
"""simple docstring"""



from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
 import torch


a					:	Optional[Any]							=		logging.get_logger(__name__)




class 		a_						( _UpperCAmelCase	):
    a      :				Tuple     =				['pixel_values']







    def __init__(			self			:       Optional[int]     ,					__UpperCamelCase			:       bool = True     ,					__UpperCamelCase			:       Optional[Dict[str, int]] = None     ,					__UpperCamelCase			:       PILImageResampling = PILImageResampling.BILINEAR     ,					__UpperCamelCase			:       bool = True     ,					__UpperCamelCase			:       Dict[str, int] = None     ,					__UpperCamelCase			:       bool = True     ,					__UpperCamelCase			:       Union[int, float] = 1 / 2_55     ,					__UpperCamelCase			:       bool = True     ,					__UpperCamelCase			:       Optional[Union[float, List[float]]] = None     ,					__UpperCamelCase			:       Optional[Union[float, List[float]]] = None     ,					**__UpperCamelCase			:       List[str]     ,					)		->None:






        '''simple docstring'''






        super().__init__(**__UpperCamelCase     )
        _UpperCAmelCase          =   size if size is not None else {"""shortest_edge""": 2_56}
        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     ,					default_to_square=__UpperCamelCase     )
        _UpperCAmelCase          =   crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     ,					param_name="""crop_size"""     )
        _UpperCAmelCase          =   do_resize
        _UpperCAmelCase          =   size
        _UpperCAmelCase          =   resample
        _UpperCAmelCase          =   do_center_crop
        _UpperCAmelCase          =   crop_size
        _UpperCAmelCase          =   do_rescale
        _UpperCAmelCase          =   rescale_factor
        _UpperCAmelCase          =   do_normalize
        _UpperCAmelCase          =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        _UpperCAmelCase          =   image_std if image_std is not None else IMAGENET_STANDARD_STD







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       np.ndarray     ,					__UpperCamelCase			:       Dict[str, int]     ,					__UpperCamelCase			:       PILImageResampling = PILImageResampling.BICUBIC     ,					__UpperCamelCase			:       Optional[Union[str, ChannelDimension]] = None     ,					**__UpperCamelCase			:       List[str]     ,					)		->np.ndarray:






        '''simple docstring'''






        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     ,					default_to_square=__UpperCamelCase     )
        if "shortest_edge" not in size:
            raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
        _UpperCAmelCase          =   get_resize_output_image_size(__UpperCamelCase     ,					size=size["""shortest_edge"""]     ,					default_to_square=__UpperCamelCase     )
        return resize(__UpperCamelCase     ,					size=__UpperCamelCase     ,					resample=__UpperCamelCase     ,					data_format=__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       np.ndarray     ,					__UpperCamelCase			:       Dict[str, int]     ,					__UpperCamelCase			:       Optional[Union[str, ChannelDimension]] = None     ,					**__UpperCamelCase			:       Tuple     ,					)		->np.ndarray:






        '''simple docstring'''






        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     )
        if "height" not in size or "width" not in size:
            raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""     )
        return center_crop(__UpperCamelCase     ,					size=(size["""height"""], size["""width"""])     ,					data_format=__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       np.ndarray     ,					__UpperCamelCase			:       float     ,					__UpperCamelCase			:       Optional[Union[str, ChannelDimension]] = None     ,					**__UpperCamelCase			:       Optional[Any]     )		->np.ndarray:






        '''simple docstring'''






        return rescale(__UpperCamelCase     ,					scale=__UpperCamelCase     ,					data_format=__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       np.ndarray     ,					__UpperCamelCase			:       Union[float, List[float]]     ,					__UpperCamelCase			:       Union[float, List[float]]     ,					__UpperCamelCase			:       Optional[Union[str, ChannelDimension]] = None     ,					**__UpperCamelCase			:       List[str]     ,					)		->np.ndarray:






        '''simple docstring'''






        return normalize(__UpperCamelCase     ,					mean=__UpperCamelCase     ,					std=__UpperCamelCase     ,					data_format=__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       ImageInput     ,					__UpperCamelCase			:       Optional[bool] = None     ,					__UpperCamelCase			:       Dict[str, int] = None     ,					__UpperCamelCase			:       PILImageResampling = None     ,					__UpperCamelCase			:       bool = None     ,					__UpperCamelCase			:       Dict[str, int] = None     ,					__UpperCamelCase			:       Optional[bool] = None     ,					__UpperCamelCase			:       Optional[float] = None     ,					__UpperCamelCase			:       Optional[bool] = None     ,					__UpperCamelCase			:       Optional[Union[float, List[float]]] = None     ,					__UpperCamelCase			:       Optional[Union[float, List[float]]] = None     ,					__UpperCamelCase			:       Optional[Union[str, TensorType]] = None     ,					__UpperCamelCase			:       Union[str, ChannelDimension] = ChannelDimension.FIRST     ,					**__UpperCamelCase			:       Optional[int]     ,					)		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase          =   size if size is not None else self.size
        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     ,					default_to_square=__UpperCamelCase     )
        _UpperCAmelCase          =   resample if resample is not None else self.resample
        _UpperCAmelCase          =   do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase          =   crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     ,					param_name="""crop_size"""     )
        _UpperCAmelCase          =   do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase          =   rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase          =   do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase          =   image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase          =   image_std if image_std is not None else self.image_std

        _UpperCAmelCase          =   make_list_of_images(__UpperCamelCase     )

        if not valid_images(__UpperCamelCase     ):
            raise ValueError(
                """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                """torch.Tensor, tf.Tensor or jax.ndarray."""     )

        if do_resize and size is None:
            raise ValueError("""Size must be specified if do_resize is True."""     )

        if do_center_crop and crop_size is None:
            raise ValueError("""Crop size must be specified if do_center_crop is True."""     )

        if do_rescale and rescale_factor is None:
            raise ValueError("""Rescale factor must be specified if do_rescale is True."""     )

        if do_normalize and (image_mean is None or image_std is None):
            raise ValueError("""Image mean and std must be specified if do_normalize is True."""     )

        # All transformations expect numpy arrays.
        _UpperCAmelCase          =   [to_numpy_array(__UpperCamelCase     ) for image in images]

        if do_resize:
            _UpperCAmelCase          =   [self.resize(image=__UpperCamelCase     ,					size=__UpperCamelCase     ,					resample=__UpperCamelCase     ) for image in images]

        if do_center_crop:
            _UpperCAmelCase          =   [self.center_crop(image=__UpperCamelCase     ,					size=__UpperCamelCase     ) for image in images]

        if do_rescale:
            _UpperCAmelCase          =   [self.rescale(image=__UpperCamelCase     ,					scale=__UpperCamelCase     ) for image in images]

        if do_normalize:
            _UpperCAmelCase          =   [self.normalize(image=__UpperCamelCase     ,					mean=__UpperCamelCase     ,					std=__UpperCamelCase     ) for image in images]

        _UpperCAmelCase          =   [to_channel_dimension_format(__UpperCamelCase     ,					__UpperCamelCase     ) for image in images]

        _UpperCAmelCase          =   {"""pixel_values""": images}
        return BatchFeature(data=__UpperCamelCase     ,					tensor_type=__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       List[Tuple] = None     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   outputs.logits

        # Resize logits and compute semantic segmentation maps
        if target_sizes is not None:
            if len(__UpperCamelCase     ) != len(__UpperCamelCase     ):
                raise ValueError(
                    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""     )

            if is_torch_tensor(__UpperCamelCase     ):
                _UpperCAmelCase          =   target_sizes.numpy()

            _UpperCAmelCase          =   []

            for idx in range(len(__UpperCamelCase     )     ):
                _UpperCAmelCase          =   torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0     )     ,					size=target_sizes[idx]     ,					mode="""bilinear"""     ,					align_corners=__UpperCamelCase     )
                _UpperCAmelCase          =   resized_logits[0].argmax(dim=0     )
                semantic_segmentation.append(__UpperCamelCase     )
        else:
            _UpperCAmelCase          =   logits.argmax(dim=1     )
            _UpperCAmelCase          =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]     )]

        return semantic_segmentation

"""simple docstring"""



import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 a					:	str							=		'''platform'''
 import jax
 import jax.numpy as jnp
 import numpy as np

 from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel




@require_flax
class 		a_						:
    a      :				List[Any]     =				PegasusConfig
    a      :				Dict     =				{}
    a      :				List[Any]     =				'gelu'







    def __init__(			self			:       Any     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Tuple=13     ,					__UpperCamelCase			:       Tuple=7     ,					__UpperCamelCase			:       Tuple=True     ,					__UpperCamelCase			:       Any=False     ,					__UpperCamelCase			:       Any=99     ,					__UpperCamelCase			:       Optional[int]=32     ,					__UpperCamelCase			:       Dict=5     ,					__UpperCamelCase			:       List[str]=4     ,					__UpperCamelCase			:       Dict=37     ,					__UpperCamelCase			:       int=0.1     ,					__UpperCamelCase			:       Dict=0.1     ,					__UpperCamelCase			:       Optional[Any]=20     ,					__UpperCamelCase			:       Tuple=2     ,					__UpperCamelCase			:       Optional[int]=1     ,					__UpperCamelCase			:       Tuple=0     ,					)		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   parent
        _UpperCAmelCase          =   batch_size
        _UpperCAmelCase          =   seq_length
        _UpperCAmelCase          =   is_training
        _UpperCAmelCase          =   use_labels
        _UpperCAmelCase          =   vocab_size
        _UpperCAmelCase          =   hidden_size
        _UpperCAmelCase          =   num_hidden_layers
        _UpperCAmelCase          =   num_attention_heads
        _UpperCAmelCase          =   intermediate_size

        _UpperCAmelCase          =   hidden_dropout_prob
        _UpperCAmelCase          =   attention_probs_dropout_prob
        _UpperCAmelCase          =   max_position_embeddings
        _UpperCAmelCase          =   eos_token_id
        _UpperCAmelCase          =   pad_token_id
        _UpperCAmelCase          =   bos_token_id







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






        '''simple docstring'''






        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.seq_length - 1]     ,					self.vocab_size     ).clip(3     ,					self.vocab_size     )
        _UpperCAmelCase          =   np.expand_dims(np.array([self.eos_token_id] * self.batch_size     )     ,					1     )
        _UpperCAmelCase          =   np.concatenate([input_ids, eos_tensor]     ,					axis=1     )

        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size     )

        _UpperCAmelCase          =   self.config_cls(
            vocab_size=self.vocab_size     ,					d_model=self.hidden_size     ,					encoder_layers=self.num_hidden_layers     ,					decoder_layers=self.num_hidden_layers     ,					encoder_attention_heads=self.num_attention_heads     ,					decoder_attention_heads=self.num_attention_heads     ,					encoder_ffn_dim=self.intermediate_size     ,					decoder_ffn_dim=self.intermediate_size     ,					dropout=self.hidden_dropout_prob     ,					attention_dropout=self.attention_probs_dropout_prob     ,					max_position_embeddings=self.max_position_embeddings     ,					eos_token_ids=[2]     ,					bos_token_id=self.bos_token_id     ,					pad_token_id=self.pad_token_id     ,					decoder_start_token_id=self.pad_token_id     ,					**self.config_updates     ,					)
        _UpperCAmelCase          =   prepare_pegasus_inputs_dict(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        return config, inputs_dict







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       List[Any]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   20
        _UpperCAmelCase          =   model_class_name(__UpperCamelCase     )

        _UpperCAmelCase          =   model.encode(inputs_dict["""input_ids"""]     )

        _UpperCAmelCase    ,_UpperCAmelCase          =   (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        _UpperCAmelCase          =   model.init_cache(decoder_input_ids.shape[0]     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,					dtype="""i4"""     )

        _UpperCAmelCase          =   jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]     ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,					)
        _UpperCAmelCase          =   model.decode(
            decoder_input_ids[:, :-1]     ,					__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					past_key_values=__UpperCamelCase     ,					decoder_position_ids=__UpperCamelCase     ,					)

        _UpperCAmelCase          =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,					dtype="""i4"""     )
        _UpperCAmelCase          =   model.decode(
            decoder_input_ids[:, -1:]     ,					__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					past_key_values=outputs_cache.past_key_values     ,					decoder_position_ids=__UpperCamelCase     ,					)

        _UpperCAmelCase          =   model.decode(__UpperCamelCase     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1e-3     ,					msg=f"""Max diff is {diff}"""     )







    def      _snake_case					(			self			:       Any     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Union[str, Any]     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   20
        _UpperCAmelCase          =   model_class_name(__UpperCamelCase     )

        _UpperCAmelCase          =   model.encode(inputs_dict["""input_ids"""]     )

        _UpperCAmelCase    ,_UpperCAmelCase          =   (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        _UpperCAmelCase          =   jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
            ]     ,					axis=-1     ,					)

        _UpperCAmelCase          =   model.init_cache(decoder_input_ids.shape[0]     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]     ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,					)

        _UpperCAmelCase          =   model.decode(
            decoder_input_ids[:, :-1]     ,					__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					past_key_values=__UpperCamelCase     ,					decoder_position_ids=__UpperCamelCase     ,					)
        _UpperCAmelCase          =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,					dtype="""i4"""     )
        _UpperCAmelCase          =   model.decode(
            decoder_input_ids[:, -1:]     ,					__UpperCamelCase     ,					past_key_values=outputs_cache.past_key_values     ,					decoder_attention_mask=__UpperCamelCase     ,					decoder_position_ids=__UpperCamelCase     ,					)

        _UpperCAmelCase          =   model.decode(__UpperCamelCase     ,					__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     )

        _UpperCAmelCase          =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1e-3     ,					msg=f"""Max diff is {diff}"""     )







def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A=None					,      _A=None					,      )    ->  int:





    """simple docstring"""

    if attention_mask is None:
        _UpperCAmelCase          =   np.not_equal(_A					,      config.pad_token_id      ).astype(np.inta      )
    if decoder_attention_mask is None:
        _UpperCAmelCase          =   np.concatenate(
            [
                np.ones(decoder_input_ids[:, :1].shape					,      dtype=np.inta      ),
                np.not_equal(decoder_input_ids[:, 1:]					,      config.pad_token_id      ).astype(np.inta      ),
            ]					,      axis=-1					,      )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
    }







@require_flax
class 		a_						( _UpperCAmelCase     ,						unittest.TestCase	):
    a      :				List[str]     =				(
        (
            FlaxPegasusForConditionalGeneration,
            FlaxPegasusModel,
        )
        if is_flax_available()
        else ()
    )
    a      :				Any     =				(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
    a      :				Any     =				True
    a      :				int     =				False
    a      :				Union[str, Any]     =				False
    a      :				Optional[int]     =				False







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






        '''simple docstring'''






        _UpperCAmelCase          =   FlaxPegasusModelTester(self     )
        _UpperCAmelCase          =   ConfigTester(self     ,					config_class=__UpperCamelCase     )







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






        '''simple docstring'''






        self.config_tester.run_common_tests()







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






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       Dict     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       Dict     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__     ):
                _UpperCAmelCase          =   self._prepare_for_class(__UpperCamelCase     ,					__UpperCamelCase     )
                _UpperCAmelCase          =   model_class(__UpperCamelCase     )

                @jax.jit
                def encode_jitted(__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       str=None     ,					**__UpperCamelCase			:       int     ):
                    return model.encode(input_ids=__UpperCamelCase     ,					attention_mask=__UpperCamelCase     )

                with self.subTest("""JIT Enabled"""     ):
                    _UpperCAmelCase          =   encode_jitted(**__UpperCamelCase     ).to_tuple()

                with self.subTest("""JIT Disabled"""     ):
                    with jax.disable_jit():
                        _UpperCAmelCase          =   encode_jitted(**__UpperCamelCase     ).to_tuple()

                self.assertEqual(len(__UpperCamelCase     )     ,					len(__UpperCamelCase     )     )
                for jitted_output, output in zip(__UpperCamelCase     ,					__UpperCamelCase     ):
                    self.assertEqual(jitted_output.shape     ,					output.shape     )







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






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__     ):
                _UpperCAmelCase          =   model_class(__UpperCamelCase     )
                _UpperCAmelCase          =   model.encode(inputs_dict["""input_ids"""]     ,					inputs_dict["""attention_mask"""]     )

                _UpperCAmelCase          =   {
                    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                    """encoder_outputs""": encoder_outputs,
                }

                @jax.jit
                def decode_jitted(__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Tuple     ):
                    return model.decode(
                        decoder_input_ids=__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					encoder_outputs=__UpperCamelCase     ,					)

                with self.subTest("""JIT Enabled"""     ):
                    _UpperCAmelCase          =   decode_jitted(**__UpperCamelCase     ).to_tuple()

                with self.subTest("""JIT Disabled"""     ):
                    with jax.disable_jit():
                        _UpperCAmelCase          =   decode_jitted(**__UpperCamelCase     ).to_tuple()

                self.assertEqual(len(__UpperCamelCase     )     ,					len(__UpperCamelCase     )     )
                for jitted_output, output in zip(__UpperCamelCase     ,					__UpperCamelCase     ):
                    self.assertEqual(jitted_output.shape     ,					output.shape     )







    @slow
    def      _snake_case					(			self			:       int     )		->int:






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
            _UpperCAmelCase          =   model_class_name.from_pretrained("""google/pegasus-large"""     ,					from_pt=__UpperCamelCase     )
            _UpperCAmelCase          =   np.ones((1, 1)     )
            _UpperCAmelCase          =   model(__UpperCamelCase     )
            self.assertIsNotNone(__UpperCamelCase     )







    @slow
    def      _snake_case					(			self			:       Dict     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""     )
        _UpperCAmelCase          =   PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""     )

        _UpperCAmelCase          =   [
            """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
            """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
        ]

        _UpperCAmelCase          =   [
            """California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
            """Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
        ]

        _UpperCAmelCase          =   tokenizer(__UpperCamelCase     ,					return_tensors="""np"""     ,					truncation=__UpperCamelCase     ,					max_length=5_12     ,					padding=__UpperCamelCase     )
        _UpperCAmelCase          =   model.generate(**__UpperCamelCase     ,					num_beams=2     ).sequences
        _UpperCAmelCase          =   tokenizer.batch_decode(__UpperCamelCase     ,					skip_special_tokens=__UpperCamelCase     )
        assert tgt_text == decoded