





'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
      import jax.numpy as jnp

if is_tf_available():
      import tensorflow as tf

if is_torch_available():
      import torch


class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			{
            """task_specific_params""": {
                """summarization""": {"""length_penalty""": 1.0, """max_length""": 128, """min_length""": 12, """num_beams""": 4},
                """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 142, """min_length""": 56, """num_beams""": 4},
                """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
            }
        }
        _SCREAMING_SNAKE_CASE									=			{
            """task_specific_params.summarization.length_penalty""": 1.0,
            """task_specific_params.summarization.max_length""": 128,
            """task_specific_params.summarization.min_length""": 12,
            """task_specific_params.summarization.num_beams""": 4,
            """task_specific_params.summarization_cnn.length_penalty""": 2.0,
            """task_specific_params.summarization_cnn.max_length""": 142,
            """task_specific_params.summarization_cnn.min_length""": 56,
            """task_specific_params.summarization_cnn.num_beams""": 4,
            """task_specific_params.summarization_xsum.length_penalty""": 1.0,
            """task_specific_params.summarization_xsum.max_length""": 62,
            """task_specific_params.summarization_xsum.min_length""": 11,
            """task_specific_params.summarization_xsum.num_beams""": 6,
        }

        self.assertEqual(flatten_dict(A       ) ,		A       )


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        self.assertTrue(np.allclose(transpose(A       ) ,		x.transpose()       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        self.assertTrue(np.allclose(transpose(A ,		axes=(1, 2, 0)       ) ,		x.transpose((1, 2, 0)       )       )       )


    @require_torch
    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(transpose(A       ) ,		transpose(A       ).numpy()       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(transpose(A ,		axes=(1, 2, 0)       ) ,		transpose(A ,		axes=(1, 2, 0)       ).numpy()       )       )


    @require_tf
    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(transpose(A       ) ,		transpose(A       ).numpy()       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(transpose(A ,		axes=(1, 2, 0)       ) ,		transpose(A ,		axes=(1, 2, 0)       ).numpy()       )       )


    @require_flax
    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(transpose(A       ) ,		np.asarray(transpose(A       )       )       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(transpose(A ,		axes=(1, 2, 0)       ) ,		np.asarray(transpose(A ,		axes=(1, 2, 0)       )       )       )       )


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        self.assertTrue(np.allclose(reshape(A ,		(4, 3)       ) ,		np.reshape(A ,		(4, 3)       )       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        self.assertTrue(np.allclose(reshape(A ,		(12, 5)       ) ,		np.reshape(A ,		(12, 5)       )       )       )


    @require_torch
    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(reshape(A ,		(4, 3)       ) ,		reshape(A ,		(4, 3)       ).numpy()       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(reshape(A ,		(12, 5)       ) ,		reshape(A ,		(12, 5)       ).numpy()       )       )


    @require_tf
    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(reshape(A ,		(4, 3)       ) ,		reshape(A ,		(4, 3)       ).numpy()       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(reshape(A ,		(12, 5)       ) ,		reshape(A ,		(12, 5)       ).numpy()       )       )


    @require_flax
    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(reshape(A ,		(4, 3)       ) ,		np.asarray(reshape(A ,		(4, 3)       )       )       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(reshape(A ,		(12, 5)       ) ,		np.asarray(reshape(A ,		(12, 5)       )       )       )       )


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		3 ,		4       )
        self.assertTrue(np.allclose(squeeze(A       ) ,		np.squeeze(A       )       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		4 ,		1 ,		5       )
        self.assertTrue(np.allclose(squeeze(A ,		axis=2       ) ,		np.squeeze(A ,		axis=2       )       )       )


    @require_torch
    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(squeeze(A       ) ,		squeeze(A       ).numpy()       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		4 ,		1 ,		5       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(squeeze(A ,		axis=2       ) ,		squeeze(A ,		axis=2       ).numpy()       )       )


    @require_tf
    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(squeeze(A       ) ,		squeeze(A       ).numpy()       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		4 ,		1 ,		5       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(squeeze(A ,		axis=2       ) ,		squeeze(A ,		axis=2       ).numpy()       )       )


    @require_flax
    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(squeeze(A       ) ,		np.asarray(squeeze(A       )       )       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		4 ,		1 ,		5       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(squeeze(A ,		axis=2       ) ,		np.asarray(squeeze(A ,		axis=2       )       )       )       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        self.assertTrue(np.allclose(expand_dims(A ,		axis=1       ) ,		np.expand_dims(A ,		axis=1       )       )       )


    @require_torch
    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(expand_dims(A ,		axis=1       ) ,		expand_dims(A ,		axis=1       ).numpy()       )       )


    @require_tf
    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(expand_dims(A ,		axis=1       ) ,		expand_dims(A ,		axis=1       ).numpy()       )       )





    @require_flax
    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(expand_dims(A ,		axis=1       ) ,		np.asarray(expand_dims(A ,		axis=1       )       )       )       )









import os

import pytest
from attr import dataclass


_UpperCAmelCase		:  List[str]	=   "us-east-1"  # defaults region




@dataclass
class       __lowerCAmelCase				:
       _a           =  42
       _a           =  '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
       _a           =  {
           '''task_name''': '''mnli''',
           '''per_device_train_batch_size''': 16,
           '''per_device_eval_batch_size''': 16,
           '''do_train''': True,
           '''do_eval''': True,
           '''do_predict''': True,
           '''output_dir''': '''/opt/ml/model''',
           '''overwrite_output_dir''': True,
           '''max_steps''': 500,
           '''save_steps''': 5500,
       }
       _a           =  {**hyperparameters, '''max_steps''': 1000}






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         if self.framework == "pytorch":
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
               {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
           ]
         else:
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
               {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
           ]






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         return F"{self.framework}-transfromers-test"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return F"./tests/sagemaker/scripts/{self.framework}"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         if self.framework == "pytorch":
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
         else:
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"







@pytest.fixture(scope="class"      )
def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:Union[str, Any]							=  SageMakerTestEnvironment(framework=request.cls.framework      )



