



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 UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				{
						    'task_specific_params': {
						        'summarization': {'length_penalty': 1.0, 'max_length': 1_2_8, 'min_length': 1_2, 'num_beams': 4},
						        'summarization_cnn': {'length_penalty': 2.0, 'max_length': 1_4_2, 'min_length': 5_6, 'num_beams': 4},
						        'summarization_xsum': {'length_penalty': 1.0, 'max_length': 6_2, 'min_length': 1_1, 'num_beams': 6},
						    }
						}
						A_		:    Any         =				{
						    'task_specific_params.summarization.length_penalty': 1.0,
						    'task_specific_params.summarization.max_length': 1_2_8,
						    'task_specific_params.summarization.min_length': 1_2,
						    'task_specific_params.summarization.num_beams': 4,
						    'task_specific_params.summarization_cnn.length_penalty': 2.0,
						    'task_specific_params.summarization_cnn.max_length': 1_4_2,
						    'task_specific_params.summarization_cnn.min_length': 5_6,
						    'task_specific_params.summarization_cnn.num_beams': 4,
						    'task_specific_params.summarization_xsum.length_penalty': 1.0,
						    'task_specific_params.summarization_xsum.max_length': 6_2,
						    'task_specific_params.summarization_xsum.min_length': 1_1,
						    'task_specific_params.summarization_xsum.num_beams': 6,
						}

						self.assertEqual(flatten_dict(a__ )		,     a__ )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				np.random.randn(3		,     4 )
						self.assertTrue(np.allclose(transpose(a__ )		,     x.transpose() ) )

						A_		:    str         =				np.random.randn(3		,     4		,     5 )
						self.assertTrue(np.allclose(transpose(a__		,     axes=(1, 2, 0) )		,     x.transpose((1, 2, 0) ) ) )
				@require_torch
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				np.random.randn(3		,     4 )
						A_		:    Optional[Any]         =				torch.tensor(a__ )
						self.assertTrue(np.allclose(transpose(a__ )		,     transpose(a__ ).numpy() ) )

						A_		:    Optional[int]         =				np.random.randn(3		,     4		,     5 )
						A_		:    Any         =				torch.tensor(a__ )
						self.assertTrue(np.allclose(transpose(a__		,     axes=(1, 2, 0) )		,     transpose(a__		,     axes=(1, 2, 0) ).numpy() ) )
				@require_tf
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				np.random.randn(3		,     4 )
						A_		:    Any         =				tf.constant(a__ )
						self.assertTrue(np.allclose(transpose(a__ )		,     transpose(a__ ).numpy() ) )

						A_		:    Union[str, Any]         =				np.random.randn(3		,     4		,     5 )
						A_		:    Any         =				tf.constant(a__ )
						self.assertTrue(np.allclose(transpose(a__		,     axes=(1, 2, 0) )		,     transpose(a__		,     axes=(1, 2, 0) ).numpy() ) )
				@require_flax
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				np.random.randn(3		,     4 )
						A_		:    str         =				jnp.array(a__ )
						self.assertTrue(np.allclose(transpose(a__ )		,     np.asarray(transpose(a__ ) ) ) )

						A_		:    Tuple         =				np.random.randn(3		,     4		,     5 )
						A_		:    Optional[int]         =				jnp.array(a__ )
						self.assertTrue(np.allclose(transpose(a__		,     axes=(1, 2, 0) )		,     np.asarray(transpose(a__		,     axes=(1, 2, 0) ) ) ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				np.random.randn(3		,     4 )
						self.assertTrue(np.allclose(reshape(a__		,     (4, 3) )		,     np.reshape(a__		,     (4, 3) ) ) )

						A_		:    int         =				np.random.randn(3		,     4		,     5 )
						self.assertTrue(np.allclose(reshape(a__		,     (1_2, 5) )		,     np.reshape(a__		,     (1_2, 5) ) ) )
				@require_torch
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				np.random.randn(3		,     4 )
						A_		:    str         =				torch.tensor(a__ )
						self.assertTrue(np.allclose(reshape(a__		,     (4, 3) )		,     reshape(a__		,     (4, 3) ).numpy() ) )

						A_		:    Any         =				np.random.randn(3		,     4		,     5 )
						A_		:    Union[str, Any]         =				torch.tensor(a__ )
						self.assertTrue(np.allclose(reshape(a__		,     (1_2, 5) )		,     reshape(a__		,     (1_2, 5) ).numpy() ) )
				@require_tf
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    str         =				np.random.randn(3		,     4 )
						A_		:    List[Any]         =				tf.constant(a__ )
						self.assertTrue(np.allclose(reshape(a__		,     (4, 3) )		,     reshape(a__		,     (4, 3) ).numpy() ) )

						A_		:    Any         =				np.random.randn(3		,     4		,     5 )
						A_		:    Optional[int]         =				tf.constant(a__ )
						self.assertTrue(np.allclose(reshape(a__		,     (1_2, 5) )		,     reshape(a__		,     (1_2, 5) ).numpy() ) )
				@require_flax
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				np.random.randn(3		,     4 )
						A_		:    Any         =				jnp.array(a__ )
						self.assertTrue(np.allclose(reshape(a__		,     (4, 3) )		,     np.asarray(reshape(a__		,     (4, 3) ) ) ) )

						A_		:    Tuple         =				np.random.randn(3		,     4		,     5 )
						A_		:    Optional[int]         =				jnp.array(a__ )
						self.assertTrue(np.allclose(reshape(a__		,     (1_2, 5) )		,     np.asarray(reshape(a__		,     (1_2, 5) ) ) ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				np.random.randn(1		,     3		,     4 )
						self.assertTrue(np.allclose(squeeze(a__ )		,     np.squeeze(a__ ) ) )

						A_		:    Dict         =				np.random.randn(1		,     4		,     1		,     5 )
						self.assertTrue(np.allclose(squeeze(a__		,     axis=2 )		,     np.squeeze(a__		,     axis=2 ) ) )
				@require_torch
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    int         =				np.random.randn(1		,     3		,     4 )
						A_		:    int         =				torch.tensor(a__ )
						self.assertTrue(np.allclose(squeeze(a__ )		,     squeeze(a__ ).numpy() ) )

						A_		:    Dict         =				np.random.randn(1		,     4		,     1		,     5 )
						A_		:    Dict         =				torch.tensor(a__ )
						self.assertTrue(np.allclose(squeeze(a__		,     axis=2 )		,     squeeze(a__		,     axis=2 ).numpy() ) )
				@require_tf
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				np.random.randn(1		,     3		,     4 )
						A_		:    str         =				tf.constant(a__ )
						self.assertTrue(np.allclose(squeeze(a__ )		,     squeeze(a__ ).numpy() ) )

						A_		:    Union[str, Any]         =				np.random.randn(1		,     4		,     1		,     5 )
						A_		:    Dict         =				tf.constant(a__ )
						self.assertTrue(np.allclose(squeeze(a__		,     axis=2 )		,     squeeze(a__		,     axis=2 ).numpy() ) )
				@require_flax
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				np.random.randn(1		,     3		,     4 )
						A_		:    str         =				jnp.array(a__ )
						self.assertTrue(np.allclose(squeeze(a__ )		,     np.asarray(squeeze(a__ ) ) ) )

						A_		:    Any         =				np.random.randn(1		,     4		,     1		,     5 )
						A_		:    Optional[Any]         =				jnp.array(a__ )
						self.assertTrue(np.allclose(squeeze(a__		,     axis=2 )		,     np.asarray(squeeze(a__		,     axis=2 ) ) ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				np.random.randn(3		,     4 )
						self.assertTrue(np.allclose(expand_dims(a__		,     axis=1 )		,     np.expand_dims(a__		,     axis=1 ) ) )
				@require_torch
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    int         =				np.random.randn(3		,     4 )
						A_		:    Optional[int]         =				torch.tensor(a__ )
						self.assertTrue(np.allclose(expand_dims(a__		,     axis=1 )		,     expand_dims(a__		,     axis=1 ).numpy() ) )
				@require_tf
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    str         =				np.random.randn(3		,     4 )
						A_		:    Optional[int]         =				tf.constant(a__ )
						self.assertTrue(np.allclose(expand_dims(a__		,     axis=1 )		,     expand_dims(a__		,     axis=1 ).numpy() ) )







				@require_flax
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				np.random.randn(3		,     4 )
						A_		:    Optional[int]         =				jnp.array(a__ )
						self.assertTrue(np.allclose(expand_dims(a__		,     axis=1 )		,     np.asarray(expand_dims(a__		,     axis=1 ) ) ) )







from ..utils import DummyObject, requires_backends





class UpperCAmelCase						( metaclass=__A  ):


				'''simple docstring'''






				lowerCamelCase_    = ['''onnx''']
				def __init__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(self		,     ['onnx'] )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )







				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )






