





'''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      snake_case     :




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					=XGLMConfig
 SCREAMING_SNAKE_CASE_	:					Optional[Any]					={}
 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					="gelu"






 def __init__(       self       :							Optional[int]							,	__A       :							List[str]							,	__A       :							Optional[int]=1_4							,	__A       :							str=7							,	__A       :							Optional[Any]=True							,	__A       :							List[Any]=True							,	__A       :							int=True							,	__A       :							List[str]=9_9							,	__A       :							Union[str, Any]=3_2							,	__A       :							Union[str, Any]=2							,	__A       :							Union[str, Any]=4							,	__A       :							Tuple=3_7							,	__A       :							List[Any]="gelu"							,	__A       :							List[str]=0.1							,	__A       :							Optional[int]=0.1							,	__A       :							Tuple=5_1_2							,	__A       :							Optional[Any]=0.02							,	):
        __UpperCamelCase       =  parent
        __UpperCamelCase       =  batch_size
        __UpperCamelCase       =  seq_length
        __UpperCamelCase       =  is_training
        __UpperCamelCase       =  use_input_mask
        __UpperCamelCase       =  use_labels
        __UpperCamelCase       =  vocab_size
        __UpperCamelCase       =  d_model
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  ffn_dim
        __UpperCamelCase       =  activation_function
        __UpperCamelCase       =  activation_dropout
        __UpperCamelCase       =  attention_dropout
        __UpperCamelCase       =  max_position_embeddings
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  None
        __UpperCamelCase       =  0
        __UpperCamelCase       =  2
        __UpperCamelCase       =  1






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        return XGLMConfig.from_pretrained('facebook/xglm-564M'	)






 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  tf.clip_by_value(
            ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size	)							,	clip_value_min=0							,	clip_value_max=3	)

        __UpperCamelCase       =  None
        if self.use_input_mask:
               __UpperCamelCase       =  random_attention_mask([self.batch_size, self.seq_length]	)

        __UpperCamelCase       =  self.get_config()

        __UpperCamelCase       =  floats_tensor([self.num_hidden_layers, self.num_attention_heads]							,	2	)

        return (
            config,
            input_ids,
            input_mask,
            head_mask,
        )






 def        _lowerCamelCase					(       self       :							List[Any]	):
        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=UpperCAmelCase__							,	bos_token_id=self.bos_token_id							,	eos_token_id=self.eos_token_id							,	pad_token_id=self.pad_token_id							,	return_dict=UpperCAmelCase__							,	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  self.prepare_config_and_inputs()

        (
            (
            __UpperCamelCase
        )       ,						(
            __UpperCamelCase
        )       ,						(
            __UpperCamelCase
        )       ,						(
            __UpperCamelCase
        )       ,						
        )       =  config_and_inputs

        __UpperCamelCase       =  {
            'input_ids': input_ids,
            'head_mask': head_mask,
        }

        return config, inputs_dict






@require_tf
class      snake_case     (    __lowerCamelCase			,       __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



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






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  TFXGLMModelTester(self	)
        __UpperCamelCase       =  ConfigTester(self							,	config_class=UpperCAmelCase__							,	n_embd=3_7	)






 def        _lowerCamelCase					(       self       :							Any	):
        self.config_tester.run_common_tests()






 @slow
 def        _lowerCamelCase					(       self       :							List[str]	):
        for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCamelCase       =  TFXGLMModel.from_pretrained(UpperCAmelCase__	)
               self.assertIsNotNone(UpperCAmelCase__	)






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






@require_tf
class      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 @slow
 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							Optional[int]=True	):
        __UpperCamelCase       =  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'	)
        __UpperCamelCase       =  tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]							,	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
        __UpperCamelCase       =  [2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
        # fmt: on
        __UpperCamelCase       =  model.generate(UpperCAmelCase__							,	do_sample=UpperCAmelCase__							,	num_beams=1	)
        if verify_outputs:
               self.assertListEqual(output_ids[0].numpy().tolist()							,	UpperCAmelCase__	)






 @slow
 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  XGLMTokenizer.from_pretrained('facebook/xglm-564M'	)
        __UpperCamelCase       =  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'	)

        tf.random.set_seed(0	)
        __UpperCamelCase       =  tokenizer('Today is a nice day and'							,	return_tensors='tf'	)
        __UpperCamelCase       =  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'	):
               __UpperCamelCase       =  model.generate(UpperCAmelCase__							,	do_sample=UpperCAmelCase__							,	seed=[7, 0]	)
        __UpperCamelCase       =  tokenizer.decode(output_ids[0]							,	skip_special_tokens=UpperCAmelCase__	)

        __UpperCamelCase       =  (
            'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
        )
        self.assertEqual(UpperCAmelCase__							,	UpperCAmelCase__	)






 @slow
 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'	)
        __UpperCamelCase       =  XGLMTokenizer.from_pretrained('facebook/xglm-564M'	)

        __UpperCamelCase       =  'left'

        # use different length sentences to test batching
        __UpperCamelCase       =  [
            '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',
        ]

        __UpperCamelCase       =  tokenizer(UpperCAmelCase__							,	return_tensors='tf'							,	padding=UpperCAmelCase__	)
        __UpperCamelCase       =  inputs['input_ids']

        __UpperCamelCase       =  model.generate(input_ids=UpperCAmelCase__							,	attention_mask=inputs['attention_mask']							,	max_new_tokens=1_2	)

        __UpperCamelCase       =  tokenizer(sentences[0]							,	return_tensors='tf'	).input_ids
        __UpperCamelCase       =  model.generate(input_ids=UpperCAmelCase__							,	max_new_tokens=1_2	)

        __UpperCamelCase       =  tokenizer(sentences[1]							,	return_tensors='tf'	).input_ids
        __UpperCamelCase       =  model.generate(input_ids=UpperCAmelCase__							,	max_new_tokens=1_2	)

        __UpperCamelCase       =  tokenizer.batch_decode(UpperCAmelCase__							,	skip_special_tokens=UpperCAmelCase__	)
        __UpperCamelCase       =  tokenizer.decode(output_non_padded[0]							,	skip_special_tokens=UpperCAmelCase__	)
        __UpperCamelCase       =  tokenizer.decode(output_padded[0]							,	skip_special_tokens=UpperCAmelCase__	)

        __UpperCamelCase       =  [
            '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(UpperCAmelCase__							,	UpperCAmelCase__	)
        self.assertListEqual(UpperCAmelCase__							,	[non_padded_sentence, padded_sentence]	)



"""simple docstring"""





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
   from sagemaker import Session, TrainingJobAnalytics
   from sagemaker.huggingface import HuggingFace




@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"     ,"False"   )   ) is not True     ,reason="Skipping test because should only be run when releasing minor transformers version"     ,)
@pytest.mark.usefixtures("sm_env"   )
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]   )
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			Optional[int]	):
    if self.framework == "pytorch":
       subprocess.run(
           F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split(),					encoding="utf-8",					check=UpperCAmelCase__,					)
    assert hasattr(self,					"env"	)


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			List[Any]	):
    # configuration for running training on smdistributed Model Parallel
    __lowercase								=						{
        "enabled": True,
        "processes_per_host": 8,
    }
    __lowercase								=						{
        "enabled": True,
        "parameters": {
            "microbatches": 4,
            "placement_strategy": "spread",
            "pipeline": "interleaved",
            "optimize": "speed",
            "partitions": 4,
            "ddp": True,
        },
    }

    __lowercase								=						{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

    __lowercase								=						"trainer" if self.script == "run_glue.py" else "smtrainer"
    # creates estimator
    return HuggingFace(
        entry_point=self.script,					source_dir=self.env.test_path,					role=self.env.role,					image_uri=self.env.image_uri,					base_job_name=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""",					instance_count=UpperCAmelCase__,					instance_type=self.instance_type,					debugger_hook_config=UpperCAmelCase__,					hyperparameters={
            **self.env.hyperparameters,
            "model_name_or_path": self.model_name_or_path,
            "max_steps": 5_0_0,
        },					metric_definitions=self.env.metric_definitions,					distribution=UpperCAmelCase__,					py_version="py36",					)


 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			int	):
    TrainingJobAnalytics(UpperCAmelCase__	).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""	)



 @parameterized.expand([(1,)]	)
 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Union[str, Any]	):
    # create estimator
    __lowercase								=						self.create_estimator(UpperCAmelCase__	)

    # run training
    estimator.fit()

    # result dataframe
    __lowercase								=						TrainingJobAnalytics(estimator.latest_training_job.name	).dataframe()

    # extract kpis
    __lowercase								=						list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]	)
    __lowercase								=						list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]	)
    # get train time from SageMaker job, this includes starting, preprocessing, stopping
    __lowercase								=						(
        Session().describe_training_job(estimator.latest_training_job.name	).get("TrainingTimeInSeconds",					9_9_9_9_9_9	)
    )

    # assert kpis
    assert train_runtime <= self.results["train_runtime"]
    assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy	)
    assert all(t <= self.results["eval_loss"] for t in eval_loss	)

    # dump tests result into json file to share in PR
    with open(F"""{estimator.latest_training_job.name}.json""",					"w"	) as outfile:
       json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss},					UpperCAmelCase__	)




