






"""simple docstring"""

from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


lowercase    			=       logging.get_logger(__name__)


# General docstring
lowercase    			=       '''MobileNetV1Config'''

# Base docstring
lowercase    			=       '''google/mobilenet_v1_1.0_224'''
lowercase    			=       [1, 1024, 7, 7]

# Image classification docstring
lowercase    			=       '''google/mobilenet_v1_1.0_224'''
lowercase    			=       '''tabby, tabby cat'''


lowercase    			=       [
    '''google/mobilenet_v1_1.0_224''',
    '''google/mobilenet_v1_0.75_192''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]






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

						'''simple docstring'''






						__magic_name__            =     {}

						if isinstance(__UpperCamelCase ,						__UpperCamelCase   ):
												__magic_name__            =     model.mobilenet_va
						else:
												__magic_name__            =     model

						__magic_name__            =     """MobilenetV1/Conv2d_0/"""
						__magic_name__            =     backbone.conv_stem.convolution.weight
						__magic_name__            =     backbone.conv_stem.normalization.bias
						__magic_name__            =     backbone.conv_stem.normalization.weight
						__magic_name__            =     backbone.conv_stem.normalization.running_mean
						__magic_name__            =     backbone.conv_stem.normalization.running_var

						for i in range(1_3   ):
												__magic_name__            =     i + 1
												__magic_name__            =     i * 2

												__magic_name__            =     backbone.layer[pt_index]
												__magic_name__            =     f'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
												__magic_name__            =     pointer.convolution.weight
												__magic_name__            =     pointer.normalization.bias
												__magic_name__            =     pointer.normalization.weight
												__magic_name__            =     pointer.normalization.running_mean
												__magic_name__            =     pointer.normalization.running_var

												__magic_name__            =     backbone.layer[pt_index + 1]
												__magic_name__            =     f'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
												__magic_name__            =     pointer.convolution.weight
												__magic_name__            =     pointer.normalization.bias
												__magic_name__            =     pointer.normalization.weight
												__magic_name__            =     pointer.normalization.running_mean
												__magic_name__            =     pointer.normalization.running_var

						if isinstance(__UpperCamelCase ,						__UpperCamelCase   ):
												__magic_name__            =     """MobilenetV1/Logits/Conv2d_1c_1x1/"""
												__magic_name__            =     model.classifier.weight
												__magic_name__            =     model.classifier.bias

						return tf_to_pt_map






def _lowerCAmelCase	(  __lowerCamelCase:List[str] ,						__lowerCamelCase:Any ,						__lowerCamelCase:Union[str, Any]   ):

						'''simple docstring'''






						try:
												import numpy as np
												import tensorflow as tf
						except ImportError:
												logger.error(
												    "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
												    "https://www.tensorflow.org/install/ for installation instructions."   )
												raise

						# Load weights from TF model
						__magic_name__            =     tf.train.list_variables(__UpperCamelCase   )
						__magic_name__            =     {}
						for name, shape in init_vars:
												logger.info(f'''Loading TF weight {name} with shape {shape}'''   )
												__magic_name__            =     tf.train.load_variable(__UpperCamelCase ,						__UpperCamelCase   )
												__magic_name__            =     array

						# Build TF to PyTorch weights loading map
						__magic_name__            =     _build_tf_to_pytorch_map(__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase   )

						for name, pointer in tf_to_pt_map.items():
												logger.info(f'''Importing {name}'''   )
												if name not in tf_weights:
																		logger.info(f'''{name} not in tf pre-trained weights, skipping'''   )
																		continue

												__magic_name__            =     tf_weights[name]

												if "depthwise_weights" in name:
																		logger.info("Transposing depthwise"   )
																		__magic_name__            =     np.transpose(__UpperCamelCase ,						(2, 3, 0, 1)   )
												elif "weights" in name:
																		logger.info("Transposing"   )
																		if len(pointer.shape   ) == 2:  # copying into linear layer
																								__magic_name__            =     array.squeeze().transpose()
																		else:
																								__magic_name__            =     np.transpose(__UpperCamelCase ,						(3, 2, 0, 1)   )

												if pointer.shape != array.shape:
																		raise ValueError(f'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched'''   )

												logger.info(f'''Initialize PyTorch weight {name} {array.shape}'''   )
												__magic_name__            =     torch.from_numpy(__UpperCamelCase   )

												tf_weights.pop(__UpperCamelCase ,						__UpperCamelCase   )
												tf_weights.pop(name + "/RMSProp" ,						__UpperCamelCase   )
												tf_weights.pop(name + "/RMSProp_1" ,						__UpperCamelCase   )
												tf_weights.pop(name + "/ExponentialMovingAverage" ,						__UpperCamelCase   )

						logger.info(f'''Weights not copied to PyTorch model: {", ".join(tf_weights.keys()   )}'''   )
						return model






def _lowerCAmelCase	(  __lowerCamelCase:torch.Tensor ,						__lowerCamelCase:nn.Convad   ):

						'''simple docstring'''






						__magic_name__            =     features.shape[-2:]
						__magic_name__            =     conv_layer.stride
						__magic_name__            =     conv_layer.kernel_size

						if in_height % stride_height == 0:
												__magic_name__            =     max(kernel_height - stride_height ,						0   )
						else:
												__magic_name__            =     max(kernel_height - (in_height % stride_height) ,						0   )

						if in_width % stride_width == 0:
												__magic_name__            =     max(kernel_width - stride_width ,						0   )
						else:
												__magic_name__            =     max(kernel_width - (in_width % stride_width) ,						0   )

						__magic_name__            =     pad_along_width // 2
						__magic_name__            =     pad_along_width - pad_left
						__magic_name__            =     pad_along_height // 2
						__magic_name__            =     pad_along_height - pad_top

						__magic_name__            =     (pad_left, pad_right, pad_top, pad_bottom)
						return nn.functional.pad(__UpperCamelCase ,						__UpperCamelCase ,						"constant" ,						0.0   )

class 						A_							(      nn.Module  ):







				def __init__(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						Optional[int] = 1						,    __lowerCamelCase							:						int = 1						,    __lowerCamelCase							:						int = False						,    __lowerCamelCase							:						int = True						,    __lowerCamelCase							:						Optional[int] = True						,    )				->					None:
										super().__init__()
										__magic_name__            =     config

										if in_channels % groups != 0:
																raise ValueError(f'''Input channels ({in_channels}) are not divisible by {groups} groups.'''	)
										if out_channels % groups != 0:
																raise ValueError(f'''Output channels ({out_channels}) are not divisible by {groups} groups.'''	)

										__magic_name__            =     0 if config.tf_padding else int((kernel_size - 1) / 2	)

										__magic_name__            =     nn.Convad(
										    in_channels=_lowercase						,    out_channels=_lowercase						,    kernel_size=_lowercase						,    stride=_lowercase						,    padding=_lowercase						,    groups=_lowercase						,    bias=_lowercase						,    padding_mode="zeros"						,    )

										if use_normalization:
																__magic_name__            =     nn.BatchNormad(
																    num_features=_lowercase						,    eps=config.layer_norm_eps						,    momentum=0.9997						,    affine=_lowercase						,    track_running_stats=_lowercase						,    )
										else:
																__magic_name__            =     None

										if use_activation:
																if isinstance(_lowercase						,    _lowercase	):
																						__magic_name__            =     ACTaFN[use_activation]
																elif isinstance(config.hidden_act						,    _lowercase	):
																						__magic_name__            =     ACTaFN[config.hidden_act]
																else:
																						__magic_name__            =     config.hidden_act
										else:
																__magic_name__            =     None







				def     _snake_case					(							self							:						List[Any]						,    __lowerCamelCase							:						Union[str, Any]	)				->					torch.Tensor:

										if self.config.tf_padding:
																__magic_name__            =     apply_tf_padding(_lowercase						,    self.convolution	)
										__magic_name__            =     self.convolution(_lowercase	)
										if self.normalization is not None:
																__magic_name__            =     self.normalization(_lowercase	)
										if self.activation is not None:
																__magic_name__            =     self.activation(_lowercase	)
										return features





class 						A_							(      SCREAMING_SNAKE_CASE__  ):
				UpperCAmelCase__        =				MobileNetVaConfig
				UpperCAmelCase__        =				load_tf_weights_in_mobilenet_va
				UpperCAmelCase__        =				'''mobilenet_v1'''
				UpperCAmelCase__        =				'''pixel_values'''
				UpperCAmelCase__        =				False







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						int	)				->					None:

										if isinstance(_lowercase						,    (nn.Linear, nn.Convad)	):
																module.weight.data.normal_(mean=0.0						,    std=self.config.initializer_range	)
																if module.bias is not None:
																						module.bias.data.zero_()
										elif isinstance(_lowercase						,    nn.BatchNormad	):
																module.bias.data.zero_()
																module.weight.data.fill_(1.0	)





lowercase    			=       R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): 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. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. 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(
    '''The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'''					,							SCREAMING_SNAKE_CASE__					,							)
class 						A_							(      SCREAMING_SNAKE_CASE__  ):







				def __init__(							self							:						Dict						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						Union[str, Any] = True	)				->					Tuple:
										super().__init__(_lowercase	)
										__magic_name__            =     config

										__magic_name__            =     3_2
										__magic_name__            =     max(int(depth * config.depth_multiplier	)						,    config.min_depth	)

										__magic_name__            =     MobileNetVaConvLayer(
										    _lowercase						,    in_channels=config.num_channels						,    out_channels=_lowercase						,    kernel_size=3						,    stride=2						,    )

										__magic_name__            =     [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

										__magic_name__            =     nn.ModuleList()
										for i in range(1_3	):
																__magic_name__            =     out_channels

																if strides[i] == 2 or i == 0:
																						depth *= 2
																						__magic_name__            =     max(int(depth * config.depth_multiplier	)						,    config.min_depth	)

																self.layer.append(
																    MobileNetVaConvLayer(
																        _lowercase						,    in_channels=_lowercase						,    out_channels=_lowercase						,    kernel_size=3						,    stride=strides[i]						,    groups=_lowercase						,    )	)

																self.layer.append(
																    MobileNetVaConvLayer(
																        _lowercase						,    in_channels=_lowercase						,    out_channels=_lowercase						,    kernel_size=1						,    )	)

										__magic_name__            =     nn.AdaptiveAvgPoolad((1, 1)	) if add_pooling_layer else None

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







				def     _snake_case					(							self							:						List[Any]						,    __lowerCamelCase							:						List[str]	)				->					Optional[Any]:
										raise NotImplementedError







				@add_start_docstrings_to_model_forward(_lowercase	)
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC						,    output_type=_lowercase						,    config_class=_CONFIG_FOR_DOC						,    modality="vision"						,    expected_output=_EXPECTED_OUTPUT_SHAPE						,    )
				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						Optional[Any] = None						,    __lowerCamelCase							:						Tuple = None						,    __lowerCamelCase							:						str = None						,    )				->					Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
										__magic_name__            =     (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										__magic_name__            =     return_dict if return_dict is not None else self.config.use_return_dict

										if pixel_values is None:
																raise ValueError("You have to specify pixel_values"	)

										__magic_name__            =     self.conv_stem(_lowercase	)

										__magic_name__            =     () if output_hidden_states else None

										for i, layer_module in enumerate(self.layer	):
																__magic_name__            =     layer_module(_lowercase	)

																if output_hidden_states:
																						__magic_name__            =     all_hidden_states + (hidden_states,)

										__magic_name__            =     hidden_states

										if self.pooler is not None:
																__magic_name__            =     torch.flatten(self.pooler(_lowercase	)						,    start_dim=1	)
										else:
																__magic_name__            =     None

										if not return_dict:
																return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None	)

										return BaseModelOutputWithPoolingAndNoAttention(
										    last_hidden_state=_lowercase						,    pooler_output=_lowercase						,    hidden_states=_lowercase						,    )





@add_start_docstrings(
    '''
    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''					,							SCREAMING_SNAKE_CASE__					,							)
class 						A_							(      SCREAMING_SNAKE_CASE__  ):







				def __init__(							self							:						List[str]						,    __lowerCamelCase							:						List[Any]	)				->					None:
										super().__init__(_lowercase	)

										__magic_name__            =     config.num_labels
										__magic_name__            =     MobileNetVaModel(_lowercase	)

										__magic_name__            =     self.mobilenet_va.layer[-1].convolution.out_channels

										# Classifier head
										__magic_name__            =     nn.Dropout(config.classifier_dropout_prob						,    inplace=_lowercase	)
										__magic_name__            =     nn.Linear(_lowercase						,    config.num_labels	) if config.num_labels > 0 else nn.Identity()

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







				@add_start_docstrings_to_model_forward(_lowercase	)
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT						,    output_type=_lowercase						,    config_class=_CONFIG_FOR_DOC						,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,    )
				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						Dict = None						,    __lowerCamelCase							:						List[Any] = None						,    __lowerCamelCase							:						Union[str, Any] = None						,    __lowerCamelCase							:						Optional[Any] = None						,    )				->					Union[tuple, ImageClassifierOutputWithNoAttention]:
										__magic_name__            =     return_dict if return_dict is not None else self.config.use_return_dict

										__magic_name__            =     self.mobilenet_va(_lowercase						,    output_hidden_states=_lowercase						,    return_dict=_lowercase	)

										__magic_name__            =     outputs.pooler_output if return_dict else outputs[1]

										__magic_name__            =     self.classifier(self.dropout(_lowercase	)	)

										__magic_name__            =     None
										if labels is not None:
																if self.config.problem_type is None:
																						if self.num_labels == 1:
																												__magic_name__            =     """regression"""
																						elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																												__magic_name__            =     """single_label_classification"""
																						else:
																												__magic_name__            =     """multi_label_classification"""

																if self.config.problem_type == "regression":
																						__magic_name__            =     MSELoss()
																						if self.num_labels == 1:
																												__magic_name__            =     loss_fct(logits.squeeze()						,    labels.squeeze()	)
																						else:
																												__magic_name__            =     loss_fct(_lowercase						,    _lowercase	)
																elif self.config.problem_type == "single_label_classification":
																						__magic_name__            =     CrossEntropyLoss()
																						__magic_name__            =     loss_fct(logits.view(-1						,    self.num_labels	)						,    labels.view(-1	)	)
																elif self.config.problem_type == "multi_label_classification":
																						__magic_name__            =     BCEWithLogitsLoss()
																						__magic_name__            =     loss_fct(_lowercase						,    _lowercase	)

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

										return ImageClassifierOutputWithNoAttention(
										    loss=_lowercase						,    logits=_lowercase						,    hidden_states=outputs.hidden_states						,    )











"""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	)



