


"""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 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				{
			    '''task_specific_params''': {
			        '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 1_28, '''min_length''': 12, '''num_beams''': 4},
			        '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 1_42, '''min_length''': 56, '''num_beams''': 4},
			        '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
			    }
			}
			__snake_case	:						Dict						=				{
			    '''task_specific_params.summarization.length_penalty''': 1.0,
			    '''task_specific_params.summarization.max_length''': 1_28,
			    '''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''': 1_42,
			    '''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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				np.random.randn(3				,       4					)
			self.assertTrue(np.allclose(transpose(a_					)				,       x.transpose()					)					)

			__snake_case	:						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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				np.random.randn(3				,       4					)
			__snake_case	:						str						=				torch.tensor(a_					)
			self.assertTrue(np.allclose(transpose(a_					)				,       transpose(a_					).numpy()					)					)

			__snake_case	:						List[Any]						=				np.random.randn(3				,       4				,       5					)
			__snake_case	:						List[Any]						=				torch.tensor(a_					)
			self.assertTrue(np.allclose(transpose(a_				,       axes=(1, 2, 0)					)				,       transpose(a_				,       axes=(1, 2, 0)					).numpy()					)					)



		@require_tf
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				np.random.randn(3				,       4					)
			__snake_case	:						Optional[Any]						=				tf.constant(a_					)
			self.assertTrue(np.allclose(transpose(a_					)				,       transpose(a_					).numpy()					)					)

			__snake_case	:						Optional[int]						=				np.random.randn(3				,       4				,       5					)
			__snake_case	:						Optional[int]						=				tf.constant(a_					)
			self.assertTrue(np.allclose(transpose(a_				,       axes=(1, 2, 0)					)				,       transpose(a_				,       axes=(1, 2, 0)					).numpy()					)					)



		@require_flax
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				np.random.randn(3				,       4					)
			__snake_case	:						Optional[Any]						=				jnp.array(a_					)
			self.assertTrue(np.allclose(transpose(a_					)				,       np.asarray(transpose(a_					)					)					)					)

			__snake_case	:						Any						=				np.random.randn(3				,       4				,       5					)
			__snake_case	:						Tuple						=				jnp.array(a_					)
			self.assertTrue(np.allclose(transpose(a_				,       axes=(1, 2, 0)					)				,       np.asarray(transpose(a_				,       axes=(1, 2, 0)					)					)					)					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						str						=				np.random.randn(3				,       4					)
			self.assertTrue(np.allclose(reshape(a_				,       (4, 3)					)				,       np.reshape(a_				,       (4, 3)					)					)					)

			__snake_case	:						Dict						=				np.random.randn(3				,       4				,       5					)
			self.assertTrue(np.allclose(reshape(a_				,       (12, 5)					)				,       np.reshape(a_				,       (12, 5)					)					)					)



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				np.random.randn(3				,       4					)
			__snake_case	:						int						=				torch.tensor(a_					)
			self.assertTrue(np.allclose(reshape(a_				,       (4, 3)					)				,       reshape(a_				,       (4, 3)					).numpy()					)					)

			__snake_case	:						Optional[int]						=				np.random.randn(3				,       4				,       5					)
			__snake_case	:						Optional[Any]						=				torch.tensor(a_					)
			self.assertTrue(np.allclose(reshape(a_				,       (12, 5)					)				,       reshape(a_				,       (12, 5)					).numpy()					)					)



		@require_tf
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				np.random.randn(3				,       4					)
			__snake_case	:						Dict						=				tf.constant(a_					)
			self.assertTrue(np.allclose(reshape(a_				,       (4, 3)					)				,       reshape(a_				,       (4, 3)					).numpy()					)					)

			__snake_case	:						Optional[Any]						=				np.random.randn(3				,       4				,       5					)
			__snake_case	:						List[Any]						=				tf.constant(a_					)
			self.assertTrue(np.allclose(reshape(a_				,       (12, 5)					)				,       reshape(a_				,       (12, 5)					).numpy()					)					)



		@require_flax
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				np.random.randn(3				,       4					)
			__snake_case	:						Any						=				jnp.array(a_					)
			self.assertTrue(np.allclose(reshape(a_				,       (4, 3)					)				,       np.asarray(reshape(a_				,       (4, 3)					)					)					)					)

			__snake_case	:						int						=				np.random.randn(3				,       4				,       5					)
			__snake_case	:						Dict						=				jnp.array(a_					)
			self.assertTrue(np.allclose(reshape(a_				,       (12, 5)					)				,       np.asarray(reshape(a_				,       (12, 5)					)					)					)					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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

			__snake_case	:						List[str]						=				np.random.randn(1				,       4				,       1				,       5					)
			self.assertTrue(np.allclose(squeeze(a_				,       axis=2					)				,       np.squeeze(a_				,       axis=2					)					)					)



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				np.random.randn(1				,       3				,       4					)
			__snake_case	:						Any						=				torch.tensor(a_					)
			self.assertTrue(np.allclose(squeeze(a_					)				,       squeeze(a_					).numpy()					)					)

			__snake_case	:						Optional[Any]						=				np.random.randn(1				,       4				,       1				,       5					)
			__snake_case	:						Optional[int]						=				torch.tensor(a_					)
			self.assertTrue(np.allclose(squeeze(a_				,       axis=2					)				,       squeeze(a_				,       axis=2					).numpy()					)					)



		@require_tf
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				np.random.randn(1				,       3				,       4					)
			__snake_case	:						Optional[int]						=				tf.constant(a_					)
			self.assertTrue(np.allclose(squeeze(a_					)				,       squeeze(a_					).numpy()					)					)

			__snake_case	:						List[Any]						=				np.random.randn(1				,       4				,       1				,       5					)
			__snake_case	:						Dict						=				tf.constant(a_					)
			self.assertTrue(np.allclose(squeeze(a_				,       axis=2					)				,       squeeze(a_				,       axis=2					).numpy()					)					)



		@require_flax
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						str						=				np.random.randn(1				,       3				,       4					)
			__snake_case	:						Union[str, Any]						=				jnp.array(a_					)
			self.assertTrue(np.allclose(squeeze(a_					)				,       np.asarray(squeeze(a_					)					)					)					)

			__snake_case	:						Union[str, Any]						=				np.random.randn(1				,       4				,       1				,       5					)
			__snake_case	:						Optional[Any]						=				jnp.array(a_					)
			self.assertTrue(np.allclose(squeeze(a_				,       axis=2					)				,       np.asarray(squeeze(a_				,       axis=2					)					)					)					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				np.random.randn(3				,       4					)
			self.assertTrue(np.allclose(expand_dims(a_				,       axis=1					)				,       np.expand_dims(a_				,       axis=1					)					)					)



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				np.random.randn(3				,       4					)
			__snake_case	:						Tuple						=				torch.tensor(a_					)
			self.assertTrue(np.allclose(expand_dims(a_				,       axis=1					)				,       expand_dims(a_				,       axis=1					).numpy()					)					)



		@require_tf
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		@require_flax
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				np.random.randn(3				,       4					)
			__snake_case	:						int						=				jnp.array(a_					)
			self.assertTrue(np.allclose(expand_dims(a_				,       axis=1					)				,       np.asarray(expand_dims(a_				,       axis=1					)					)					)					)









import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCAmelCase__      		= logging.get_logger(__name__)







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				None:
            warnings.warn(
                "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                " use BeitImageProcessor instead." ,						__lowerCamelCase ,						)
            super().__init__(*__lowerCamelCase ,						**__lowerCamelCase)
