >=0.'''							)
					parser.add_argument(
					    '''--alpha_mlm'''						, default=0.0						, type=__SCREAMING_SNAKE_CASE						, help='''Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'''						, )
					parser.add_argument('''--alpha_clm'''						, default=0.5						, type=__SCREAMING_SNAKE_CASE						, help='''Linear weight for the CLM loss. Must be >=0.'''							)
					parser.add_argument('''--alpha_mse'''						, default=0.0						, type=__SCREAMING_SNAKE_CASE						, help='''Linear weight of the MSE loss. Must be >=0.'''							)
					parser.add_argument(
					    '''--alpha_cos'''						, default=0.0						, type=__SCREAMING_SNAKE_CASE						, help='''Linear weight of the cosine embedding loss. Must be >=0.'''							)

					parser.add_argument(
					    '''--mlm'''						, action='''store_true'''						, help='''The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'''							)
					parser.add_argument(
					    '''--mlm_mask_prop'''						, default=0.15						, type=__SCREAMING_SNAKE_CASE						, help='''Proportion of tokens for which we need to make a prediction.'''						, )
					parser.add_argument('''--word_mask'''						, default=0.8						, type=__SCREAMING_SNAKE_CASE						, help='''Proportion of tokens to mask out.'''							)
					parser.add_argument('''--word_keep'''						, default=0.1						, type=__SCREAMING_SNAKE_CASE						, help='''Proportion of tokens to keep.'''							)
					parser.add_argument('''--word_rand'''						, default=0.1						, type=__SCREAMING_SNAKE_CASE						, help='''Proportion of tokens to randomly replace.'''							)
					parser.add_argument(
					    '''--mlm_smoothing'''						, default=0.7						, type=__SCREAMING_SNAKE_CASE						, help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'''						, )
					parser.add_argument('''--token_counts'''						, type=__SCREAMING_SNAKE_CASE						, help='''The token counts in the data_file for MLM.'''							)

					parser.add_argument(
					    '''--restrict_ce_to_mask'''						, action='''store_true'''						, help='''If true, compute the distillation loss only the [MLM] prediction distribution.'''						, )
					parser.add_argument(
					    '''--freeze_pos_embs'''						, action='''store_true'''						, help='''Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'''						, )
					parser.add_argument(
					    '''--freeze_token_type_embds'''						, action='''store_true'''						, help='''Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'''						, )

					parser.add_argument('''--n_epoch'''						, type=__SCREAMING_SNAKE_CASE						, default=3						, help='''Number of pass on the whole dataset.'''							)
					parser.add_argument('''--batch_size'''						, type=__SCREAMING_SNAKE_CASE						, default=5						, help='''Batch size (for each process).'''							)
					parser.add_argument(
					    '''--group_by_size'''						, action='''store_false'''						, help='''If true, group sequences that have similar length into the same batch. Default is true.'''						, )

					parser.add_argument(
					    '''--gradient_accumulation_steps'''						, type=__SCREAMING_SNAKE_CASE						, default=50						, help='''Gradient accumulation for larger training batches.'''						, )
					parser.add_argument('''--warmup_prop'''						, default=0.05						, type=__SCREAMING_SNAKE_CASE						, help='''Linear warmup proportion.'''							)
					parser.add_argument('''--weight_decay'''						, default=0.0						, type=__SCREAMING_SNAKE_CASE						, help='''Weight decay if we apply some.'''							)
					parser.add_argument('''--learning_rate'''						, default=5E-4						, type=__SCREAMING_SNAKE_CASE						, help='''The initial learning rate for Adam.'''							)
					parser.add_argument('''--adam_epsilon'''						, default=1E-6						, type=__SCREAMING_SNAKE_CASE						, help='''Epsilon for Adam optimizer.'''							)
					parser.add_argument('''--max_grad_norm'''						, default=5.0						, type=__SCREAMING_SNAKE_CASE						, help='''Max gradient norm.'''							)
					parser.add_argument('''--initializer_range'''						, default=0.02						, type=__SCREAMING_SNAKE_CASE						, help='''Random initialization range.'''							)

					parser.add_argument(
					    '''--fp16'''						, action='''store_true'''						, help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''						, )
					parser.add_argument(
					    '''--fp16_opt_level'''						, type=__SCREAMING_SNAKE_CASE						, default='''O1'''						, help=(
					        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
					        '''See details at https://nvidia.github.io/apex/amp.html'''
					    )						, )
					parser.add_argument('''--n_gpu'''						, type=__SCREAMING_SNAKE_CASE						, default=1						, help='''Number of GPUs in the node.'''							)
					parser.add_argument('''--local_rank'''						, type=__SCREAMING_SNAKE_CASE						, default=-1						, help='''Distributed training - Local rank'''							)
					parser.add_argument('''--seed'''						, type=__SCREAMING_SNAKE_CASE						, default=56						, help='''Random seed'''							)

					parser.add_argument('''--log_interval'''						, type=__SCREAMING_SNAKE_CASE						, default=5_00						, help='''Tensorboard logging interval.'''							)
					parser.add_argument('''--checkpoint_interval'''						, type=__SCREAMING_SNAKE_CASE						, default=40_00						, help='''Checkpoint interval.'''							)
					__SCREAMING_SNAKE_CASE       :				Optional[Any]       =			parser.parse_args()
					sanity_checks(__SCREAMING_SNAKE_CASE							)

					# ARGS #
					init_gpu_params(__SCREAMING_SNAKE_CASE							)
					set_seed(__SCREAMING_SNAKE_CASE							)
					if args.is_master:
										if os.path.exists(args.dump_path							):
															if not args.force:
																				raise ValueError(
																				    f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
																				    ''' itUse `--force` if you want to overwrite it'''							)
															else:
																				shutil.rmtree(args.dump_path							)

										if not os.path.exists(args.dump_path							):
															os.makedirs(args.dump_path							)
										logger.info(f"""Experiment will be dumped and logged in {args.dump_path}"""							)

										# SAVE PARAMS #
										logger.info(f"""Param: {args}"""							)
										with open(os.path.join(args.dump_path						, '''parameters.json'''							)						, '''w'''							) as f:
															json.dump(vars(__SCREAMING_SNAKE_CASE							)						, __SCREAMING_SNAKE_CASE						, indent=4							)
										git_log(args.dump_path							)

					__SCREAMING_SNAKE_CASE       :				str       =			MODEL_CLASSES[args.student_type]
					__SCREAMING_SNAKE_CASE       :				Dict       =			MODEL_CLASSES[args.teacher_type]

					# TOKENIZER #
					__SCREAMING_SNAKE_CASE       :				List[str]       =			teacher_tokenizer_class.from_pretrained(args.teacher_name							)
					__SCREAMING_SNAKE_CASE       :				Any       =			{}
					for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
										__SCREAMING_SNAKE_CASE       :				Tuple       =			tokenizer.all_special_tokens.index(__SCREAMING_SNAKE_CASE							)
										__SCREAMING_SNAKE_CASE       :				List[str]       =			tokenizer.all_special_ids[idx]
					logger.info(f"""Special tokens {special_tok_ids}"""							)
					__SCREAMING_SNAKE_CASE       :				int       =			special_tok_ids
					__SCREAMING_SNAKE_CASE       :				List[str]       =			tokenizer.max_model_input_sizes[args.teacher_name]

					# DATA LOADER #
					logger.info(f"""Loading data from {args.data_file}"""							)
					with open(args.data_file						, '''rb'''							) as fp:
										__SCREAMING_SNAKE_CASE       :				Dict       =			pickle.load(__SCREAMING_SNAKE_CASE							)

					if args.mlm:
										logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""							)
										with open(args.token_counts						, '''rb'''							) as fp:
															__SCREAMING_SNAKE_CASE       :				Optional[Any]       =			pickle.load(__SCREAMING_SNAKE_CASE							)

										__SCREAMING_SNAKE_CASE       :				Any       =			np.maximum(__SCREAMING_SNAKE_CASE						, 1							) ** -args.mlm_smoothing
										for idx in special_tok_ids.values():
															__SCREAMING_SNAKE_CASE       :				Optional[int]       =			0.0  # do not predict special tokens
										__SCREAMING_SNAKE_CASE       :				str       =			torch.from_numpy(__SCREAMING_SNAKE_CASE							)
					else:
										__SCREAMING_SNAKE_CASE       :				Optional[Any]       =			None

					__SCREAMING_SNAKE_CASE       :				str       =			LmSeqsDataset(params=__SCREAMING_SNAKE_CASE						, data=__SCREAMING_SNAKE_CASE							)
					logger.info('''Data loader created.'''							)

					# STUDENT #
					logger.info(f"""Loading student config from {args.student_config}"""							)
					__SCREAMING_SNAKE_CASE       :				Optional[int]       =			student_config_class.from_pretrained(args.student_config							)
					__SCREAMING_SNAKE_CASE       :				Dict       =			True

					if args.student_pretrained_weights is not None:
										logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""							)
										__SCREAMING_SNAKE_CASE       :				List[str]       =			student_model_class.from_pretrained(args.student_pretrained_weights						, config=__SCREAMING_SNAKE_CASE							)
					else:
										__SCREAMING_SNAKE_CASE       :				str       =			student_model_class(__SCREAMING_SNAKE_CASE							)

					if args.n_gpu > 0:
										student.to(f"""cuda:{args.local_rank}"""							)
					logger.info('''Student loaded.'''							)

					# TEACHER #
					__SCREAMING_SNAKE_CASE       :				List[str]       =			teacher_model_class.from_pretrained(args.teacher_name						, output_hidden_states=__SCREAMING_SNAKE_CASE							)
					if args.n_gpu > 0:
										teacher.to(f"""cuda:{args.local_rank}"""							)
					logger.info(f"""Teacher loaded from {args.teacher_name}."""							)

					# FREEZING #
					if args.freeze_pos_embs:
										freeze_pos_embeddings(__SCREAMING_SNAKE_CASE						, __SCREAMING_SNAKE_CASE							)
					if args.freeze_token_type_embds:
										freeze_token_type_embeddings(__SCREAMING_SNAKE_CASE						, __SCREAMING_SNAKE_CASE							)

					# SANITY CHECKS #
					assert student.config.vocab_size == teacher.config.vocab_size
					assert student.config.hidden_size == teacher.config.hidden_size
					assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
					if args.mlm:
										assert token_probs.size(0							) == stu_architecture_config.vocab_size

					# DISTILLER #
					torch.cuda.empty_cache()
					__SCREAMING_SNAKE_CASE       :				str       =			Distiller(
					    params=__SCREAMING_SNAKE_CASE						, dataset=__SCREAMING_SNAKE_CASE						, token_probs=__SCREAMING_SNAKE_CASE						, student=__SCREAMING_SNAKE_CASE						, teacher=__SCREAMING_SNAKE_CASE							)
					distiller.train()
					logger.info('''Let\'s go get some drinks.'''							)


if __name__ == "__main__":
						main()









from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    AutoTokenizer,
							    TFAutoModelForCausalLM,
							    TFAutoModelForSeqaSeqLM,
							    TFAutoModelForSpeechSeqaSeq,
							    TFAutoModelForVisionaSeq,
							    TFBartForConditionalGeneration,
							    TFLogitsProcessorList,
							    TFMinLengthLogitsProcessor,
							    tf_top_k_top_p_filtering,
							)

if is_tensorflow_text_available():
							import tensorflow_text as text
@require_tf
class     UpperCamelCase__					(     unittest.TestCase				):






		'''simple docstring'''






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






									"""simple docstring"""
									lowercase_	:   str  				=     tf.convert_to_tensor(
									    [
									        [
									            8.2220991,  # 3rd highest value; idx. 0
									            -0.5620044,
									            5.23229752,
									            4.0386393,
									            -6.8798378,
									            -0.54785802,
									            -3.2012153,
									            2.92777176,
									            1.88171953,
									            7.35341276,  # 5th highest value; idx. 9
									            8.43207833,  # 2nd highest value; idx. 10
									            -9.85711836,
									            -5.96209236,
									            -1.13039161,
									            -7.1115294,
									            -0.8369633,
									            -5.3186408,
									            7.06427407,
									            0.81369344,
									            -0.82023817,
									            -5.9179796,
									            0.58813443,
									            -6.99778438,
									            4.71551189,
									            -0.18771637,
									            7.44020759,  # 4th highest value; idx. 25
									            9.38450987,  # 1st highest value; idx. 26
									            2.12662941,
									            -9.32562038,
									            2.35652522,
									        ],  # cummulative prob of 5 highest values <= 0.6
									        [
									            0.58425518,
									            4.53139238,
									            -5.57510464,
									            -6.28030699,
									            -7.19529503,
									            -4.02122551,
									            1.39337037,
									            -6.06707057,
									            1.59480517,
									            -9.643119,
									            0.03907799,
									            0.67231762,
									            -8.88206726,
									            6.27115922,  # 4th highest value; idx. 13
									            2.28520723,
									            4.82767506,
									            4.30421368,
									            8.8275313,  # 2nd highest value; idx. 17
									            5.44029958,  # 5th highest value; idx. 18
									            -4.4735794,
									            7.38579536,  # 3rd highest value; idx. 20
									            -2.91051663,
									            2.61946077,
									            -2.5674762,
									            -9.48959302,
									            -4.02922645,
									            -1.35416918,
									            9.67702323,  # 1st highest value; idx. 27
									            -5.89478553,
									            1.85370467,
									        ],  # cummulative prob of 5 highest values <= 0.6
									    ],   dtype=tf.floataa,   )

									lowercase_	:   int  				=     tf.convert_to_tensor(
									    [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]],   dtype=tf.intaa,   )  # expected non filtered idx as noted above

									lowercase_	:   List[Any]  				=     tf.convert_to_tensor(
									    [8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023],   dtype=tf.floataa,   )  # expected non filtered values as noted above

									lowercase_	:   int  				=     tf_top_k_top_p_filtering(snake_case__,   top_k=10,   top_p=0.6,   min_tokens_to_keep=4  )

									lowercase_	:   Dict  				=     output[output != -float("""inf"""  )]
									lowercase_	:   Tuple  				=     tf.cast(
									    tf.where(tf.not_equal(snake_case__,   tf.constant(-float("""inf"""  ),   dtype=tf.floataa  )  )  ),   dtype=tf.intaa,   )

									tf.debugging.assert_near(snake_case__,   snake_case__,   rtol=1E-12  )
									tf.debugging.assert_equal(snake_case__,   snake_case__  )




@require_tf
class     UpperCamelCase__					(     unittest.TestCase				, lowerCamelCase__				):






		'''simple docstring'''






		if is_tf_available():
				__a       :       Optional[int]       =	{
				    """AutoModelForCausalLM""": TFAutoModelForCausalLM,
				    """AutoModelForSpeechSeq2Seq""": TFAutoModelForSpeechSeqaSeq,
				    """AutoModelForSeq2SeqLM""": TFAutoModelForSeqaSeqLM,
				    """AutoModelForVision2Seq""": TFAutoModelForVisionaSeq,
				    """LogitsProcessorList""": TFLogitsProcessorList,
				    """MinLengthLogitsProcessor""": TFMinLengthLogitsProcessor,
				    """create_tensor_fn""": tf.convert_to_tensor,
				    """floats_tensor""": floats_tensor,
				    """return_tensors""": """tf""",
				}






		@slow
		def 						snake_case__				(							self  )       -> str:






									"""simple docstring"""
									# TF-only test: tf.saved_model export
									lowercase_	:   Optional[Any]  				=     TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""  )
									lowercase_	:   int  				=     2
									lowercase_	:   Optional[Any]  				=     2
									class     UpperCamelCase__					(     tf.Module				):






											'''simple docstring'''






											def __init__(							self,   snake_case__  )       -> Any:






																		"""simple docstring"""
																		super(snake_case__,   self  ).__init__()
																		lowercase_	:   int  				=     model






											@tf.function(
											    input_signature=(
											        tf.TensorSpec((None, input_length),   tf.intaa,   name="""input_ids"""  ),
											        tf.TensorSpec((None, input_length),   tf.intaa,   name="""attention_mask"""  ),
											    ),   jit_compile=snake_case__,   )
											def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> int:






																		"""simple docstring"""
																		lowercase_	:   Tuple  				=     self.model.generate(
																		    input_ids=snake_case__,   attention_mask=snake_case__,   max_new_tokens=snake_case__,   return_dict_in_generate=snake_case__,   )
																		return {"sequences": outputs["sequences"]}




									lowercase_	:   Union[str, Any]  				=     [[2, 0], [1_02, 1_03]]
									lowercase_	:   Tuple  				=     [[1, 0], [1, 1]]
									lowercase_	:   int  				=     DummyModel(model=snake_case__  )
									with tempfile.TemporaryDirectory() as tmp_dir:
																tf.saved_model.save(snake_case__,   snake_case__,   signatures={"""serving_default""": dummy_model.serving}  )
																lowercase_	:   List[Any]  				=     tf.saved_model.load(snake_case__  ).signatures["""serving_default"""]
																for batch_size in range(1,   len(snake_case__  ) + 1  ):
																							lowercase_	:   str  				=     {
																							    """input_ids""": tf.constant(dummy_input_ids[:batch_size]  ),
																							    """attention_mask""": tf.constant(dummy_attention_masks[:batch_size]  ),
																							}
																							lowercase_	:   Optional[int]  				=     serving_func(**snake_case__  )["""sequences"""]
																							lowercase_	:   Optional[int]  				=     test_model.generate(**snake_case__,   max_new_tokens=snake_case__  )
																							tf.debugging.assert_equal(snake_case__,   snake_case__  )






		@slow
		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									# TF-only test: tf.saved_model export
									lowercase_	:   int  				=     TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""  )
									lowercase_	:   Tuple  				=     1
									lowercase_	:   Tuple  				=     2
									class     UpperCamelCase__					(     tf.Module				):






											'''simple docstring'''






											def __init__(							self,   snake_case__  )       -> List[str]:






																		"""simple docstring"""
																		super(snake_case__,   self  ).__init__()
																		lowercase_	:   Union[str, Any]  				=     model






											@tf.function(
											    input_signature=(
											        tf.TensorSpec((batch_size, None),   tf.intaa,   name="""input_ids"""  ),
											        tf.TensorSpec((batch_size, None),   tf.intaa,   name="""attention_mask"""  ),
											    ),   jit_compile=snake_case__,   )
											def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> Optional[Any]:






																		"""simple docstring"""
																		lowercase_	:   str  				=     self.model.generate(
																		    input_ids=snake_case__,   attention_mask=snake_case__,   max_new_tokens=snake_case__,   return_dict_in_generate=snake_case__,   )
																		return {"sequences": outputs["sequences"]}




									lowercase_	:   Union[str, Any]  				=     [[2], [1_02, 1_03]]
									lowercase_	:   List[str]  				=     [[1], [1, 1]]
									lowercase_	:   Union[str, Any]  				=     DummyModel(model=snake_case__  )
									with tempfile.TemporaryDirectory() as tmp_dir:
																tf.saved_model.save(snake_case__,   snake_case__,   signatures={"""serving_default""": dummy_model.serving}  )
																lowercase_	:   Optional[Any]  				=     tf.saved_model.load(snake_case__  ).signatures["""serving_default"""]
																for input_row in range(len(snake_case__  )  ):
																							lowercase_	:   List[str]  				=     {
																							    """input_ids""": tf.constant([dummy_input_ids[input_row]]  ),
																							    """attention_mask""": tf.constant([dummy_attention_masks[input_row]]  ),
																							}
																							lowercase_	:   Union[str, Any]  				=     serving_func(**snake_case__  )["""sequences"""]
																							lowercase_	:   Optional[Any]  				=     test_model.generate(**snake_case__,   max_new_tokens=snake_case__  )
																							tf.debugging.assert_equal(snake_case__,   snake_case__  )






		@slow
		@require_tensorflow_text
		def 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									# TF-only test: tf.saved_model export
									with tempfile.TemporaryDirectory() as tmp_dir:
																# file needed to load the TF tokenizer
																hf_hub_download(repo_id="""google/flan-t5-small""",   filename="""spiece.model""",   local_dir=snake_case__  )
																class     UpperCamelCase__					(     tf.keras.layers.Layer				):






																		'''simple docstring'''






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






																									"""simple docstring"""
																									super().__init__()
																									lowercase_	:   Tuple  				=     text.SentencepieceTokenizer(
																									    model=tf.io.gfile.GFile(os.path.join(snake_case__,   """spiece.model"""  ),   """rb"""  ).read()  )
																									lowercase_	:   Dict  				=     TFAutoModelForSeqaSeqLM.from_pretrained("""hf-internal-testing/tiny-random-t5"""  )






																		def 						snake_case__				(							self,   snake_case__,   *snake_case__,   **snake_case__  )       -> Optional[Any]:






																									"""simple docstring"""
																									lowercase_	:   Union[str, Any]  				=     self.tokenizer.tokenize(snake_case__  )
																									lowercase_				,		lowercase_	:   Union[str, Any]  				=     text.pad_model_inputs(
																									    snake_case__,   max_seq_length=64,   pad_value=self.model.config.pad_token_id  )
																									lowercase_	:   str  				=     self.model.generate(input_ids=snake_case__,   attention_mask=snake_case__  )
																									return self.tokenizer.detokenize(snake_case__  )




																lowercase_	:   Optional[int]  				=     CompleteSentenceTransformer()
																lowercase_	:   Union[str, Any]  				=     tf.keras.layers.Input(shape=(1,),   dtype=tf.string,   name="""inputs"""  )
																lowercase_	:   Any  				=     complete_model(snake_case__  )
																lowercase_	:   Optional[Any]  				=     tf.keras.Model(snake_case__,   snake_case__  )
																keras_model.save(snake_case__  )






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






									"""simple docstring"""
									# Has PT equivalent: this test relies on random sampling
									lowercase_	:   Any  				=     {
									    """do_sample""": True,
									    """num_beams""": 1,
									    """top_p""": 0.7,
									    """top_k""": 10,
									    """temperature""": 0.7,
									}
									lowercase_	:   List[str]  				=     14

									lowercase_	:   Optional[Any]  				=     AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""  )
									lowercase_	:   Dict  				=     """Hello, my dog is cute and"""
									lowercase_	:   List[str]  				=     tokenizer(snake_case__,   return_tensors="""tf"""  )
									lowercase_	:   Tuple  				=     TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""  )

									lowercase_	:   int  				=     6_38
									# forces the generation to happen on CPU, to avoid GPU-related quirks
									with tf.device(""":/CPU:0"""  ):
																tf.random.set_seed(0  )
																lowercase_	:   Optional[int]  				=     model.generate(**snake_case__,   eos_token_id=snake_case__,   **snake_case__  )
									self.assertTrue(expectation == len(generated_tokens[0]  )  )

									lowercase_	:   int  				=     [6_38, 1_98]
									with tf.device(""":/CPU:0"""  ):
																tf.random.set_seed(0  )
																lowercase_	:   Optional[int]  				=     model.generate(**snake_case__,   eos_token_id=snake_case__,   **snake_case__  )
									self.assertTrue(expectation == len(generated_tokens[0]  )  )






		def 						snake_case__				(							self  )       -> str:






									"""simple docstring"""
									# Has PT equivalent: ample use of framework-specific code
									lowercase_	:   List[Any]  				=     AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bart"""  )
									lowercase_	:   int  				=     """Hugging Face is a technology company based in New York and Paris."""
									lowercase_	:   int  				=     bart_tokenizer(snake_case__,   return_tensors="""tf"""  ).input_ids
									lowercase_	:   Tuple  				=     TFBartForConditionalGeneration.from_pretrained("""hf-internal-testing/tiny-random-bart"""  )
									lowercase_	:   Union[str, Any]  				=     bart_model.generate(snake_case__  ).numpy()
									class     UpperCamelCase__					(     lowerCamelCase__				):






											'''simple docstring'''






											def 						snake_case__				(							self,   snake_case__,   snake_case__=None,   **snake_case__  )       -> str:






																		"""simple docstring"""
																		return super().call(snake_case__,   **snake_case__  )




									lowercase_	:   Tuple  				=     FakeBart.from_pretrained("""hf-internal-testing/tiny-random-bart"""  )
									lowercase_	:   str  				=     bart_model.generate(snake_case__,   foo="""bar"""  ).numpy()
									self.assertTrue(np.array_equal(snake_case__,   snake_case__  )  )
									class     UpperCamelCase__					(     bart_model.model.encoder.__class__				):






											'''simple docstring'''






											def 						snake_case__				(							self,   snake_case__,   **snake_case__  )       -> List[str]:






																		"""simple docstring"""
																		return super().call(snake_case__,   **snake_case__  )




									lowercase_	:   Optional[int]  				=     FakeEncoder(bart_model.config,   bart_model.model.shared  )
									lowercase_	:   Any  				=     fake_encoder

									# Normal generation still works (the output will be different because the encoder weights are different)
									lowercase_	:   Any  				=     bart_model.generate(snake_case__  ).numpy()
									with self.assertRaises(snake_case__  ):
																# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
																bart_model.generate(snake_case__,   foo="""bar"""  )