


import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


_A		:				Any									=       os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
   from run_translation import main  # noqa


set_seed(42)
_A		:				Optional[Any]									=       '''sshleifer/student_marian_en_ro_6_1'''
_A		:				Optional[int]									=       '''sshleifer/tiny-mbart'''



@require_torch
class        a					(			_UpperCAmelCase   ):





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=False    ,	SCREAMING_SNAKE_CASE_					:				Dict=None    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=True    ,	SCREAMING_SNAKE_CASE_					:				int=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=True    ,	):
        __lowerCamelCase:				List[str]     =	self.run_trainer(
            eval_steps=1    ,	max_len=12    ,	model_name=SCREAMING_SNAKE_CASE_    ,	num_train_epochs=1    ,	distributed=SCREAMING_SNAKE_CASE_    ,	extra_args_str=SCREAMING_SNAKE_CASE_    ,	predict_with_generate=SCREAMING_SNAKE_CASE_    ,	do_train=SCREAMING_SNAKE_CASE_    ,	do_eval=SCREAMING_SNAKE_CASE_    ,	do_predict=SCREAMING_SNAKE_CASE_    ,	)
        __lowerCamelCase:				str     =	TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE_    ,	"""trainer_state.json"""							)							).log_history

        if not do_eval:
           return

        __lowerCamelCase:				Optional[int]     =	[log for log in logs if """eval_loss""" in log.keys()]

        __lowerCamelCase:				Dict     =	eval_metrics[0]
        if predict_with_generate:
           assert "eval_bleu" in first_step_stats

           __lowerCamelCase:				Any     =	eval_metrics[-1]
           assert isinstance(last_step_stats["""eval_bleu"""]    ,	SCREAMING_SNAKE_CASE_							)
           assert not math.isnan(float(last_step_stats["""eval_loss"""]							)							), "eval_loss must not be `nan`"





     @require_torch_non_multi_gpu
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        self.run_seqaseq_quick()





     @require_torch_multi_gpu
     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_							)





     @require_torch_multi_gpu
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_							)





     @unittest.skip("""Requires an update of the env running those tests"""							)
     @require_torch_multi_gpu
     @require_fairscale
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_    ,	extra_args_str="""--sharded_ddp simple"""							)





     @unittest.skip("""Requires an update of the env running those tests"""							)
     @require_torch_multi_gpu
     @require_fairscale
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_    ,	extra_args_str="""--sharded_ddp simple --fp16"""							)





     @unittest.skip("""Requires an update of the env running those tests"""							)
     @require_torch_multi_gpu
     @require_fairscale
     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_    ,	extra_args_str="""--sharded_ddp zero_dp_2"""    ,	predict_with_generate=SCREAMING_SNAKE_CASE_							)





     @unittest.skip("""Requires an update of the env running those tests"""							)
     @require_torch_multi_gpu
     @require_fairscale
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        self.run_seqaseq_quick(
            distributed=SCREAMING_SNAKE_CASE_    ,	extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""    ,	predict_with_generate=SCREAMING_SNAKE_CASE_							)





     @require_apex
     @require_torch_gpu
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
        # program and it breaks other tests that run from the same pytest worker, therefore until this is
        # sorted out it must be run only in an external program, that is distributed=True in this
        # test and only under one or more gpus - if we want cpu will need to make a special test
        #
        # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
        # 2nd main() call it botches the future eval.
        #
        self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_    ,	extra_args_str="""--fp16 --fp16_backend=apex"""							)
        # test 2nd time - was getting eval_loss': nan'
        # to reproduce the problem set distributed=False
        self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_    ,	extra_args_str="""--fp16 --fp16_backend=apex"""							)





     @parameterized.expand(["""base""", """low""", """high""", """mixed"""]							)
     @require_torch_multi_gpu
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
        __lowerCamelCase:				Dict     =	{
            # test with the default log_level - should be info and thus log info once
            """base""": {"""extra_args_str""": """""", """n_matches""": 1},
            # test with low log_level and log_level_replica - should be noisy on all processes
            # now the info string should appear twice on 2 processes
            """low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
            # test with high log_level and low log_level_replica
            # now the info string should appear once only on the replica
            """high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
            # test with high log_level and log_level_replica - should be quiet on all processes
            """mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
        }

        __lowerCamelCase:				int     =	experiments[experiment_id]
        __lowerCamelCase:				List[str]     =	{"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
        __lowerCamelCase:				Any     =	"""Running training"""
        with CaptureStderr() as cl:
           self.run_seqaseq_quick(**SCREAMING_SNAKE_CASE_    ,	extra_args_str=data["""extra_args_str"""]							)
        __lowerCamelCase:				Any     =	len(re.findall(SCREAMING_SNAKE_CASE_    ,	cl.err							)							)
        self.assertEqual(SCREAMING_SNAKE_CASE_    ,	data["""n_matches"""]							)





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Tuple     =	self.run_trainer(
            eval_steps=2    ,	max_len=128    ,	model_name=SCREAMING_SNAKE_CASE_    ,	learning_rate=3E-4    ,	num_train_epochs=10    ,	distributed=SCREAMING_SNAKE_CASE_    ,	)

        # Check metrics
        __lowerCamelCase:				Optional[Any]     =	TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE_    ,	"""trainer_state.json"""							)							).log_history
        __lowerCamelCase:				Optional[Any]     =	[log for log in logs if """eval_loss""" in log.keys()]
        __lowerCamelCase:				List[Any]     =	eval_metrics[0]
        __lowerCamelCase:				Optional[Any]     =	eval_metrics[-1]

        assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
        assert isinstance(last_step_stats["""eval_bleu"""]    ,	SCREAMING_SNAKE_CASE_							)

        # test if do_predict saves generations and metrics
        __lowerCamelCase:				Optional[Any]     =	os.listdir(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	{os.path.basename(SCREAMING_SNAKE_CASE_							) for p in contents}
        assert "generated_predictions.txt" in contents
        assert "predict_results.json" in contents





     @slow
     @require_bitsandbytes
     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        from transformers.training_args import OptimizerNames

        def train_and_return_metrics(SCREAMING_SNAKE_CASE_					:				str							) -> Tuple[int, float]:
           __lowerCamelCase:				Any     =	"""--skip_memory_metrics 0"""

           __lowerCamelCase:				Optional[Any]     =	self.run_trainer(
               max_len=128    ,	model_name=SCREAMING_SNAKE_CASE_    ,	learning_rate=3E-4    ,	num_train_epochs=1    ,	optim=SCREAMING_SNAKE_CASE_    ,	distributed=SCREAMING_SNAKE_CASE_    ,	extra_args_str=SCREAMING_SNAKE_CASE_    ,	do_eval=SCREAMING_SNAKE_CASE_    ,	do_predict=SCREAMING_SNAKE_CASE_    ,	n_gpus_to_use=1    ,	)

           # Check metrics
           __lowerCamelCase:				Union[str, Any]     =	TrainerState.load_from_json(Path(SCREAMING_SNAKE_CASE_    ,	"""trainer_state.json"""							)							).log_history
           __lowerCamelCase:				Optional[Any]     =	int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20							)
           __lowerCamelCase:				Tuple     =	int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20							)

           __lowerCamelCase:				List[str]     =	logs[0]["""train_loss"""]
           return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

        __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Dict     =	train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value							)
        __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Tuple     =	train_and_return_metrics(OptimizerNames.ADAMW_BNB.value							)

        __lowerCamelCase:				Optional[int]     =	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

        __lowerCamelCase:				Union[str, Any]     =	gpu_peak_mem_orig + gpu_alloc_mem_orig
        __lowerCamelCase:				str     =	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
        __lowerCamelCase:				Optional[Any]     =	gpu_total_mem_orig - gpu_total_mem_bnb

        # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
        # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
        # in 2 bytes and the diff in optim memory usage is derived as so:
        #
        # - normal 25*8=~200MB (8 bytes per param)
        # - bnb    25*2= ~50MB (2 bytes per param)
        #
        # Thus we should expect ~150MB total memory saved.
        #
        # Peak memory should be the same - the total should be different by about that same margin
        #
        # After leaving a small margin to accommodate for differences between gpus let's check
        # that we have at least 120MB in savings
        __lowerCamelCase:				Optional[int]     =	120

        # uncomment the following if this test starts failing - requires py38 for a new print feature
        # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
        # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
        # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
        # print(f"{gpu_alloc_mem_diff=}MB")
        # print(f"{gpu_peak_mem_diff=}MB")
        # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
        # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

        self.assertGreater(
            SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	"""should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
            F' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'
            F' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'    ,	)

        self.assertGreater(
            SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	"""should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
            F' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'
            F' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'    ,	)

        self.assertEqual(
            SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	F'loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				float = 3E-3    ,	SCREAMING_SNAKE_CASE_					:				str = "adafactor"    ,	SCREAMING_SNAKE_CASE_					:				bool = False    ,	SCREAMING_SNAKE_CASE_					:				str = None    ,	SCREAMING_SNAKE_CASE_					:				int = 0    ,	SCREAMING_SNAKE_CASE_					:				bool = True    ,	SCREAMING_SNAKE_CASE_					:				bool = True    ,	SCREAMING_SNAKE_CASE_					:				bool = True    ,	SCREAMING_SNAKE_CASE_					:				bool = True    ,	SCREAMING_SNAKE_CASE_					:				int = None    ,	):
        __lowerCamelCase:				str     =	self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
        __lowerCamelCase:				Dict     =	self.get_auto_remove_tmp_dir()
        __lowerCamelCase:				List[Any]     =	F'\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(SCREAMING_SNAKE_CASE_							)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(SCREAMING_SNAKE_CASE_							)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '.split()

        __lowerCamelCase:				Tuple     =	F'\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(SCREAMING_SNAKE_CASE_							)}\n        '.split()

        __lowerCamelCase:				Tuple     =	"""
            --do_predict
        """.split()

        __lowerCamelCase:				int     =	[]
        if do_train:
           args += args_train

        if do_eval:
           args += args_eval

        if do_predict:
           args += args_predict

        if predict_with_generate:
           args += "--predict_with_generate".split()

        if do_train:
           if optim == "adafactor":
              args += "--adafactor".split()
           else:
              args += F'--optim {optim}'.split()

        if extra_args_str is not None:
           args += extra_args_str.split()

        if distributed:
           if n_gpus_to_use is None:
              __lowerCamelCase:				Optional[Any]     =	get_gpu_count()
           __lowerCamelCase:				Any     =	get_torch_dist_unique_port()
           __lowerCamelCase:				int     =	F'\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            '.split()
           __lowerCamelCase:				Any     =	[sys.executable] + distributed_args + args
           # keep for quick debug
           # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
           execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,	env=self.get_env()							)
        else:
           __lowerCamelCase:				str     =	["""run_translation.py"""] + args
           with patch.object(SCREAMING_SNAKE_CASE_    ,	"""argv"""    ,	SCREAMING_SNAKE_CASE_							):
              main()

        return output_dir






from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers.models.distilbert.modeling_tf_distilbert import (
       TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFDistilBertForMaskedLM,
       TFDistilBertForMultipleChoice,
       TFDistilBertForQuestionAnswering,
       TFDistilBertForSequenceClassification,
       TFDistilBertForTokenClassification,
       TFDistilBertModel,
   )



class        a					:





     def __init__(    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	):
        __lowerCamelCase:				Optional[int]     =	parent
        __lowerCamelCase:				Dict     =	13
        __lowerCamelCase:				List[Any]     =	7
        __lowerCamelCase:				Optional[int]     =	True
        __lowerCamelCase:				str     =	True
        __lowerCamelCase:				Dict     =	False
        __lowerCamelCase:				Union[str, Any]     =	True
        __lowerCamelCase:				Any     =	99
        __lowerCamelCase:				Dict     =	32
        __lowerCamelCase:				str     =	2
        __lowerCamelCase:				Optional[int]     =	4
        __lowerCamelCase:				Optional[Any]     =	37
        __lowerCamelCase:				Union[str, Any]     =	"""gelu"""
        __lowerCamelCase:				List[Any]     =	0.1
        __lowerCamelCase:				Dict     =	0.1
        __lowerCamelCase:				str     =	512
        __lowerCamelCase:				Dict     =	16
        __lowerCamelCase:				List[str]     =	2
        __lowerCamelCase:				Optional[int]     =	0.02
        __lowerCamelCase:				Dict     =	3
        __lowerCamelCase:				Optional[int]     =	4
        __lowerCamelCase:				Tuple     =	None





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				int     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size							)

        __lowerCamelCase:				Any     =	None
        if self.use_input_mask:
           __lowerCamelCase:				Optional[int]     =	random_attention_mask([self.batch_size, self.seq_length]							)

        __lowerCamelCase:				Optional[Any]     =	None
        __lowerCamelCase:				Optional[Any]     =	None
        __lowerCamelCase:				List[str]     =	None
        if self.use_labels:
           __lowerCamelCase:				Any     =	ids_tensor([self.batch_size]    ,	self.type_sequence_label_size							)
           __lowerCamelCase:				List[Any]     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.num_labels							)
           __lowerCamelCase:				Optional[int]     =	ids_tensor([self.batch_size]    ,	self.num_choices							)

        __lowerCamelCase:				Any     =	DistilBertConfig(
            vocab_size=self.vocab_size    ,	dim=self.hidden_size    ,	n_layers=self.num_hidden_layers    ,	n_heads=self.num_attention_heads    ,	hidden_dim=self.intermediate_size    ,	hidden_act=self.hidden_act    ,	dropout=self.hidden_dropout_prob    ,	attention_dropout=self.attention_probs_dropout_prob    ,	max_position_embeddings=self.max_position_embeddings    ,	initializer_range=self.initializer_range    ,	)

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[str]							):
        __lowerCamelCase:				str     =	TFDistilBertModel(config=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}

        __lowerCamelCase:				Optional[Any]     =	model(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				int     =	[input_ids, input_mask]

        __lowerCamelCase:				str     =	model(SCREAMING_SNAKE_CASE_							)

        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.seq_length, self.hidden_size)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        __lowerCamelCase:				Any     =	TFDistilBertForMaskedLM(config=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
        __lowerCamelCase:				str     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.seq_length, self.vocab_size)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
        __lowerCamelCase:				Optional[int]     =	TFDistilBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	{
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
        }
        __lowerCamelCase:				Tuple     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.start_logits.shape    ,	(self.batch_size, self.seq_length)							)
        self.parent.assertEqual(result.end_logits.shape    ,	(self.batch_size, self.seq_length)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        __lowerCamelCase:				Any     =	self.num_labels
        __lowerCamelCase:				Optional[Any]     =	TFDistilBertForSequenceClassification(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
        __lowerCamelCase:				Optional[int]     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        __lowerCamelCase:				Tuple     =	self.num_choices
        __lowerCamelCase:				Optional[int]     =	TFDistilBertForMultipleChoice(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_    ,	1							)    ,	(1, self.num_choices, 1)							)
        __lowerCamelCase:				str     =	tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_    ,	1							)    ,	(1, self.num_choices, 1)							)
        __lowerCamelCase:				List[Any]     =	{
            """input_ids""": multiple_choice_inputs_ids,
            """attention_mask""": multiple_choice_input_mask,
        }
        __lowerCamelCase:				int     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_choices)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Union[str, Any]     =	self.num_labels
        __lowerCamelCase:				Optional[int]     =	TFDistilBertForTokenClassification(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
        __lowerCamelCase:				str     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.seq_length, self.num_labels)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Dict     =	self.prepare_config_and_inputs()
        ((__lowerCamelCase)   ,     (__lowerCamelCase)   ,     (__lowerCamelCase)   ,     (__lowerCamelCase)   ,     (__lowerCamelCase)   ,     (__lowerCamelCase)):				Tuple     =	config_and_inputs
        __lowerCamelCase:				Dict     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict



@require_tf
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Any						      =	(
         (
             TFDistilBertModel,
             TFDistilBertForMaskedLM,
             TFDistilBertForQuestionAnswering,
             TFDistilBertForSequenceClassification,
             TFDistilBertForTokenClassification,
             TFDistilBertForMultipleChoice,
         )
         if is_tf_available()
         else None
     )
     UpperCAmelCase__ :  Dict						      =	(
         {
             "feature-extraction": TFDistilBertModel,
             "fill-mask": TFDistilBertForMaskedLM,
             "question-answering": TFDistilBertForQuestionAnswering,
             "text-classification": TFDistilBertForSequenceClassification,
             "token-classification": TFDistilBertForTokenClassification,
             "zero-shot": TFDistilBertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  Tuple						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				List[str]     =	TFDistilBertModelTester(self							)
        __lowerCamelCase:				Union[str, Any]     =	ConfigTester(self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	dim=37							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        self.config_tester.run_common_tests()





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Dict     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				List[Any]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_masked_lm(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Tuple     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_question_answering(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				List[Any]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_sequence_classification(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				List[Any]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_multiple_choice(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_token_classification(*SCREAMING_SNAKE_CASE_							)







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]							):
           __lowerCamelCase:				List[str]     =	TFDistilBertModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)



@require_tf
class        a					(			unittest.TestCase   ):





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				int     =	TFDistilBertModel.from_pretrained("""distilbert-base-uncased"""							)
        __lowerCamelCase:				Optional[Any]     =	tf.constant([[0, 1, 2, 3, 4, 5]]							)
        __lowerCamelCase:				Any     =	model(SCREAMING_SNAKE_CASE_							)[0]

        __lowerCamelCase:				Union[str, Any]     =	[1, 6, 768]
        self.assertEqual(output.shape    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	tf.constant(
            [
                [
                    [0.1926_1885, -0.1373_2955, 0.411_9799],
                    [0.2215_0156, -0.0742_2661, 0.3903_7204],
                    [0.2275_6018, -0.089_6414, 0.370_1467],
                ]
            ]							)
        tf.debugging.assert_near(output[:, :3, :3]    ,	SCREAMING_SNAKE_CASE_    ,	atol=1E-4							)


