


'''simple docstring'''



# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


A__						:   List[str]				=    Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

A__						:   List[str]				=    {"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

A__						:   Tuple				=    """zero2"""
A__						:   str				=    """zero3"""
A__						:   int				=    [ZEROa, ZEROa]



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Any ,       UpperCAmelCase_       :							Any ,       UpperCAmelCase_       :							str				)	->						int:
     # customize the test name generator function as we want both params to appear in the sub-test
     # name, as by default it shows only the first param
     __lowerCamelCase  :      Dict														=						parameterized.to_safe_name('_'.join(str(UpperCAmelCase_				) for x in param.args				)				)
     return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
A__						:   Union[str, Any]				=    list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    @parameterized.expand(SCREAMING_SNAKE_CASE_     ,	name_func=SCREAMING_SNAKE_CASE_      )
    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Dict:
         self.run_and_check(
             stage=SCREAMING_SNAKE_CASE_     ,	model=SCREAMING_SNAKE_CASE_     ,	distributed=SCREAMING_SNAKE_CASE_     ,	fpaa=SCREAMING_SNAKE_CASE_     ,	)







    @require_torch_multi_gpu
    @parameterized.expand(SCREAMING_SNAKE_CASE_     ,	name_func=SCREAMING_SNAKE_CASE_      )
    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Tuple:
         self.run_and_check(
             stage=SCREAMING_SNAKE_CASE_     ,	model=SCREAMING_SNAKE_CASE_     ,	distributed=SCREAMING_SNAKE_CASE_     ,	fpaa=SCREAMING_SNAKE_CASE_     ,	)







    @parameterized.expand(SCREAMING_SNAKE_CASE_     ,	name_func=SCREAMING_SNAKE_CASE_      )
    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         self.run_and_check(
             stage=SCREAMING_SNAKE_CASE_     ,	model=SCREAMING_SNAKE_CASE_     ,	distributed=SCREAMING_SNAKE_CASE_     ,	fpaa=SCREAMING_SNAKE_CASE_     ,	)







    @require_torch_multi_gpu
    @parameterized.expand(SCREAMING_SNAKE_CASE_     ,	name_func=SCREAMING_SNAKE_CASE_      )
    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         self.run_and_check(
             stage=SCREAMING_SNAKE_CASE_     ,	model=SCREAMING_SNAKE_CASE_     ,	distributed=SCREAMING_SNAKE_CASE_     ,	fpaa=SCREAMING_SNAKE_CASE_     ,	)







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         # XXX: run_asr is premature and doesn't save any results
         # so all we check for now is that the process didn't fail
         pass







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = 10     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = True     ,	)							->	str:
         __lowerCamelCase  :      List[Any]														=						models[model]

         __lowerCamelCase  :      Union[str, Any]														=						self.run_trainer(
             stage=SCREAMING_SNAKE_CASE_     ,	model_name=SCREAMING_SNAKE_CASE_     ,	eval_steps=SCREAMING_SNAKE_CASE_     ,	num_train_epochs=1     ,	distributed=SCREAMING_SNAKE_CASE_     ,	fpaa=SCREAMING_SNAKE_CASE_     ,	)

         self.do_checks(SCREAMING_SNAKE_CASE_      )

         return output_dir







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = 10     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = True     ,	)							->	Optional[Any]:
         __lowerCamelCase  :      Optional[int]														=						self.get_auto_remove_tmp_dir('./xxx'     ,	after=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      int														=						f'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(SCREAMING_SNAKE_CASE_      )}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

         if fpaa:
              args.extend(['--fp16']      )

         # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
         # hence the separate config files
         __lowerCamelCase  :      str														=						f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
         __lowerCamelCase  :      Any														=						[f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
         __lowerCamelCase  :      Optional[int]														=						self.get_launcher(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Union[str, Any]														=						launcher + script + args + ds_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()      )

         return output_dir







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=False      )							->	Optional[int]:
         # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
         # - it won't be able to handle that
         # 2. for now testing with just 2 gpus max (since some quality tests may give different
         # results with mode gpus because we use very little data)
         __lowerCamelCase  :      List[Any]														=						min(2     ,	get_gpu_count()      ) if distributed else 1
         return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()










'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
      import tensorflow as tf

      from transformers.models.xglm.modeling_tf_xglm import (
          TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXGLMForCausalLM,
          TFXGLMModel,
      )





@require_tf
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   Dict					=     XGLMConfig
    lowerCamelCase    :   List[str]					=     {}
    lowerCamelCase    :   Union[str, Any]					=     'gelu'







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=14     ,	SCREAMING_SNAKE_CASE_=7     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=99     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=5_12     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	)							->	Any:
         __lowerCamelCase  :      int														=						parent
         __lowerCamelCase  :      Optional[int]														=						batch_size
         __lowerCamelCase  :      Optional[Any]														=						seq_length
         __lowerCamelCase  :      Optional[int]														=						is_training
         __lowerCamelCase  :      str														=						use_input_mask
         __lowerCamelCase  :      Dict														=						use_labels
         __lowerCamelCase  :      Union[str, Any]														=						vocab_size
         __lowerCamelCase  :      List[Any]														=						d_model
         __lowerCamelCase  :      List[Any]														=						num_hidden_layers
         __lowerCamelCase  :      List[Any]														=						num_attention_heads
         __lowerCamelCase  :      Optional[Any]														=						ffn_dim
         __lowerCamelCase  :      List[Any]														=						activation_function
         __lowerCamelCase  :      List[Any]														=						activation_dropout
         __lowerCamelCase  :      List[Any]														=						attention_dropout
         __lowerCamelCase  :      Union[str, Any]														=						max_position_embeddings
         __lowerCamelCase  :      Tuple														=						initializer_range
         __lowerCamelCase  :      int														=						None
         __lowerCamelCase  :      int														=						0
         __lowerCamelCase  :      Tuple														=						2
         __lowerCamelCase  :      Tuple														=						1







    def  lowercase_							(				self      )							->	Any:
         return XGLMConfig.from_pretrained('facebook/xglm-564M'      )







    def  lowercase_							(				self      )							->	Tuple:
         __lowerCamelCase  :      Optional[Any]														=						tf.clip_by_value(
             ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size      )     ,	clip_value_min=0     ,	clip_value_max=3      )

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

         __lowerCamelCase  :      str														=						self.get_config()

         __lowerCamelCase  :      List[Any]														=						floats_tensor([self.num_hidden_layers, self.num_attention_heads]     ,	2      )

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
         )







    def  lowercase_							(				self      )							->	Optional[int]:
         return XGLMConfig(
             vocab_size=self.vocab_size     ,	d_model=self.hidden_size     ,	num_layers=self.num_hidden_layers     ,	attention_heads=self.num_attention_heads     ,	ffn_dim=self.ffn_dim     ,	activation_function=self.activation_function     ,	activation_dropout=self.activation_dropout     ,	attention_dropout=self.attention_dropout     ,	max_position_embeddings=self.max_position_embeddings     ,	initializer_range=self.initializer_range     ,	use_cache=SCREAMING_SNAKE_CASE_     ,	bos_token_id=self.bos_token_id     ,	eos_token_id=self.eos_token_id     ,	pad_token_id=self.pad_token_id     ,	return_dict=SCREAMING_SNAKE_CASE_     ,	)







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      List[Any]														=						self.prepare_config_and_inputs()

         (
             (
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			
         )  :      str														=						config_and_inputs

         __lowerCamelCase  :      Union[str, Any]														=						{
             'input_ids': input_ids,
             'head_mask': head_mask,
         }

         return config, inputs_dict







@require_tf
class   UpperCAmelCase_    (_UpperCAmelCase   ,						_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Optional[Any]					=     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
    lowerCamelCase    :   List[Any]					=     (TFXGLMForCausalLM,) if is_tf_available() else ()
    lowerCamelCase    :   Any					=     (
        {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
    )
    lowerCamelCase    :   List[Any]					=     False
    lowerCamelCase    :   Dict					=     False
    lowerCamelCase    :   Union[str, Any]					=     False







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      str														=						TFXGLMModelTester(self      )
         __lowerCamelCase  :      List[Any]														=						ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	n_embd=37      )







    def  lowercase_							(				self      )							->	Dict:
         self.config_tester.run_common_tests()







    @slow
    def  lowercase_							(				self      )							->	Optional[int]:
         for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowerCamelCase  :      Optional[Any]														=						TFXGLMModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )







    @unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'      )
    def  lowercase_							(				self      )							->	Any:
         super().test_resize_token_embeddings()







@require_tf
class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    @slow
    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=True      )							->	List[str]:
         __lowerCamelCase  :      Optional[Any]														=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'      )
         __lowerCamelCase  :      int														=						tf.convert_to_tensor([[2, 2_68, 98_65]]     ,	dtype=tf.intaa      )  # The dog
         # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
         # fmt: off
         __lowerCamelCase  :      Optional[int]														=						[2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
         # fmt: on
         __lowerCamelCase  :      Any														=						model.generate(SCREAMING_SNAKE_CASE_     ,	do_sample=SCREAMING_SNAKE_CASE_     ,	num_beams=1      )
         if verify_outputs:
              self.assertListEqual(output_ids[0].numpy().tolist()     ,	SCREAMING_SNAKE_CASE_      )







    @slow
    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Any														=						XGLMTokenizer.from_pretrained('facebook/xglm-564M'      )
         __lowerCamelCase  :      Tuple														=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'      )

         tf.random.set_seed(0      )
         __lowerCamelCase  :      List[Any]														=						tokenizer('Today is a nice day and'     ,	return_tensors='tf'      )
         __lowerCamelCase  :      int														=						tokenized.input_ids
         # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
         with tf.device(':/CPU:0'      ):
              __lowerCamelCase  :      Tuple														=						model.generate(SCREAMING_SNAKE_CASE_     ,	do_sample=SCREAMING_SNAKE_CASE_     ,	seed=[7, 0]      )
         __lowerCamelCase  :      Optional[Any]														=						tokenizer.decode(output_ids[0]     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Union[str, Any]														=						(
             'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
         )
         self.assertEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    @slow
    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Tuple														=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'      )
         __lowerCamelCase  :      Any														=						XGLMTokenizer.from_pretrained('facebook/xglm-564M'      )

         __lowerCamelCase  :      Any														=						'left'

         # use different length sentences to test batching
         __lowerCamelCase  :      Any														=						[
             'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
             'left-padding, such as in batched generation. The output for the sequence below should be the same '
             'regardless of whether left padding is applied or not. When',
             'Hello, my dog is a little',
         ]

         __lowerCamelCase  :      Any														=						tokenizer(SCREAMING_SNAKE_CASE_     ,	return_tensors='tf'     ,	padding=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Optional[int]														=						inputs['input_ids']

         __lowerCamelCase  :      str														=						model.generate(input_ids=SCREAMING_SNAKE_CASE_     ,	attention_mask=inputs['attention_mask']     ,	max_new_tokens=12      )

         __lowerCamelCase  :      Optional[int]														=						tokenizer(sentences[0]     ,	return_tensors='tf'      ).input_ids
         __lowerCamelCase  :      int														=						model.generate(input_ids=SCREAMING_SNAKE_CASE_     ,	max_new_tokens=12      )

         __lowerCamelCase  :      Optional[Any]														=						tokenizer(sentences[1]     ,	return_tensors='tf'      ).input_ids
         __lowerCamelCase  :      Optional[Any]														=						model.generate(input_ids=SCREAMING_SNAKE_CASE_     ,	max_new_tokens=12      )

         __lowerCamelCase  :      Union[str, Any]														=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						tokenizer.decode(output_non_padded[0]     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						tokenizer.decode(output_padded[0]     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Optional[int]														=						[
             'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
             'left-padding, such as in batched generation. The output for the sequence below should be the same '
             'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
             'a single',
             'Hello, my dog is a little bit of a shy one, but he is very friendly',
         ]
         self.assertListEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         self.assertListEqual(SCREAMING_SNAKE_CASE_     ,	[non_padded_sentence, padded_sentence]      )






