






"""simple docstring"""

from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


lowercase    			=       [
    '''openmmlab/upernet-convnext-tiny''',
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
lowercase    			=       '''UperNetConfig'''

class 						A_							(      nn.Module  ):







				def __init__(							self							:						Dict						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						Union[int, Tuple[int, int]]						,    __lowerCamelCase							:						Union[int, Tuple[int, int], str] = 0						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						Union[int, Tuple[int, int]] = 1						,    )				->					None:
										super().__init__()
										__magic_name__            =     nn.Convad(
										    in_channels=__lowerCamelCase						,    out_channels=__lowerCamelCase						,    kernel_size=__lowerCamelCase						,    padding=__lowerCamelCase						,    bias=__lowerCamelCase						,    dilation=__lowerCamelCase						,    )
										__magic_name__            =     nn.BatchNormad(__lowerCamelCase	)
										__magic_name__            =     nn.ReLU()







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						torch.Tensor	)				->					torch.Tensor:
										__magic_name__            =     self.conv(__lowerCamelCase	)
										__magic_name__            =     self.batch_norm(__lowerCamelCase	)
										__magic_name__            =     self.activation(__lowerCamelCase	)

										return output





class 						A_							(      nn.Module  ):







				def __init__(							self							:						Dict						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int	)				->					None:
										super().__init__()
										__magic_name__            =     [
										    nn.AdaptiveAvgPoolad(__lowerCamelCase	),
										    UperNetConvModule(__lowerCamelCase						,    __lowerCamelCase						,    kernel_size=1	),
										]
										for i, layer in enumerate(self.layers	):
																self.add_module(str(__lowerCamelCase	)						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						torch.Tensor	)				->					torch.Tensor:
										__magic_name__            =     input
										for layer in self.layers:
																__magic_name__            =     layer(__lowerCamelCase	)
										return hidden_state





class 						A_							(      nn.Module  ):







				def __init__(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Tuple[int, ...]						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						bool	)				->					None:
										super().__init__()
										__magic_name__            =     pool_scales
										__magic_name__            =     align_corners
										__magic_name__            =     in_channels
										__magic_name__            =     channels
										__magic_name__            =     []
										for i, pool_scale in enumerate(__lowerCamelCase	):
																__magic_name__            =     UperNetPyramidPoolingBlock(pool_scale=__lowerCamelCase						,    in_channels=__lowerCamelCase						,    channels=__lowerCamelCase	)
																self.blocks.append(__lowerCamelCase	)
																self.add_module(str(__lowerCamelCase	)						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						torch.Tensor	)				->					List[torch.Tensor]:
										__magic_name__            =     []
										for ppm in self.blocks:
																__magic_name__            =     ppm(__lowerCamelCase	)
																__magic_name__            =     nn.functional.interpolate(
																    __lowerCamelCase						,    size=x.size()[2:]						,    mode="bilinear"						,    align_corners=self.align_corners	)
																ppm_outs.append(__lowerCamelCase	)
										return ppm_outs





class 						A_							(      nn.Module  ):







				def __init__(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						str	)				->					List[Any]:
										super().__init__()

										__magic_name__            =     config
										__magic_name__            =     config.pool_scales  # e.g. (1, 2, 3, 6)
										__magic_name__            =     in_channels
										__magic_name__            =     config.hidden_size
										__magic_name__            =     False
										__magic_name__            =     nn.Convad(self.channels						,    config.num_labels						,    kernel_size=1	)

										# PSP Module
										__magic_name__            =     UperNetPyramidPoolingModule(
										    self.pool_scales						,    self.in_channels[-1]						,    self.channels						,    align_corners=self.align_corners						,    )
										__magic_name__            =     UperNetConvModule(
										    self.in_channels[-1] + len(self.pool_scales	) * self.channels						,    self.channels						,    kernel_size=3						,    padding=1						,    )
										# FPN Module
										__magic_name__            =     nn.ModuleList()
										__magic_name__            =     nn.ModuleList()
										for in_channels in self.in_channels[:-1]:  # skip the top layer
																__magic_name__            =     UperNetConvModule(__lowerCamelCase						,    self.channels						,    kernel_size=1	)
																__magic_name__            =     UperNetConvModule(self.channels						,    self.channels						,    kernel_size=3						,    padding=1	)
																self.lateral_convs.append(__lowerCamelCase	)
																self.fpn_convs.append(__lowerCamelCase	)

										__magic_name__            =     UperNetConvModule(
										    len(self.in_channels	) * self.channels						,    self.channels						,    kernel_size=3						,    padding=1						,    )







				def     _snake_case					(							self							:						Any	)				->					int:
										self.apply(self._init_weights	)







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Dict	)				->					int:
										if isinstance(__lowerCamelCase						,    nn.Convad	):
																module.weight.data.normal_(mean=0.0						,    std=self.config.initializer_range	)
																if module.bias is not None:
																						module.bias.data.zero_()







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						str	)				->					Optional[Any]:
										__magic_name__            =     inputs[-1]
										__magic_name__            =     [x]
										psp_outs.extend(self.psp_modules(__lowerCamelCase	)	)
										__magic_name__            =     torch.cat(__lowerCamelCase						,    dim=1	)
										__magic_name__            =     self.bottleneck(__lowerCamelCase	)

										return output







				def     _snake_case					(							self							:						List[Any]						,    __lowerCamelCase							:						torch.Tensor	)				->					torch.Tensor:
										# build laterals
										__magic_name__            =     [lateral_conv(encoder_hidden_states[i]	) for i, lateral_conv in enumerate(self.lateral_convs	)]

										laterals.append(self.psp_forward(__lowerCamelCase	)	)

										# build top-down path
										__magic_name__            =     len(__lowerCamelCase	)
										for i in range(used_backbone_levels - 1						,    0						,    -1	):
																__magic_name__            =     laterals[i - 1].shape[2:]
																__magic_name__            =     laterals[i - 1] + nn.functional.interpolate(
																    laterals[i]						,    size=__lowerCamelCase						,    mode="bilinear"						,    align_corners=self.align_corners	)

										# build outputs
										__magic_name__            =     [self.fpn_convs[i](laterals[i]	) for i in range(used_backbone_levels - 1	)]
										# append psp feature
										fpn_outs.append(laterals[-1]	)

										for i in range(used_backbone_levels - 1						,    0						,    -1	):
																__magic_name__            =     nn.functional.interpolate(
																    fpn_outs[i]						,    size=fpn_outs[0].shape[2:]						,    mode="bilinear"						,    align_corners=self.align_corners	)
										__magic_name__            =     torch.cat(__lowerCamelCase						,    dim=1	)
										__magic_name__            =     self.fpn_bottleneck(__lowerCamelCase	)
										__magic_name__            =     self.classifier(__lowerCamelCase	)

										return output





class 						A_							(      nn.Module  ):







				def __init__(							self							:						List[Any]						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						int = 2						,    __lowerCamelCase							:						int = 3						,    __lowerCamelCase							:						Union[int, Tuple[int, int]] = 1	)				->					None:
										super().__init__()

										__magic_name__            =     config
										__magic_name__            =     config.auxiliary_in_channels
										__magic_name__            =     config.auxiliary_channels
										__magic_name__            =     config.auxiliary_num_convs
										__magic_name__            =     config.auxiliary_concat_input
										__magic_name__            =     in_index

										__magic_name__            =     (kernel_size // 2) * dilation
										__magic_name__            =     []
										convs.append(
										    UperNetConvModule(
										        self.in_channels						,    self.channels						,    kernel_size=__lowerCamelCase						,    padding=__lowerCamelCase						,    dilation=__lowerCamelCase	)	)
										for i in range(self.num_convs - 1	):
																convs.append(
																    UperNetConvModule(
																        self.channels						,    self.channels						,    kernel_size=__lowerCamelCase						,    padding=__lowerCamelCase						,    dilation=__lowerCamelCase	)	)
										if self.num_convs == 0:
																__magic_name__            =     nn.Identity()
										else:
																__magic_name__            =     nn.Sequential(*__lowerCamelCase	)
										if self.concat_input:
																__magic_name__            =     UperNetConvModule(
																    self.in_channels + self.channels						,    self.channels						,    kernel_size=__lowerCamelCase						,    padding=kernel_size // 2	)

										__magic_name__            =     nn.Convad(self.channels						,    config.num_labels						,    kernel_size=1	)







				def     _snake_case					(							self							:						Dict	)				->					Optional[int]:
										self.apply(self._init_weights	)







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						List[Any]	)				->					Optional[int]:
										if isinstance(__lowerCamelCase						,    nn.Convad	):
																module.weight.data.normal_(mean=0.0						,    std=self.config.initializer_range	)
																if module.bias is not None:
																						module.bias.data.zero_()







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						torch.Tensor	)				->					torch.Tensor:
										# just take the relevant feature maps
										__magic_name__            =     encoder_hidden_states[self.in_index]
										__magic_name__            =     self.convs(__lowerCamelCase	)
										if self.concat_input:
																__magic_name__            =     self.conv_cat(torch.cat([hidden_states, output]						,    dim=1	)	)
										__magic_name__            =     self.classifier(__lowerCamelCase	)
										return output





class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				UperNetConfig
				UpperCAmelCase__        =				'''pixel_values'''
				UpperCAmelCase__        =				True







				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						Optional[int]	)				->					int:
										if isinstance(__lowerCamelCase						,    __lowerCamelCase	):
																module.backbone.init_weights()
																module.decode_head.init_weights()
																module.auxiliary_head.init_weights()







				def     _snake_case					(							self							:						Tuple	)				->					Tuple:
										self.backbone.init_weights()
										self.decode_head.init_weights()
										self.auxiliary_head.init_weights()







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Dict=False	)				->					Any:
										if isinstance(__lowerCamelCase						,    __lowerCamelCase	):
																__magic_name__            =     value





lowercase    			=       r'''
    Parameters:
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

lowercase    			=       r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
            `attentions` under returned tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
            returned tensors for more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''

@add_start_docstrings(
    '''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.'''					,							snake_case_					,							)
class 						A_							(      snake_case_  ):







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						int	)				->					List[Any]:
										super().__init__(__lowerCamelCase	)

										__magic_name__            =     AutoBackbone.from_config(config.backbone_config	)

										# Semantic segmentation head(s)
										__magic_name__            =     UperNetHead(__lowerCamelCase						,    in_channels=self.backbone.channels	)
										__magic_name__            =     UperNetFCNHead(__lowerCamelCase	) if config.use_auxiliary_head else None

										# Initialize weights and apply final processing
										self.post_init()







				@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"	)	)
				@replace_return_docstrings(output_type=__lowerCamelCase						,    config_class=_CONFIG_FOR_DOC	)
				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						Optional[torch.Tensor] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    __lowerCamelCase							:						Optional[torch.Tensor] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    )				->					Union[tuple, SemanticSegmenterOutput]:
										__magic_name__            =     return_dict if return_dict is not None else self.config.use_return_dict
										__magic_name__            =     (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										__magic_name__            =     output_attentions if output_attentions is not None else self.config.output_attentions

										__magic_name__            =     self.backbone.forward_with_filtered_kwargs(
										    __lowerCamelCase						,    output_hidden_states=__lowerCamelCase						,    output_attentions=__lowerCamelCase	)
										__magic_name__            =     outputs.feature_maps

										__magic_name__            =     self.decode_head(__lowerCamelCase	)
										__magic_name__            =     nn.functional.interpolate(__lowerCamelCase						,    size=pixel_values.shape[2:]						,    mode="bilinear"						,    align_corners=__lowerCamelCase	)

										__magic_name__            =     None
										if self.auxiliary_head is not None:
																__magic_name__            =     self.auxiliary_head(__lowerCamelCase	)
																__magic_name__            =     nn.functional.interpolate(
																    __lowerCamelCase						,    size=pixel_values.shape[2:]						,    mode="bilinear"						,    align_corners=__lowerCamelCase	)

										__magic_name__            =     None
										if labels is not None:
																if self.config.num_labels == 1:
																						raise ValueError("The number of labels should be greater than one"	)
																else:
																						# compute weighted loss
																						__magic_name__            =     CrossEntropyLoss(ignore_index=self.config.loss_ignore_index	)
																						__magic_name__            =     loss_fct(__lowerCamelCase						,    __lowerCamelCase	)
																						__magic_name__            =     loss_fct(__lowerCamelCase						,    __lowerCamelCase	)
																						__magic_name__            =     main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

										if not return_dict:
																if output_hidden_states:
																						__magic_name__            =     (logits,) + outputs[1:]
																else:
																						__magic_name__            =     (logits,) + outputs[2:]
																return ((loss,) + output) if loss is not None else output

										return SemanticSegmenterOutput(
										    loss=__lowerCamelCase						,    logits=__lowerCamelCase						,    hidden_states=outputs.hidden_states						,    attentions=outputs.attentions						,    )











"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFLEDForConditionalGeneration, TFLEDModel

@require_tf
class 						A_							:
				UpperCAmelCase__        =				LEDConfig
				UpperCAmelCase__        =				{}
				UpperCAmelCase__        =				'''gelu'''







				def __init__(							self							:						Optional[Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						List[Any]=1_3						,    __lowerCamelCase							:						str=7						,    __lowerCamelCase							:						Any=True						,    __lowerCamelCase							:						int=False						,    __lowerCamelCase							:						List[Any]=9_9						,    __lowerCamelCase							:						Any=3_2						,    __lowerCamelCase							:						Optional[Any]=2						,    __lowerCamelCase							:						Dict=4						,    __lowerCamelCase							:						int=3_7						,    __lowerCamelCase							:						Tuple=0.1						,    __lowerCamelCase							:						Union[str, Any]=0.1						,    __lowerCamelCase							:						Optional[Any]=2_0						,    __lowerCamelCase							:						List[str]=2						,    __lowerCamelCase							:						str=1						,    __lowerCamelCase							:						List[Any]=0						,    __lowerCamelCase							:						Dict=4						,    )				->					Optional[Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     seq_length
										__magic_name__            =     is_training
										__magic_name__            =     use_labels
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size

										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     eos_token_id
										__magic_name__            =     pad_token_id
										__magic_name__            =     bos_token_id
										__magic_name__            =     attention_window

										# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
										# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
										# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
										# because its local attention only attends to `self.attention_window` and one before and one after
										__magic_name__            =     self.attention_window + 2

										# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
										# the `test_attention_outputs` and `test_hidden_states_output` tests
										__magic_name__            =     (
										    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
										)







				def     _snake_case					(							self							:						Dict	)				->					Optional[Any]:
										__magic_name__            =     ids_tensor([self.batch_size, self.seq_length - 1]						,    self.vocab_size	)
										__magic_name__            =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)						,    1	)
										__magic_name__            =     tf.concat([input_ids, eos_tensor]						,    axis=1	)

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

										__magic_name__            =     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						,    attention_window=self.attention_window						,    **self.config_updates						,    )
										__magic_name__            =     prepare_led_inputs_dict(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     tf.concat(
										    [tf.zeros_like(__lowerCamelCase	)[:, :-1], tf.ones_like(__lowerCamelCase	)[:, -1:]]						,    axis=-1						,    )
										__magic_name__            =     global_attention_mask
										return config, inputs_dict







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Any	)				->					Union[str, Any]:
										__magic_name__            =     TFLEDModel(config=__lowerCamelCase	).get_decoder()
										__magic_name__            =     inputs_dict["input_ids"]

										__magic_name__            =     input_ids[:1, :]
										__magic_name__            =     inputs_dict["attention_mask"][:1, :]
										__magic_name__            =     1

										# first forward pass
										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase						,    use_cache=__lowerCamelCase	)

										__magic_name__      ,    __magic_name__            =     outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										__magic_name__            =     ids_tensor((self.batch_size, 3)						,    config.vocab_size	)
										__magic_name__            =     tf.cast(ids_tensor((self.batch_size, 3)						,    2	)						,    tf.inta	)

										# append to next input_ids and
										__magic_name__            =     tf.concat([input_ids, next_tokens]						,    axis=-1	)
										__magic_name__            =     tf.concat([attention_mask, next_attn_mask]						,    axis=-1	)

										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase	)[0]
										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase						,    past_key_values=__lowerCamelCase	)[0]

										self.parent.assertEqual(next_tokens.shape[1]						,    output_from_past.shape[1]	)

										# select random slice
										__magic_name__            =     int(ids_tensor((1,)						,    output_from_past.shape[-1]	)	)
										__magic_name__            =     output_from_no_past[:, -3:, random_slice_idx]
										__magic_name__            =     output_from_past[:, :, random_slice_idx]

										# test that outputs are equal for slice
										tf.debugging.assert_near(__lowerCamelCase						,    __lowerCamelCase						,    rtol=1e-3	)






def _lowerCAmelCase	(  __lowerCamelCase:str ,						__lowerCamelCase:str ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:Dict=None ,						__lowerCamelCase:List[Any]=None ,						__lowerCamelCase:Union[str, Any]=None ,						):

						'''simple docstring'''






						if attention_mask is None:
												__magic_name__            =     tf.cast(tf.math.not_equal(__lowerCamelCase ,						config.pad_token_id   ) ,						tf.inta   )
						if decoder_attention_mask is None:
												__magic_name__            =     tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape ,						dtype=tf.inta   ),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,						config.pad_token_id   ) ,						tf.inta   ),
												    ] ,						axis=-1 ,						)
						if head_mask is None:
												__magic_name__            =     tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
						if decoder_head_mask is None:
												__magic_name__            =     tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
						return {
						    "input_ids": input_ids,
						    "attention_mask": attention_mask,
						    "decoder_input_ids": decoder_input_ids,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						}

@require_tf
class 						A_							(      snake_case_					,							snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
				UpperCAmelCase__        =				(TFLEDForConditionalGeneration,) if is_tf_available() else ()
				UpperCAmelCase__        =				(
				    {
				        '''conversational''': TFLEDForConditionalGeneration,
				        '''feature-extraction''': TFLEDModel,
				        '''summarization''': TFLEDForConditionalGeneration,
				        '''text2text-generation''': TFLEDForConditionalGeneration,
				        '''translation''': TFLEDForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				UpperCAmelCase__        =				True
				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False







				def     _snake_case					(							self							:						int	)				->					Optional[int]:
										__magic_name__            =     TFLEDModelTester(self	)
										__magic_name__            =     ConfigTester(self						,    config_class=__lowerCamelCase	)







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







				def     _snake_case					(							self							:						Optional[int]	)				->					List[Any]:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]	)				->					str:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()
										__magic_name__            =     tf.zeros_like(inputs_dict["attention_mask"]	)
										__magic_name__            =     2
										__magic_name__            =     tf.where(
										    tf.range(self.model_tester.seq_length	)[None, :] < num_global_attn_indices						,    1						,    inputs_dict["global_attention_mask"]						,    )

										__magic_name__            =     True
										__magic_name__            =     self.model_tester.seq_length
										__magic_name__            =     self.model_tester.encoder_seq_length

										def check_decoder_attentions_output(__lowerCamelCase							:						int	):
																__magic_name__            =     outputs.decoder_attentions
																self.assertEqual(len(__lowerCamelCase	)						,    self.model_tester.num_hidden_layers	)
																self.assertListEqual(
																    list(decoder_attentions[0].shape[-3:]	)						,    [self.model_tester.num_attention_heads, seq_length, seq_length]						,    )

										def check_encoder_attentions_output(__lowerCamelCase							:						Any	):
																__magic_name__            =     [t.numpy() for t in outputs.encoder_attentions]
																__magic_name__            =     [t.numpy() for t in outputs.encoder_global_attentions]
																self.assertEqual(len(__lowerCamelCase	)						,    self.model_tester.num_hidden_layers	)
																self.assertEqual(len(__lowerCamelCase	)						,    self.model_tester.num_hidden_layers	)
																self.assertListEqual(
																    list(attentions[0].shape[-3:]	)						,    [self.model_tester.num_attention_heads, seq_length, seq_length]						,    )
																self.assertListEqual(
																    list(global_attentions[0].shape[-3:]	)						,    [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]						,    )

										for model_class in self.all_model_classes:
																__magic_name__            =     True
																__magic_name__            =     False
																__magic_name__            =     False
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     model(self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)	)
																__magic_name__            =     len(__lowerCamelCase	)
																self.assertEqual(config.output_hidden_states						,    __lowerCamelCase	)
																check_encoder_attentions_output(__lowerCamelCase	)

																if self.is_encoder_decoder:
																						__magic_name__            =     model_class(__lowerCamelCase	)
																						__magic_name__            =     model(self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)	)
																						self.assertEqual(config.output_hidden_states						,    __lowerCamelCase	)
																						check_decoder_attentions_output(__lowerCamelCase	)

																# Check that output attentions can also be changed via the config
																del inputs_dict["output_attentions"]
																__magic_name__            =     True
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     model(self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)	)
																self.assertEqual(config.output_hidden_states						,    __lowerCamelCase	)
																check_encoder_attentions_output(__lowerCamelCase	)

																# Check attention is always last and order is fine
																__magic_name__            =     True
																__magic_name__            =     True
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     model(self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)	)

																self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)						,    len(__lowerCamelCase	)	)
																self.assertEqual(model.config.output_hidden_states						,    __lowerCamelCase	)
																check_encoder_attentions_output(__lowerCamelCase	)







				@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."	)
				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[str]:
										pass







				def     _snake_case					(							self							:						int	)				->					str:
										# TODO: Head-masking not yet implement
										pass






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						return tf.constant(__lowerCamelCase ,						dtype=tf.intaa   )


lowercase    			=       1e-4


@slow
@require_tf
class 						A_							(      unittest.TestCase  ):







				def     _snake_case					(							self							:						Optional[Any]	)				->					List[str]:
										__magic_name__            =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	).led

										# change to intended input here
										__magic_name__            =     _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     prepare_led_inputs_dict(model.config						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     model(**__lowerCamelCase	)[0]
										__magic_name__            =     (1, 1_0_2_4, 7_6_8)
										self.assertEqual(output.shape						,    __lowerCamelCase	)
										# change to expected output here
										__magic_name__            =     tf.convert_to_tensor(
										    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]						,    )
										tf.debugging.assert_near(output[:, :3, :3]						,    __lowerCamelCase						,    atol=1e-3	)







				def     _snake_case					(							self							:						Any	)				->					Dict:
										__magic_name__            =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	)

										# change to intended input here
										__magic_name__            =     _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     prepare_led_inputs_dict(model.config						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     model(**__lowerCamelCase	)[0]
										__magic_name__            =     (1, 1_0_2_4, model.config.vocab_size)
										self.assertEqual(output.shape						,    __lowerCamelCase	)
										# change to expected output here
										__magic_name__            =     tf.convert_to_tensor(
										    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]						,    )
										tf.debugging.assert_near(output[:, :3, :3]						,    __lowerCamelCase						,    atol=1e-3						,    rtol=1e-3	)



