

from typing import Dict

from .base import GenericTensor, Pipeline



class 							_UpperCAmelCase			( __lowercase      ):
  '''simple docstring'''







  def 						UpperCamelCase							(			self						:			Dict		,    UpperCamelCase__						:			int=None		,    UpperCamelCase__						:			List[Any]=None		,    UpperCamelCase__						:			Tuple=None		,    **UpperCamelCase__						:			str			):
        if tokenize_kwargs is None:
              A 			=					{}

        if truncation is not None:
              if "truncation" in tokenize_kwargs:
                    raise ValueError(
                        'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'			)
              A 			=					truncation

        A 			=					tokenize_kwargs

        A 			=					{}
        if return_tensors is not None:
              A 			=					return_tensors

        return preprocess_params, {}, postprocess_params







  def 						UpperCamelCase							(			self						:			Union[str, Any]		,    UpperCamelCase__						:			List[Any]		,    **UpperCamelCase__						:			int			):
        A 			=					self.framework
        A 			=					self.tokenizer(__lowerCAmelCase		,    return_tensors=__lowerCAmelCase		,    **__lowerCAmelCase			)
        return model_inputs







  def 						UpperCamelCase							(			self						:			Dict		,    UpperCamelCase__						:			int			):
        A 			=					self.model(**__lowerCAmelCase			)
        return model_outputs







  def 						UpperCamelCase							(			self						:			List[str]		,    UpperCamelCase__						:			Optional[Any]		,    UpperCamelCase__						:			Optional[int]=False			):
        # [0] is the first available tensor, logits or last_hidden_state.
        if return_tensors:
              return model_outputs[0]
        if self.framework == "pt":
              return model_outputs[0].tolist()
        elif self.framework == "tf":
              return model_outputs[0].numpy().tolist()







  def __call__(			self						:			Optional[int]		,    *UpperCamelCase__						:			List[Any]		,    **UpperCamelCase__						:			List[str]			):
        return super().__call__(*__lowerCAmelCase		,    **__lowerCAmelCase			)











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			):







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[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},
								    }
								}
								__magic_name__		:List[str]   =   {
								    '''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(__lowerCAmelCase  )							,       __lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   np.random.randn(3							,       4  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase  )							,       x.transpose()  )  )

								__magic_name__		:List[Any]   =   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  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   np.random.randn(3							,       4  )
								__magic_name__		:Tuple   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase  )							,       transpose(__lowerCAmelCase  ).numpy()  )  )

								__magic_name__		:int   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:Union[str, Any]   =   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  ):
								"""simple docstring"""
								__magic_name__		:int   =   np.random.randn(3							,       4  )
								__magic_name__		:Optional[Any]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase  )							,       transpose(__lowerCAmelCase  ).numpy()  )  )

								__magic_name__		:List[str]   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:Optional[Any]   =   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  ):
								"""simple docstring"""
								__magic_name__		:int   =   np.random.randn(3							,       4  )
								__magic_name__		:Dict   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase  )							,       np.asarray(transpose(__lowerCAmelCase  )  )  )  )

								__magic_name__		:Dict   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:Dict   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  )							,       np.asarray(transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  )  )  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   np.random.randn(3							,       4  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (4, 3)  )							,       np.reshape(__lowerCAmelCase							,       (4, 3)  )  )  )

								__magic_name__		:Union[str, Any]   =   np.random.randn(3							,       4							,       5  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (1_2, 5)  )							,       np.reshape(__lowerCAmelCase							,       (1_2, 5)  )  )  )







				@require_torch
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   np.random.randn(3							,       4  )
								__magic_name__		:Tuple   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (4, 3)  )							,       reshape(__lowerCAmelCase							,       (4, 3)  ).numpy()  )  )

								__magic_name__		:Union[str, Any]   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:List[str]   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (1_2, 5)  )							,       reshape(__lowerCAmelCase							,       (1_2, 5)  ).numpy()  )  )







				@require_tf
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   np.random.randn(3							,       4  )
								__magic_name__		:Union[str, Any]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (4, 3)  )							,       reshape(__lowerCAmelCase							,       (4, 3)  ).numpy()  )  )

								__magic_name__		:List[Any]   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:Optional[int]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (1_2, 5)  )							,       reshape(__lowerCAmelCase							,       (1_2, 5)  ).numpy()  )  )







				@require_flax
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   np.random.randn(3							,       4  )
								__magic_name__		:Any   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (4, 3)  )							,       np.asarray(reshape(__lowerCAmelCase							,       (4, 3)  )  )  )  )

								__magic_name__		:List[Any]   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:List[str]   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (1_2, 5)  )							,       np.asarray(reshape(__lowerCAmelCase							,       (1_2, 5)  )  )  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   np.random.randn(1							,       3							,       4  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase  )							,       np.squeeze(__lowerCAmelCase  )  )  )

								__magic_name__		:Optional[Any]   =   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  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   np.random.randn(1							,       3							,       4  )
								__magic_name__		:List[Any]   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase  )							,       squeeze(__lowerCAmelCase  ).numpy()  )  )

								__magic_name__		:List[str]   =   np.random.randn(1							,       4							,       1							,       5  )
								__magic_name__		:str   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase							,       axis=2  )							,       squeeze(__lowerCAmelCase							,       axis=2  ).numpy()  )  )







				@require_tf
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   np.random.randn(1							,       3							,       4  )
								__magic_name__		:Tuple   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase  )							,       squeeze(__lowerCAmelCase  ).numpy()  )  )

								__magic_name__		:Tuple   =   np.random.randn(1							,       4							,       1							,       5  )
								__magic_name__		:Optional[int]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase							,       axis=2  )							,       squeeze(__lowerCAmelCase							,       axis=2  ).numpy()  )  )







				@require_flax
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   np.random.randn(1							,       3							,       4  )
								__magic_name__		:Optional[Any]   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase  )							,       np.asarray(squeeze(__lowerCAmelCase  )  )  )  )

								__magic_name__		:List[Any]   =   np.random.randn(1							,       4							,       1							,       5  )
								__magic_name__		:Optional[Any]   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase							,       axis=2  )							,       np.asarray(squeeze(__lowerCAmelCase							,       axis=2  )  )  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   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  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   np.random.randn(3							,       4  )
								__magic_name__		:Any   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(expand_dims(__lowerCAmelCase							,       axis=1  )							,       expand_dims(__lowerCAmelCase							,       axis=1  ).numpy()  )  )







				@require_tf
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   np.random.randn(3							,       4  )
								__magic_name__		:Union[str, Any]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(expand_dims(__lowerCAmelCase							,       axis=1  )							,       expand_dims(__lowerCAmelCase							,       axis=1  ).numpy()  )  )







				@require_flax
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   np.random.randn(3							,       4  )
								__magic_name__		:Tuple   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(expand_dims(__lowerCAmelCase							,       axis=1  )							,       np.asarray(expand_dims(__lowerCAmelCase							,       axis=1  )  )  )  )






