
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': 6_50, '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': 6_00, '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': 6_00, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ]						)
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					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=lowercase						,  )
					assert hasattr(self						,  "env" )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Optional[Any]:
					lowerCamelCase_						=			f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
					# distributed data settings
					lowerCamelCase_						=			{"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=lowercase						,  instance_count=lowercase						,  instance_type=self.instance_type						,  debugger_hook_config=lowercase						,  hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}						,  metric_definitions=self.env.metric_definitions						,  distribution=lowercase						,  py_version="py36"						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       int:
					TrainingJobAnalytics(lowercase ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv' )



	@parameterized.expand([(2,)] )
	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       int:
					# create estimator
					lowerCamelCase_						=			self.create_estimator(lowercase )

					# run training
					estimator.fit()

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

					# extract kpis
					lowerCamelCase_						=			list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"] )
					lowerCamelCase_						=			list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"] )
					# get train time from SageMaker job, this includes starting, preprocessing, stopping
					lowerCamelCase_						=			(
					    Session().describe_training_job(estimator.latest_training_job.name ).get("TrainingTimeInSeconds"						,  999999 )
					)

					# 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}						,  lowercase )






import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		str		=[]
             _lowerCamelCase       :		Dict		=[]

             for i in range(self.num_layers ):
                    _lowerCamelCase       :		Union[str, Any]		=self.in_channels if i == 0 else self.out_channels

                    _lowerCamelCase       :		Any		=FlaxResnetBlockaD(
                        in_channels=lowercase_			,   out_channels=self.out_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

                    _lowerCamelCase       :		Dict		=FlaxTransformeraDModel(
                        in_channels=self.out_channels			,   n_heads=self.num_attention_heads			,   d_head=self.out_channels // self.num_attention_heads			,   depth=1			,   use_linear_projection=self.use_linear_projection			,   only_cross_attention=self.only_cross_attention			,   use_memory_efficient_attention=self.use_memory_efficient_attention			,   dtype=self.dtype			,   )
                    attentions.append(lowercase_ )

             _lowerCamelCase       :		Optional[int]		=resnets
             _lowerCamelCase       :		Dict		=attentions

             if self.add_downsample:
                    _lowerCamelCase       :		Union[str, Any]		=FlaxDownsampleaD(self.out_channels			,   dtype=self.dtype )






      def __call__(					self						:     Any			,   lowercase_						:     Optional[Any]			,   lowercase_						:     List[Any]			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     List[str]=True )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=()

             for resnet, attn in zip(self.resnets			,   self.attentions ):
                    _lowerCamelCase       :		Union[str, Any]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    _lowerCamelCase       :		Union[str, Any]		=attn(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    output_states += (hidden_states,)

             if self.add_downsample:
                    _lowerCamelCase       :		Optional[int]		=self.downsamplers_a(lowercase_ )
                    output_states += (hidden_states,)

             return hidden_states, output_states




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     Optional[int] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		str		=[]

             for i in range(self.num_layers ):
                    _lowerCamelCase       :		Tuple		=self.in_channels if i == 0 else self.out_channels

                    _lowerCamelCase       :		Any		=FlaxResnetBlockaD(
                        in_channels=lowercase_			,   out_channels=self.out_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )
             _lowerCamelCase       :		Union[str, Any]		=resnets

             if self.add_downsample:
                    _lowerCamelCase       :		List[str]		=FlaxDownsampleaD(self.out_channels			,   dtype=self.dtype )






      def __call__(					self						:     Any			,   lowercase_						:     Dict			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Optional[Any]=True )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=()

             for resnet in self.resnets:
                    _lowerCamelCase       :		Tuple		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    output_states += (hidden_states,)

             if self.add_downsample:
                    _lowerCamelCase       :		Tuple		=self.downsamplers_a(lowercase_ )
                    output_states += (hidden_states,)

             return hidden_states, output_states




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     Dict )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		str		=[]
             _lowerCamelCase       :		List[str]		=[]

             for i in range(self.num_layers ):
                    _lowerCamelCase       :		List[str]		=self.in_channels if (i == self.num_layers - 1) else self.out_channels
                    _lowerCamelCase       :		Tuple		=self.prev_output_channel if i == 0 else self.out_channels

                    _lowerCamelCase       :		List[str]		=FlaxResnetBlockaD(
                        in_channels=resnet_in_channels + res_skip_channels			,   out_channels=self.out_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

                    _lowerCamelCase       :		Tuple		=FlaxTransformeraDModel(
                        in_channels=self.out_channels			,   n_heads=self.num_attention_heads			,   d_head=self.out_channels // self.num_attention_heads			,   depth=1			,   use_linear_projection=self.use_linear_projection			,   only_cross_attention=self.only_cross_attention			,   use_memory_efficient_attention=self.use_memory_efficient_attention			,   dtype=self.dtype			,   )
                    attentions.append(lowercase_ )

             _lowerCamelCase       :		int		=resnets
             _lowerCamelCase       :		Dict		=attentions

             if self.add_upsample:
                    _lowerCamelCase       :		str		=FlaxUpsampleaD(self.out_channels			,   dtype=self.dtype )






      def __call__(					self						:     Optional[Any]			,   lowercase_						:     Any			,   lowercase_						:     Any			,   lowercase_						:     List[Any]			,   lowercase_						:     Dict			,   lowercase_						:     Union[str, Any]=True )					->						Optional[int]:
             """simple docstring"""




             for resnet, attn in zip(self.resnets			,   self.attentions ):
                    # pop res hidden states
                    _lowerCamelCase       :		Optional[int]		=res_hidden_states_tuple[-1]
                    _lowerCamelCase       :		Union[str, Any]		=res_hidden_states_tuple[:-1]
                    _lowerCamelCase       :		Any		=jnp.concatenate((hidden_states, res_hidden_states)			,   axis=-1 )

                    _lowerCamelCase       :		Optional[Any]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    _lowerCamelCase       :		List[Any]		=attn(lowercase_			,   lowercase_			,   deterministic=lowercase_ )

             if self.add_upsample:
                    _lowerCamelCase       :		Optional[Any]		=self.upsamplers_a(lowercase_ )

             return hidden_states




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=[]

             for i in range(self.num_layers ):
                    _lowerCamelCase       :		Tuple		=self.in_channels if (i == self.num_layers - 1) else self.out_channels
                    _lowerCamelCase       :		int		=self.prev_output_channel if i == 0 else self.out_channels

                    _lowerCamelCase       :		str		=FlaxResnetBlockaD(
                        in_channels=resnet_in_channels + res_skip_channels			,   out_channels=self.out_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

             _lowerCamelCase       :		str		=resnets

             if self.add_upsample:
                    _lowerCamelCase       :		List[str]		=FlaxUpsampleaD(self.out_channels			,   dtype=self.dtype )






      def __call__(					self						:     List[str]			,   lowercase_						:     str			,   lowercase_						:     Dict			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Any=True )					->						int:
             """simple docstring"""




             for resnet in self.resnets:
                    # pop res hidden states
                    _lowerCamelCase       :		List[str]		=res_hidden_states_tuple[-1]
                    _lowerCamelCase       :		str		=res_hidden_states_tuple[:-1]
                    _lowerCamelCase       :		List[str]		=jnp.concatenate((hidden_states, res_hidden_states)			,   axis=-1 )

                    _lowerCamelCase       :		Optional[Any]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )

             if self.add_upsample:
                    _lowerCamelCase       :		Union[str, Any]		=self.upsamplers_a(lowercase_ )

             return hidden_states




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     int )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=[
                 FlaxResnetBlockaD(
                     in_channels=self.in_channels			,   out_channels=self.in_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
             ]

             _lowerCamelCase       :		Any		=[]

             for _ in range(self.num_layers ):
                    _lowerCamelCase       :		Optional[int]		=FlaxTransformeraDModel(
                        in_channels=self.in_channels			,   n_heads=self.num_attention_heads			,   d_head=self.in_channels // self.num_attention_heads			,   depth=1			,   use_linear_projection=self.use_linear_projection			,   use_memory_efficient_attention=self.use_memory_efficient_attention			,   dtype=self.dtype			,   )
                    attentions.append(lowercase_ )

                    _lowerCamelCase       :		Tuple		=FlaxResnetBlockaD(
                        in_channels=self.in_channels			,   out_channels=self.in_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

             _lowerCamelCase       :		List[Any]		=resnets
             _lowerCamelCase       :		List[str]		=attentions






      def __call__(					self						:     Optional[int]			,   lowercase_						:     List[Any]			,   lowercase_						:     Dict			,   lowercase_						:     Tuple			,   lowercase_						:     List[str]=True )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.resnets[0](lowercase_			,   lowercase_ )
             for attn, resnet in zip(self.attentions			,   self.resnets[1:] ):
                    _lowerCamelCase       :		Tuple		=attn(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    _lowerCamelCase       :		List[str]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )

             return hidden_states


