from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase	: int    		=  logging.get_logger(__name__)

__UpperCamelCase	: int    		=  {
    'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""cvt"""


						def __init__( self			:       Optional[int]					,	UpperCamelCase__			:       int=3					,	UpperCamelCase__			:       Optional[int]=[7, 3, 3]					,	UpperCamelCase__			:       Dict=[4, 2, 2]					,	UpperCamelCase__			:       str=[2, 1, 1]					,	UpperCamelCase__			:       Optional[Any]=[64, 192, 384]					,	UpperCamelCase__			:       Union[str, Any]=[1, 3, 6]					,	UpperCamelCase__			:       Optional[Any]=[1, 2, 10]					,	UpperCamelCase__			:       Any=[4.0, 4.0, 4.0]					,	UpperCamelCase__			:       Dict=[0.0, 0.0, 0.0]					,	UpperCamelCase__			:       int=[0.0, 0.0, 0.0]					,	UpperCamelCase__			:       Dict=[0.0, 0.0, 0.1]					,	UpperCamelCase__			:       Any=[True, True, True]					,	UpperCamelCase__			:       Union[str, Any]=[False, False, True]					,	UpperCamelCase__			:       Optional[int]=["dw_bn", "dw_bn", "dw_bn"]					,	UpperCamelCase__			:       List[Any]=[3, 3, 3]					,	UpperCamelCase__			:       List[str]=[1, 1, 1]					,	UpperCamelCase__			:       Optional[Any]=[2, 2, 2]					,	UpperCamelCase__			:       List[Any]=[1, 1, 1]					,	UpperCamelCase__			:       Any=[1, 1, 1]					,	UpperCamelCase__			:       Any=0.02					,	UpperCamelCase__			:       Optional[int]=1E-12					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					int           =							num_channels
												SCREAMING_SNAKE_CASE							:					Tuple           =							patch_sizes
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							patch_stride
												SCREAMING_SNAKE_CASE							:					Tuple           =							patch_padding
												SCREAMING_SNAKE_CASE							:					str           =							embed_dim
												SCREAMING_SNAKE_CASE							:					List[Any]           =							num_heads
												SCREAMING_SNAKE_CASE							:					str           =							depth
												SCREAMING_SNAKE_CASE							:					List[Any]           =							mlp_ratio
												SCREAMING_SNAKE_CASE							:					Any           =							attention_drop_rate
												SCREAMING_SNAKE_CASE							:					List[Any]           =							drop_rate
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							drop_path_rate
												SCREAMING_SNAKE_CASE							:					Tuple           =							qkv_bias
												SCREAMING_SNAKE_CASE							:					Dict           =							cls_token
												SCREAMING_SNAKE_CASE							:					List[str]           =							qkv_projection_method
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							kernel_qkv
												SCREAMING_SNAKE_CASE							:					str           =							padding_kv
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							stride_kv
												SCREAMING_SNAKE_CASE							:					str           =							padding_q
												SCREAMING_SNAKE_CASE							:					List[Any]           =							stride_q
												SCREAMING_SNAKE_CASE							:					str           =							initializer_range
												SCREAMING_SNAKE_CASE							:					Any           =							layer_norm_eps






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.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ])
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Any		):





												'''simple docstring'''





												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  __A ( self			:       str					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							f"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
												# distributed data settings
												SCREAMING_SNAKE_CASE							:					Any           =							{'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None

												# 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=UpperCamelCase__					,	instance_count=UpperCamelCase__					,	instance_type=self.instance_type					,	debugger_hook_config=UpperCamelCase__					,	hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}					,	metric_definitions=self.env.metric_definitions					,	distribution=UpperCamelCase__					,	py_version='''py36'''					,	)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												TrainingJobAnalytics(UpperCamelCase__		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)



						@parameterized.expand([(2,)]		)
						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.create_estimator(UpperCamelCase__		)

												# run training
												estimator.fit()

												# result dataframe
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

												# extract kpis
												SCREAMING_SNAKE_CASE							:					List[str]           =							list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']		)
												SCREAMING_SNAKE_CASE							:					Dict           =							list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']		)
												# get train time from SageMaker job, this includes starting, preprocessing, stopping
												SCREAMING_SNAKE_CASE							:					List[Any]           =							(
												    Session().describe_training_job(estimator.latest_training_job.name		).get('''TrainingTimeInSeconds'''					,	99_9999		)
												)

												# 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__		)





