


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      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						int:




											'''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=SCREAMING_SNAKE_CASE  ,     )
											assert hasattr(self  ,     "env"    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		f'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
											# distributed data settings
											__snake_case				=		{"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=SCREAMING_SNAKE_CASE  ,     instance_count=SCREAMING_SNAKE_CASE  ,     instance_type=self.instance_type  ,     debugger_hook_config=SCREAMING_SNAKE_CASE  ,     hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}  ,     metric_definitions=self.env.metric_definitions  ,     distribution=SCREAMING_SNAKE_CASE  ,     py_version="py36"  ,     )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]    )      ->						Union[str, Any]:




											'''simple docstring'''

											TrainingJobAnalytics(SCREAMING_SNAKE_CASE    ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''    )






					@parameterized.expand([(2,)]    )
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		Any    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.create_estimator(SCREAMING_SNAKE_CASE    )

											# run training
											estimator.fit()

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

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









import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()






class      UpperCamelCase(	_a  ,				unittest.TestCase ):
					snake_case_		:					Any    					=						PriorTransformer
					snake_case_		:					List[str]    					=						"""hidden_states"""


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						str:




											'''simple docstring'''

											__snake_case				=		4
											__snake_case				=		8
											__snake_case				=		7

											__snake_case				=		floats_tensor((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

											__snake_case				=		floats_tensor((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )
											__snake_case				=		floats_tensor((batch_size, num_embeddings, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

											return {
											    "hidden_states": hidden_states,
											    "timestep": 2,
											    "proj_embedding": proj_embedding,
											    "encoder_hidden_states": encoder_hidden_states,
											}


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=0    )      ->						Any:




											'''simple docstring'''

											torch.manual_seed(SCREAMING_SNAKE_CASE    )
											__snake_case				=		4
											__snake_case				=		8
											__snake_case				=		7

											__snake_case				=		torch.randn((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

											__snake_case				=		torch.randn((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )
											__snake_case				=		torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

											return {
											    "hidden_states": hidden_states,
											    "timestep": 2,
											    "proj_embedding": proj_embedding,
											    "encoder_hidden_states": encoder_hidden_states,
											}


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						str:




											'''simple docstring'''

											return (4, 8)


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Any:




											'''simple docstring'''

											return (4, 8)


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		{
											    "num_attention_heads": 2,
											    "attention_head_dim": 4,
											    "num_layers": 2,
											    "embedding_dim": 8,
											    "num_embeddings": 7,
											    "additional_embeddings": 4,
											}
											__snake_case				=		self.dummy_input
											return init_dict, inputs_dict


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Dict:




											'''simple docstring'''

											__snake_case		,       __snake_case				=		PriorTransformer.from_pretrained(
											    "hf-internal-testing/prior-dummy"  ,     output_loading_info=SCREAMING_SNAKE_CASE    )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE    )
											self.assertEqual(len(loading_info["missing_keys"]    )  ,     0    )

											model.to(SCREAMING_SNAKE_CASE    )
											__snake_case				=		model(**self.dummy_input    )[0]

											assert hidden_states is not None, "Make sure output is not None"


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case		,       __snake_case				=		self.prepare_init_args_and_inputs_for_common()

											__snake_case				=		self.model_class(**SCREAMING_SNAKE_CASE    )
											__snake_case				=		inspect.signature(model.forward    )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											__snake_case				=		[*signature.parameters.keys()]

											__snake_case				=		["hidden_states", "timestep"]
											self.assertListEqual(arg_names[:2]  ,     SCREAMING_SNAKE_CASE    )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"    )
											__snake_case				=		model.to(SCREAMING_SNAKE_CASE    )

											if hasattr(SCREAMING_SNAKE_CASE  ,     "set_default_attn_processor"    ):
																	model.set_default_attn_processor()

											__snake_case				=		self.get_dummy_seed_input()

											with torch.no_grad():
																	__snake_case				=		model(**SCREAMING_SNAKE_CASE    )[0]

											__snake_case				=		output[0, :5].flatten().cpu()
											print(SCREAMING_SNAKE_CASE    )

											# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
											# the expected output slices are not the same for CPU and GPU.
											__snake_case				=		torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]    )
											self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     rtol=1e-2    )    )






@slow
class      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		int=1  ,     SCREAMING_SNAKE_CASE						:		str=7_6_8  ,     SCREAMING_SNAKE_CASE						:		Dict=7_7  ,     SCREAMING_SNAKE_CASE						:		int=0    )      ->						List[str]:




											'''simple docstring'''

											torch.manual_seed(SCREAMING_SNAKE_CASE    )
											__snake_case				=		batch_size
											__snake_case				=		embedding_dim
											__snake_case				=		num_embeddings

											__snake_case				=		torch.randn((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

											__snake_case				=		torch.randn((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )
											__snake_case				=		torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

											return {
											    "hidden_states": hidden_states,
											    "timestep": 2,
											    "proj_embedding": proj_embedding,
											    "encoder_hidden_states": encoder_hidden_states,
											}


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Union[str, Any]:




											'''simple docstring'''

											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






					@parameterized.expand(
					    [
					        # fmt: off
					        [1_3, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
					        [3_7, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
					        # fmt: on
					    ]    )
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Tuple    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"  ,     subfolder="prior"    )
											model.to(SCREAMING_SNAKE_CASE    )
											__snake_case				=		self.get_dummy_seed_input(seed=SCREAMING_SNAKE_CASE    )

											with torch.no_grad():
																	__snake_case				=		model(**SCREAMING_SNAKE_CASE    )[0]

											assert list(sample.shape    ) == [1, 7_6_8]

											__snake_case				=		sample[0, :8].flatten().cpu()
											print(SCREAMING_SNAKE_CASE    )
											__snake_case				=		torch.tensor(SCREAMING_SNAKE_CASE    )

											assert torch_all_close(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     atol=1e-3    )





