


'''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	):




    def 					a__(		self      :			int				)->					List[str]:

          """simple docstring"""




          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(lowerCAmelCase				)				,   lowerCAmelCase				)




    def 					a__(		self      :			List[Any]				)->					Union[str, Any]:

          """simple docstring"""




          UpperCAmelCase					    =     np.random.randn(3				,   4				)
          self.assertTrue(np.allclose(transpose(lowerCAmelCase				)				,   x.transpose()				)				)

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




    @require_torch
    def 					a__(		self      :			Tuple				)->					Dict:

          """simple docstring"""




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

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




    @require_tf
    def 					a__(		self      :			List[str]				)->					Tuple:

          """simple docstring"""




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

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




    @require_flax
    def 					a__(		self      :			str				)->					Dict:

          """simple docstring"""




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

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




    def 					a__(		self      :			Union[str, Any]				)->					Optional[Any]:

          """simple docstring"""




          UpperCAmelCase					    =     np.random.randn(3				,   4				)
          self.assertTrue(np.allclose(reshape(lowerCAmelCase				,   (4, 3)				)				,   np.reshape(lowerCAmelCase				,   (4, 3)				)				)				)

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




    @require_torch
    def 					a__(		self      :			Union[str, Any]				)->					Any:

          """simple docstring"""




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

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




    @require_tf
    def 					a__(		self      :			Any				)->					Optional[Any]:

          """simple docstring"""




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

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




    @require_flax
    def 					a__(		self      :			Union[str, Any]				)->					List[str]:

          """simple docstring"""




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

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




    def 					a__(		self      :			Any				)->					Optional[Any]:

          """simple docstring"""




          UpperCAmelCase					    =     np.random.randn(1				,   3				,   4				)
          self.assertTrue(np.allclose(squeeze(lowerCAmelCase				)				,   np.squeeze(lowerCAmelCase				)				)				)

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




    @require_torch
    def 					a__(		self      :			List[str]				)->					Dict:

          """simple docstring"""




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

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




    @require_tf
    def 					a__(		self      :			List[str]				)->					int:

          """simple docstring"""




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

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




    @require_flax
    def 					a__(		self      :			Optional[Any]				)->					Tuple:

          """simple docstring"""




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

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




    def 					a__(		self      :			Union[str, Any]				)->					Optional[int]:

          """simple docstring"""




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




    @require_torch
    def 					a__(		self      :			str				)->					str:

          """simple docstring"""




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




    @require_tf
    def 					a__(		self      :			List[Any]				)->					int:

          """simple docstring"""




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





    @require_flax
    def 					a__(		self      :			List[str]				)->					List[str]:

          """simple docstring"""




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









'''simple docstring'''



from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
	from .features import FeatureType






@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				List[str]
				__magic_name__    :				Optional[str]  		=	None
				# Automatically constructed
				__magic_name__    :				ClassVar[str]  		=	"dict"
				__magic_name__    :				ClassVar[Any]  		=	None
				__magic_name__    :				str  		=	field(default="Translation"						,    init=lowerCAmelCase						,    repr=lowerCAmelCase	)




				def __call__(		self      :			Union[str, Any]				)->					str:

										"""simple docstring"""




										return pa.struct({lang: pa.string() for lang in sorted(self.languages				)}				)





				def 					a__(		self      :			int				)->					Union["FeatureType", Dict[str, "FeatureType"]]:

										"""simple docstring"""




										from .features import Value

										return {k: Value('''string'''				) for k in sorted(self.languages				)}







@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				Optional[List]  		=	None
				__magic_name__    :				Optional[int]  		=	None
				__magic_name__    :				Optional[str]  		=	None
				# Automatically constructed
				__magic_name__    :				ClassVar[str]  		=	"dict"
				__magic_name__    :				ClassVar[Any]  		=	None
				__magic_name__    :				str  		=	field(default="TranslationVariableLanguages"						,    init=lowerCAmelCase						,    repr=lowerCAmelCase	)




				def 					a__(		self      :			Union[str, Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     sorted(set(self.languages				)				) if self.languages else None
										UpperCAmelCase					    =     len(self.languages				) if self.languages else None




				def __call__(		self      :			int				)->					Optional[Any]:

										"""simple docstring"""




										return pa.struct({'''language''': pa.list_(pa.string()				), '''translation''': pa.list_(pa.string()				)}				)




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     set(self.languages				)
										if self.languages and set(lowerCAmelCase				) - lang_set:
																raise ValueError(
																    F"""Some languages in example ({", ".join(sorted(set(lowerCAmelCase				) - lang_set				)				)}) are not in valid set ({", ".join(lowerCAmelCase				)})."""				)

										# Convert dictionary into tuples, splitting out cases where there are
										# multiple translations for a single language.
										UpperCAmelCase					    =     []
										for lang, text in translation_dict.items():
																if isinstance(lowerCAmelCase				,   lowerCAmelCase				):
																						translation_tuples.append((lang, text)				)
																else:
																						translation_tuples.extend([(lang, el) for el in text]				)

        # Ensure translations are in ascending order by language code.
										UpperCAmelCase    ,    UpperCAmelCase					    =     zip(*sorted(lowerCAmelCase				)				)

										return {"language": languages, "translation": translations}





				def 					a__(		self      :			Any				)->					Union["FeatureType", Dict[str, "FeatureType"]]:

										"""simple docstring"""




										from .features import Sequence, Value

										return {
										    "language": Sequence(Value('''string'''				)				),
										    "translation": Sequence(Value('''string'''				)				),
										}





