


'''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     __lowerCAmelCase		(unittest.TestCase	):





      '''simple docstring'''






      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					{
                 '''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},
                 }
             }
             lowercase__      =					{
                 '''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(snake_case_					)  ,		snake_case_					)



      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(3  ,		4					)
             self.assertTrue(np.allclose(transpose(snake_case_					)  ,		x.transpose()					)					)

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



      @require_torch
      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(3  ,		4					)
             lowercase__      =					torch.tensor(snake_case_					)
             self.assertTrue(np.allclose(transpose(snake_case_					)  ,		transpose(snake_case_					).numpy()					)					)

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



      @require_tf
      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(3  ,		4					)
             lowercase__      =					tf.constant(snake_case_					)
             self.assertTrue(np.allclose(transpose(snake_case_					)  ,		transpose(snake_case_					).numpy()					)					)

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



      @require_flax
      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(3  ,		4					)
             lowercase__      =					jnp.array(snake_case_					)
             self.assertTrue(np.allclose(transpose(snake_case_					)  ,		np.asarray(transpose(snake_case_					)					)					)					)

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



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(3  ,		4					)
             self.assertTrue(np.allclose(reshape(snake_case_  ,		(4, 3)					)  ,		np.reshape(snake_case_  ,		(4, 3)					)					)					)

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



      @require_torch
      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(3  ,		4					)
             lowercase__      =					torch.tensor(snake_case_					)
             self.assertTrue(np.allclose(reshape(snake_case_  ,		(4, 3)					)  ,		reshape(snake_case_  ,		(4, 3)					).numpy()					)					)

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



      @require_tf
      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(3  ,		4					)
             lowercase__      =					tf.constant(snake_case_					)
             self.assertTrue(np.allclose(reshape(snake_case_  ,		(4, 3)					)  ,		reshape(snake_case_  ,		(4, 3)					).numpy()					)					)

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



      @require_flax
      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(3  ,		4					)
             lowercase__      =					jnp.array(snake_case_					)
             self.assertTrue(np.allclose(reshape(snake_case_  ,		(4, 3)					)  ,		np.asarray(reshape(snake_case_  ,		(4, 3)					)					)					)					)

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



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(1  ,		3  ,		4					)
             self.assertTrue(np.allclose(squeeze(snake_case_					)  ,		np.squeeze(snake_case_					)					)					)

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



      @require_torch
      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(1  ,		3  ,		4					)
             lowercase__      =					torch.tensor(snake_case_					)
             self.assertTrue(np.allclose(squeeze(snake_case_					)  ,		squeeze(snake_case_					).numpy()					)					)

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



      @require_tf
      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(1  ,		3  ,		4					)
             lowercase__      =					tf.constant(snake_case_					)
             self.assertTrue(np.allclose(squeeze(snake_case_					)  ,		squeeze(snake_case_					).numpy()					)					)

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



      @require_flax
      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(1  ,		3  ,		4					)
             lowercase__      =					jnp.array(snake_case_					)
             self.assertTrue(np.allclose(squeeze(snake_case_					)  ,		np.asarray(squeeze(snake_case_					)					)					)					)

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



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(3  ,		4					)
             self.assertTrue(np.allclose(expand_dims(snake_case_  ,		axis=1					)  ,		np.expand_dims(snake_case_  ,		axis=1					)					)					)



      @require_torch
      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(3  ,		4					)
             lowercase__      =					torch.tensor(snake_case_					)
             self.assertTrue(np.allclose(expand_dims(snake_case_  ,		axis=1					)  ,		expand_dims(snake_case_  ,		axis=1					).numpy()					)					)



      @require_tf
      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(3  ,		4					)
             lowercase__      =					tf.constant(snake_case_					)
             self.assertTrue(np.allclose(expand_dims(snake_case_  ,		axis=1					)  ,		expand_dims(snake_case_  ,		axis=1					).numpy()					)					)






      @require_flax
      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					np.random.randn(3  ,		4					)
             lowercase__      =					jnp.array(snake_case_					)
             self.assertTrue(np.allclose(expand_dims(snake_case_  ,		axis=1					)  ,		np.asarray(expand_dims(snake_case_  ,		axis=1					)					)					)					)






"""simple docstring"""
import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
			from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm

def  lowercase     (_lowerCAmelCase						):
						if is_torch_version("""<""" ,    """2.0.0"""						) or not hasattr(_lowerCAmelCase ,    """_dynamo"""						):
												return False
						return isinstance(_lowerCAmelCase ,    torch._dynamo.eval_frame.OptimizedModule						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase = True						):
						__lowerCAmelCase     =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

						__lowerCAmelCase     =  is_compiled_module(_lowerCAmelCase						)
						if is_compiled:
												__lowerCAmelCase     =  model
												__lowerCAmelCase     =  model._orig_mod

						if is_deepspeed_available():
												options += (DeepSpeedEngine,)

						while isinstance(_lowerCAmelCase ,    _lowerCAmelCase						):
												__lowerCAmelCase     =  model.module

						if not keep_fpaa_wrapper:
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    """forward"""						)
												__lowerCAmelCase     =  model.__dict__.pop("""_original_forward""" ,    _lowerCAmelCase						)
												if original_forward is not None:
																		while hasattr(_lowerCAmelCase ,    """__wrapped__"""						):
																								__lowerCAmelCase     =  forward.__wrapped__
																								if forward == original_forward:
																														break
																		__lowerCAmelCase     =  forward
												if getattr(_lowerCAmelCase ,    """_converted_to_transformer_engine""" ,    _lowerCAmelCase						):
																		convert_model(_lowerCAmelCase ,    to_transformer_engine=_lowerCAmelCase						)

						if is_compiled:
												__lowerCAmelCase     =  model
												__lowerCAmelCase     =  compiled_model

						return model

def  lowercase     ():
						PartialState().wait_for_everyone()

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						if PartialState().distributed_type == DistributedType.TPU:
												xm.save(_lowerCAmelCase ,    _lowerCAmelCase						)
						elif PartialState().local_process_index == 0:
												torch.save(_lowerCAmelCase ,    _lowerCAmelCase						)

@contextmanager
def  lowercase     (**_lowerCAmelCase						):
						for key, value in kwargs.items():
												__lowerCAmelCase     =  str(_lowerCAmelCase						)

						yield

						for key in kwargs:
												if key.upper() in os.environ:
																		del os.environ[key.upper()]

def  lowercase     (_lowerCAmelCase						):
						if not hasattr(_lowerCAmelCase ,    """__qualname__"""						) and not hasattr(_lowerCAmelCase ,    """__name__"""						):
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    """__class__""" ,    _lowerCAmelCase						)
						if hasattr(_lowerCAmelCase ,    """__qualname__"""						):
												return obj.__qualname__
						if hasattr(_lowerCAmelCase ,    """__name__"""						):
												return obj.__name__
						return str(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						for key, value in source.items():
												if isinstance(_lowerCAmelCase ,    _lowerCAmelCase						):
																		__lowerCAmelCase     =  destination.setdefault(_lowerCAmelCase ,    {}						)
																		merge_dicts(_lowerCAmelCase ,    _lowerCAmelCase						)
												else:
																		__lowerCAmelCase     =  value

						return destination



def  lowercase     (_lowerCAmelCase = None						):
						if port is None:
												__lowerCAmelCase     =  2_9500
						with socket.socket(socket.AF_INET ,    socket.SOCK_STREAM						) as s:
												return s.connect_ex(("""localhost""", port)						) == 0
