



'''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      _lowerCAmelCase							(      unittest.TestCase			):


	def    _a     (self							):
				A_     :						str     =					{
				    """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},
				    }
				}
				A_     :						str     =					{
				    """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(lowercase							)	,				lowercase							)


	def    _a     (self							):
				A_     :						Optional[Any]     =					np.random.randn(3	,				4							)
				self.assertTrue(np.allclose(transpose(lowercase							)	,				x.transpose()							)							)

				A_     :						Union[str, Any]     =					np.random.randn(3	,				4	,				5							)
				self.assertTrue(np.allclose(transpose(lowercase	,				axes=(1, 2, 0)							)	,				x.transpose((1, 2, 0)							)							)							)


	@require_torch
	def    _a     (self							):
				A_     :						List[str]     =					np.random.randn(3	,				4							)
				A_     :						Optional[int]     =					torch.tensor(lowercase							)
				self.assertTrue(np.allclose(transpose(lowercase							)	,				transpose(lowercase							).numpy()							)							)

				A_     :						Union[str, Any]     =					np.random.randn(3	,				4	,				5							)
				A_     :						Tuple     =					torch.tensor(lowercase							)
				self.assertTrue(np.allclose(transpose(lowercase	,				axes=(1, 2, 0)							)	,				transpose(lowercase	,				axes=(1, 2, 0)							).numpy()							)							)


	@require_tf
	def    _a     (self							):
				A_     :						int     =					np.random.randn(3	,				4							)
				A_     :						Dict     =					tf.constant(lowercase							)
				self.assertTrue(np.allclose(transpose(lowercase							)	,				transpose(lowercase							).numpy()							)							)

				A_     :						int     =					np.random.randn(3	,				4	,				5							)
				A_     :						int     =					tf.constant(lowercase							)
				self.assertTrue(np.allclose(transpose(lowercase	,				axes=(1, 2, 0)							)	,				transpose(lowercase	,				axes=(1, 2, 0)							).numpy()							)							)


	@require_flax
	def    _a     (self							):
				A_     :						Optional[Any]     =					np.random.randn(3	,				4							)
				A_     :						Dict     =					jnp.array(lowercase							)
				self.assertTrue(np.allclose(transpose(lowercase							)	,				np.asarray(transpose(lowercase							)							)							)							)

				A_     :						Union[str, Any]     =					np.random.randn(3	,				4	,				5							)
				A_     :						Dict     =					jnp.array(lowercase							)
				self.assertTrue(np.allclose(transpose(lowercase	,				axes=(1, 2, 0)							)	,				np.asarray(transpose(lowercase	,				axes=(1, 2, 0)							)							)							)							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					np.random.randn(3	,				4							)
				self.assertTrue(np.allclose(reshape(lowercase	,				(4, 3)							)	,				np.reshape(lowercase	,				(4, 3)							)							)							)

				A_     :						Any     =					np.random.randn(3	,				4	,				5							)
				self.assertTrue(np.allclose(reshape(lowercase	,				(12, 5)							)	,				np.reshape(lowercase	,				(12, 5)							)							)							)


	@require_torch
	def    _a     (self							):
				A_     :						str     =					np.random.randn(3	,				4							)
				A_     :						Any     =					torch.tensor(lowercase							)
				self.assertTrue(np.allclose(reshape(lowercase	,				(4, 3)							)	,				reshape(lowercase	,				(4, 3)							).numpy()							)							)

				A_     :						str     =					np.random.randn(3	,				4	,				5							)
				A_     :						Dict     =					torch.tensor(lowercase							)
				self.assertTrue(np.allclose(reshape(lowercase	,				(12, 5)							)	,				reshape(lowercase	,				(12, 5)							).numpy()							)							)


	@require_tf
	def    _a     (self							):
				A_     :						str     =					np.random.randn(3	,				4							)
				A_     :						Optional[int]     =					tf.constant(lowercase							)
				self.assertTrue(np.allclose(reshape(lowercase	,				(4, 3)							)	,				reshape(lowercase	,				(4, 3)							).numpy()							)							)

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


	@require_flax
	def    _a     (self							):
				A_     :						List[Any]     =					np.random.randn(3	,				4							)
				A_     :						Dict     =					jnp.array(lowercase							)
				self.assertTrue(np.allclose(reshape(lowercase	,				(4, 3)							)	,				np.asarray(reshape(lowercase	,				(4, 3)							)							)							)							)

				A_     :						Any     =					np.random.randn(3	,				4	,				5							)
				A_     :						str     =					jnp.array(lowercase							)
				self.assertTrue(np.allclose(reshape(lowercase	,				(12, 5)							)	,				np.asarray(reshape(lowercase	,				(12, 5)							)							)							)							)


	def    _a     (self							):
				A_     :						str     =					np.random.randn(1	,				3	,				4							)
				self.assertTrue(np.allclose(squeeze(lowercase							)	,				np.squeeze(lowercase							)							)							)

				A_     :						int     =					np.random.randn(1	,				4	,				1	,				5							)
				self.assertTrue(np.allclose(squeeze(lowercase	,				axis=2							)	,				np.squeeze(lowercase	,				axis=2							)							)							)


	@require_torch
	def    _a     (self							):
				A_     :						Tuple     =					np.random.randn(1	,				3	,				4							)
				A_     :						Tuple     =					torch.tensor(lowercase							)
				self.assertTrue(np.allclose(squeeze(lowercase							)	,				squeeze(lowercase							).numpy()							)							)

				A_     :						Any     =					np.random.randn(1	,				4	,				1	,				5							)
				A_     :						Any     =					torch.tensor(lowercase							)
				self.assertTrue(np.allclose(squeeze(lowercase	,				axis=2							)	,				squeeze(lowercase	,				axis=2							).numpy()							)							)


	@require_tf
	def    _a     (self							):
				A_     :						List[Any]     =					np.random.randn(1	,				3	,				4							)
				A_     :						Any     =					tf.constant(lowercase							)
				self.assertTrue(np.allclose(squeeze(lowercase							)	,				squeeze(lowercase							).numpy()							)							)

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


	@require_flax
	def    _a     (self							):
				A_     :						List[Any]     =					np.random.randn(1	,				3	,				4							)
				A_     :						Tuple     =					jnp.array(lowercase							)
				self.assertTrue(np.allclose(squeeze(lowercase							)	,				np.asarray(squeeze(lowercase							)							)							)							)

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


	def    _a     (self							):
				A_     :						Dict     =					np.random.randn(3	,				4							)
				self.assertTrue(np.allclose(expand_dims(lowercase	,				axis=1							)	,				np.expand_dims(lowercase	,				axis=1							)							)							)


	@require_torch
	def    _a     (self							):
				A_     :						Any     =					np.random.randn(3	,				4							)
				A_     :						int     =					torch.tensor(lowercase							)
				self.assertTrue(np.allclose(expand_dims(lowercase	,				axis=1							)	,				expand_dims(lowercase	,				axis=1							).numpy()							)							)


	@require_tf
	def    _a     (self							):
				A_     :						Any     =					np.random.randn(3	,				4							)
				A_     :						Tuple     =					tf.constant(lowercase							)
				self.assertTrue(np.allclose(expand_dims(lowercase	,				axis=1							)	,				expand_dims(lowercase	,				axis=1							).numpy()							)							)


	@require_flax
	def    _a     (self							):
				A_     :						Dict     =					np.random.randn(3	,				4							)
				A_     :						Optional[Any]     =					jnp.array(lowercase							)
				self.assertTrue(np.allclose(expand_dims(lowercase	,				axis=1							)	,				np.asarray(expand_dims(lowercase	,				axis=1							)							)							)							)




'''simple docstring'''




from typing import Dict

from .base import GenericTensor, Pipeline







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def    _a     (self	,				lowercase=None	,				lowercase=None	,				lowercase=None	,				**lowercase							):
				if tokenize_kwargs is None:
							A_     :						Optional[Any]     =					{}

				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_     :						str     =					truncation

				A_     :						List[str]     =					tokenize_kwargs

				A_     :						Dict     =					{}
				if return_tensors is not None:
							A_     :						List[Any]     =					return_tensors

				return preprocess_params, {}, postprocess_params


	def    _a     (self	,				lowercase	,				**lowercase							):
				A_     :						Optional[int]     =					self.framework
				A_     :						str     =					self.tokenizer(lowercase	,				return_tensors=lowercase	,				**lowercase							)
				return model_inputs


	def    _a     (self	,				lowercase							):
				A_     :						str     =					self.model(**lowercase							)
				return model_outputs


	def    _a     (self	,				lowercase	,				lowercase=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	,				*lowercase	,				**lowercase							):
				return super().__call__(*lowercase	,				**lowercase							)