






"""simple docstring"""




import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class    lowercase_    (_UpperCAmelCase,    _UpperCAmelCase     ):






      @register_to_config
      def __init__(    self     ,     *,
          a_ = 4     ,     a_ = 7_6_8     ,     a_     ,     a_     ,     )	->Optional[int]:







        '''simple docstring'''


        super().__init__()

        _a							=	nn.Parameter(torch.zeros(a_	)	)

        # parameters for additional clip time embeddings
        _a							=	nn.Linear(a_     ,     a_	)
        _a							=	nn.Linear(a_     ,     a_	)

        # parameters for encoder hidden states
        _a							=	clip_extra_context_tokens
        _a							=	nn.Linear(
            a_     ,     self.clip_extra_context_tokens * cross_attention_dim	)
        _a							=	nn.Linear(a_     ,     a_	)
        _a							=	nn.LayerNorm(a_	)






      def 			lowerCamelCase__    (    self     ,     *, a_     ,     a_     ,     a_     ,     a_	)	->List[str]:







        '''simple docstring'''


        if do_classifier_free_guidance:
          # Add the classifier free guidance embeddings to the image embeddings
          _a							=	image_embeddings.shape[0]
          _a							=	self.learned_classifier_free_guidance_embeddings.unsqueeze(0	)
          _a							=	classifier_free_guidance_embeddings.expand(
              a_     ,     -1	)
          _a							=	torch.cat([classifier_free_guidance_embeddings, image_embeddings]     ,     dim=0	)

        # The image embeddings batch size and the text embeddings batch size are equal
        assert image_embeddings.shape[0] == prompt_embeds.shape[0]

        _a							=	prompt_embeds.shape[0]

        # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
        # adding CLIP embeddings to the existing timestep embedding, ...
        _a							=	self.embedding_proj(a_	)
        _a							=	self.clip_image_embeddings_project_to_time_embeddings(a_	)
        _a							=	time_projected_image_embeddings + time_projected_prompt_embeds

        # ... and by projecting CLIP embeddings into four
        # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
        _a							=	self.clip_extra_context_tokens_proj(a_	)
        _a							=	clip_extra_context_tokens.reshape(a_     ,     -1     ,     self.clip_extra_context_tokens	)
        _a							=	clip_extra_context_tokens.permute(0     ,     2     ,     1	)

        _a							=	self.encoder_hidden_states_proj(a_	)
        _a							=	self.text_encoder_hidden_states_norm(a_	)
        _a							=	torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,     dim=1	)

        return text_encoder_hidden_states, additive_clip_time_embeddings










"""simple docstring"""




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''': 650, '''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''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]     )
class    lowercase_    (unittest.TestCase     ):






      def 			lowerCamelCase__    (    self	)	->Union[str, 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=a_     ,     )
        assert hasattr(self     ,     "env"	)






      def 			lowerCamelCase__    (    self     ,     a_=1	)	->int:







        '''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 			lowerCamelCase__    (    self     ,     a_	)	->str:







        '''simple docstring'''


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






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        _a							=	self.create_estimator()

        # run training
        estimator.fit()

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

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


