



"""simple docstring"""





from typing import TYPE_CHECKING

from ..utils import _LazyModule


lowercase  							=					{
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
     from .config import (
         EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
         OnnxConfig,
         OnnxConfigWithPast,
         OnnxSeqaSeqConfigWithPast,
         PatchingSpec,
     )
     from .convert import export, validate_model_outputs
     from .features import FeaturesManager
     from .utils import ParameterFormat, compute_serialized_parameters_size

else:
     import sys

     lowercase  							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







"""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 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		{
									    '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},
									    }
									}
									_UpperCAmelCase							 =		{
									    '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        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									self.assertTrue(np.allclose(transpose(snake_case						)	,       x.transpose()						)						)

									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case						)	,       transpose(snake_case						).numpy()						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case						)	,       transpose(snake_case						).numpy()						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case						)	,       np.asarray(transpose(snake_case						)						)						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (4, 3)						)	,       np.reshape(snake_case	,       (4, 3)						)						)						)

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






							@require_torch
							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (4, 3)						)	,       reshape(snake_case	,       (4, 3)						).numpy()						)						)

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






							@require_tf
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (4, 3)						)	,       reshape(snake_case	,       (4, 3)						).numpy()						)						)

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






							@require_flax
							def        lowerCamelCase_   (  self						)			->							Tuple:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (4, 3)						)	,       np.asarray(reshape(snake_case	,       (4, 3)						)						)						)						)

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






							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		np.random.randn(1	,       3	,       4						)
									self.assertTrue(np.allclose(squeeze(snake_case						)	,       np.squeeze(snake_case						)						)						)

									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		np.random.randn(1	,       3	,       4						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case						)	,       squeeze(snake_case						).numpy()						)						)

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






							@require_tf
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									_UpperCAmelCase							 =		np.random.randn(1	,       3	,       4						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case						)	,       squeeze(snake_case						).numpy()						)						)

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






							@require_flax
							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		np.random.randn(1	,       3	,       4						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case						)	,       np.asarray(squeeze(snake_case						)						)						)						)

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






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(expand_dims(snake_case	,       axis=1						)	,       expand_dims(snake_case	,       axis=1						).numpy()						)						)






							@require_tf
							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(expand_dims(snake_case	,       axis=1						)	,       expand_dims(snake_case	,       axis=1						).numpy()						)						)






							@require_flax
							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(expand_dims(snake_case	,       axis=1						)	,       np.asarray(expand_dims(snake_case	,       axis=1						)						)						)						)


