
"""simple docstring"""







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
							import tensorflow as tf

							from transformers import TFResNetForImageClassification, TFResNetModel
							from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor




class  A__       :

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=3       ,		_SCREAMING_SNAKE_CASE=32       ,		_SCREAMING_SNAKE_CASE=3       ,		_SCREAMING_SNAKE_CASE=10       ,		_SCREAMING_SNAKE_CASE=[10, 20, 30, 40]       ,		_SCREAMING_SNAKE_CASE=[1, 1, 2, 1]       ,		_SCREAMING_SNAKE_CASE=True       ,		_SCREAMING_SNAKE_CASE=True       ,		_SCREAMING_SNAKE_CASE="relu"       ,		_SCREAMING_SNAKE_CASE=3       ,		_SCREAMING_SNAKE_CASE=None       ,		):
			__lowerCAmelCase			:						Optional[int]									=      parent
			__lowerCAmelCase			:						int									=      batch_size
			__lowerCAmelCase			:						Dict									=      image_size
			__lowerCAmelCase			:						Union[str, Any]									=      num_channels
			__lowerCAmelCase			:						List[Any]									=      embeddings_size
			__lowerCAmelCase			:						Tuple									=      hidden_sizes
			__lowerCAmelCase			:						Dict									=      depths
			__lowerCAmelCase			:						int									=      is_training
			__lowerCAmelCase			:						Any									=      use_labels
			__lowerCAmelCase			:						Union[str, Any]									=      hidden_act
			__lowerCAmelCase			:						Any									=      num_labels
			__lowerCAmelCase			:						int									=      scope
			__lowerCAmelCase			:						List[Any]									=      len(_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Tuple									=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

			__lowerCAmelCase			:						List[Any]									=      None
			if self.use_labels:
				__lowerCAmelCase			:						Optional[Any]									=      ids_tensor([self.batch_size]       ,		self.num_labels )

			__lowerCAmelCase			:						Optional[Any]									=      self.get_config()

			return config, pixel_values, labels

		def 							__lowerCamelCase							(      self ):
			return ResNetConfig(
			    num_channels=self.num_channels       ,		embeddings_size=self.embeddings_size       ,		hidden_sizes=self.hidden_sizes       ,		depths=self.depths       ,		hidden_act=self.hidden_act       ,		num_labels=self.num_labels       ,		image_size=self.image_size       ,		)

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Union[str, Any]									=      TFResNetModel(config=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						str									=      model(_SCREAMING_SNAKE_CASE )
			# expected last hidden states: B, C, H // 32, W // 32
			self.parent.assertEqual(
			    result.last_hidden_state.shape       ,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,		)

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Optional[Any]									=      self.num_labels
			__lowerCAmelCase			:						Tuple									=      TFResNetForImageClassification(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						List[Any]									=      model(_SCREAMING_SNAKE_CASE       ,		labels=_SCREAMING_SNAKE_CASE )
			self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels) )







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						str									=      self.prepare_config_and_inputs()
			__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						Dict									=      config_and_inputs
			__lowerCAmelCase			:						str									=      {'pixel_values': pixel_values}
			return config, inputs_dict




@require_tf
class  A__       (	_lowerCamelCase       ,     _lowerCamelCase       ,     unittest.TestCase):
		A_						:       List[str]				   =	(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
		A_						:       Dict				   =	(
		    {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
		    if is_tf_available()
		    else {}
		)

		A_						:       Any				   =	False
		A_						:       int				   =	False
		A_						:       Optional[int]				   =	False
		A_						:       int				   =	False
		A_						:       str				   =	False

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      TFResNetModelTester(self )
			__lowerCAmelCase			:						Union[str, Any]									=      ConfigTester(self       ,		config_class=_SCREAMING_SNAKE_CASE       ,		has_text_modality=_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			self.create_and_test_config_common_properties()
			self.config_tester.create_and_test_config_to_json_string()
			self.config_tester.create_and_test_config_to_json_file()
			self.config_tester.create_and_test_config_from_and_save_pretrained()
			self.config_tester.create_and_test_config_with_num_labels()
			self.config_tester.check_config_can_be_init_without_params()
			self.config_tester.check_config_arguments_init()

		def 							__lowerCamelCase							(      self ):
			return

		@unittest.skip(reason='ResNet does not use inputs_embeds' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip(reason='ResNet does not support input and output embeddings' )
		def 							__lowerCamelCase							(      self ):
			pass

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			,   __lowerCAmelCase			:						Optional[Any]									=      self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase			:						List[Any]									=      model_class(_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						int									=      inspect.signature(model.call )
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				__lowerCAmelCase			:						Dict									=      [*signature.parameters.keys()]

				__lowerCAmelCase			:						Optional[int]									=      ['pixel_values']
				self.assertListEqual(arg_names[:1]       ,		_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[Any]									=      self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			def check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						Tuple									=      model_class(_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						Tuple									=      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ) )

				__lowerCAmelCase			:						Optional[Any]									=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

				__lowerCAmelCase			:						Union[str, Any]									=      self.model_tester.num_stages
				self.assertEqual(len(_SCREAMING_SNAKE_CASE )       ,		expected_num_stages + 1 )

				# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
				self.assertListEqual(
				    list(hidden_states[0].shape[-2:] )       ,		[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,		)

			__lowerCAmelCase			,   __lowerCAmelCase			:						Dict									=      self.model_tester.prepare_config_and_inputs_for_common()
			__lowerCAmelCase			:						int									=      ['basic', 'bottleneck']
			for model_class in self.all_model_classes:
				for layer_type in layers_type:
					__lowerCAmelCase			:						Dict									=      layer_type
					__lowerCAmelCase			:						Dict									=      True
					check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					__lowerCAmelCase			:						int									=      True

					check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						str									=      self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE )







		@slow
		def 							__lowerCamelCase							(      self ):
			for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__lowerCAmelCase			:						Dict									=      TFResNetModel.from_pretrained(_SCREAMING_SNAKE_CASE )
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE )



def     __lowerCAmelCase				():
	__lowerCAmelCase			:						Optional[Any]									=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
	return image




@require_tf
@require_vision
class  A__       (	unittest.TestCase):

		@cached_property
		def 							__lowerCamelCase							(      self ):
			return (
			    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
			    if is_vision_available()
			    else None
			)







		@slow
		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						str									=      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

			__lowerCAmelCase			:						Tuple									=      self.default_image_processor
			__lowerCAmelCase			:						int									=      prepare_img()
			__lowerCAmelCase			:						List[str]									=      image_processor(images=_SCREAMING_SNAKE_CASE       ,		return_tensors='tf' )

			# forward pass
			__lowerCAmelCase			:						Any									=      model(**_SCREAMING_SNAKE_CASE )

			# verify the logits
			__lowerCAmelCase			:						Optional[Any]									=      tf.TensorShape((1, 10_00) )
			self.assertEqual(outputs.logits.shape       ,		_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Optional[int]									=      tf.constant([-11.1069, -9.7877, -8.3777] )

			self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()       ,		_SCREAMING_SNAKE_CASE       ,		atol=1E-4 ) )






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

import pytest
from parameterized import 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.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]			)
class 	A_       (unittest.TestCase			):
	def      _lowercase				(    self		):






				'''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=_A							,					)
				assert hasattr(self							,					'''env'''		)
	def      _lowercase				(    self							,					_A=1		):






				'''simple docstring'''







				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}-single"""							,					instance_count=_A							,					instance_type=self.instance_type							,					debugger_hook_config=_A							,					hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}							,					metric_definitions=self.env.metric_definitions							,					py_version='''py36'''							,					)
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				TrainingJobAnalytics(_A		).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.create_estimator()

				# run training
				estimator.fit()

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

				# extract kpis
				UpperCAmelCase   		=				list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']		)
				UpperCAmelCase   		=				list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']		)
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				UpperCAmelCase   		=				(
				    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}							,					_A		)



