






"""simple docstring"""

import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


lowercase    			=       logging.get_logger(__name__)






def _lowerCAmelCase	(  __lowerCamelCase:Dict ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:Optional[Any]=False   ):

						'''simple docstring'''






						try:
												import torch  # noqa: F401
						except ImportError:
												logger.error(
												    "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
												    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
												    " instructions."   )
												raise

						if not is_sharded:
												__magic_name__            =     os.path.abspath(__lowerCamelCase   )
												logger.info(f'''Loading PyTorch weights from {pt_path}'''   )

												__magic_name__            =     torch.load(__lowerCamelCase ,						map_location="cpu"   )
												logger.info(f'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()   ):,} parameters.'''   )

												__magic_name__            =     convert_pytorch_state_dict_to_flax(__lowerCamelCase ,						__lowerCamelCase   )
						else:
												# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
												__magic_name__            =     convert_pytorch_sharded_state_dict_to_flax(__lowerCamelCase ,						__lowerCamelCase   )
						return flax_state_dict






def _lowerCAmelCase	(  __lowerCamelCase:Tuple[str] ,						__lowerCamelCase:np.ndarray ,						__lowerCamelCase:Dict[str, jnp.ndarray] ,						__lowerCamelCase:str ,						):

						'''simple docstring'''






						def is_key_or_prefix_key_in_dict(__lowerCamelCase:Tuple[str]   ) -> bool:
												return len(set(__lowerCamelCase   ) & {key, (model_prefix,) + key}   ) > 0

						# layer norm
						__magic_name__            =     pt_tuple_key[:-1] + ("scale",)
						if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__lowerCamelCase   ):
												return renamed_pt_tuple_key, pt_tensor

						# batch norm layer mean
						__magic_name__            =     pt_tuple_key[:-1] + ("mean",)
						if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__lowerCamelCase   ):
												return renamed_pt_tuple_key, pt_tensor

						# batch norm layer var
						__magic_name__            =     pt_tuple_key[:-1] + ("var",)
						if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__lowerCamelCase   ):
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						__magic_name__            =     pt_tuple_key[:-1] + ("embedding",)
						if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__lowerCamelCase   ):
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						__magic_name__            =     pt_tuple_key[:-1] + ("kernel",)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__lowerCamelCase   ):
												__magic_name__            =     pt_tensor.transpose(2 ,						3 ,						1 ,						0   )
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						__magic_name__            =     pt_tuple_key[:-1] + ("kernel",)
						if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__lowerCamelCase   ):
												__magic_name__            =     pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						__magic_name__            =     pt_tuple_key[:-1] + ("weight",)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						__magic_name__            =     pt_tuple_key[:-1] + ("bias",)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
						__magic_name__            =     None
						if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
												__magic_name__            =     pt_tuple_key[-2] + "_g"
						elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
												__magic_name__            =     pt_tuple_key[-2] + "_v"
						if name is not None:
												__magic_name__            =     pt_tuple_key[:-3] + (name,)
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor






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

						'''simple docstring'''






						__magic_name__            =     {k: v.numpy() for k, v in pt_state_dict.items()}

						__magic_name__            =     flax_model.base_model_prefix

						# use params dict if the model contains batch norm layers
						if "params" in flax_model.params:
												__magic_name__            =     flax_model.params["params"]
						else:
												__magic_name__            =     flax_model.params
						__magic_name__            =     flatten_dict(__lowerCamelCase   )

						# add batch_stats keys,values to dict
						if "batch_stats" in flax_model.params:
												__magic_name__            =     flatten_dict(flax_model.params["batch_stats"]   )
												random_flax_state_dict.update(__lowerCamelCase   )

						__magic_name__            =     {}

						__magic_name__            =     (model_prefix not in flax_model_params) and (
						    model_prefix in {k.split("."   )[0] for k in pt_state_dict.keys()}
						)
						__magic_name__            =     (model_prefix in flax_model_params) and (
						    model_prefix not in {k.split("."   )[0] for k in pt_state_dict.keys()}
						)

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												__magic_name__            =     tuple(pt_key.split("."   )   )

												# remove base model prefix if necessary
												__magic_name__            =     pt_tuple_key[0] == model_prefix
												if load_model_with_head_into_base_model and has_base_model_prefix:
																		__magic_name__            =     pt_tuple_key[1:]

												# Correctly rename weight parameters
												__magic_name__      ,    __magic_name__            =     rename_key_and_reshape_tensor(
												    __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )

												# add model prefix if necessary
												__magic_name__            =     (model_prefix,) + flax_key in random_flax_state_dict
												if load_base_model_into_model_with_head and require_base_model_prefix:
																		__magic_name__            =     (model_prefix,) + flax_key

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																								    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''   )

        # add batch stats if the model contains batchnorm layers
												if "batch_stats" in flax_model.params:
																		if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																								__magic_name__            =     jnp.asarray(__lowerCamelCase   )
																								continue
																		# remove num_batches_tracked key
																		if "num_batches_tracked" in flax_key[-1]:
																								flax_state_dict.pop(__lowerCamelCase ,						__lowerCamelCase   )
																								continue

																		# also add unexpected weight so that warning is thrown
																		__magic_name__            =     jnp.asarray(__lowerCamelCase   )

												else:
																		# also add unexpected weight so that warning is thrown
																		__magic_name__            =     jnp.asarray(__lowerCamelCase   )

						return unflatten_dict(__lowerCamelCase   )






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

						'''simple docstring'''






						import torch

						# Load the index
						__magic_name__            =     {}
						for shard_file in shard_filenames:
												# load using msgpack utils
												__magic_name__            =     torch.load(__lowerCamelCase   )
												__magic_name__            =     {k: v.numpy() for k, v in pt_state_dict.items()}

												__magic_name__            =     flax_model.base_model_prefix

												# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
												if "batch_stats" in flax_model.params:
																		__magic_name__            =     flax_model.params["params"]

																		__magic_name__            =     flatten_dict(__lowerCamelCase   )
																		random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]   )   )
												else:
																		__magic_name__            =     flax_model.params
																		__magic_name__            =     flatten_dict(__lowerCamelCase   )

												__magic_name__            =     (model_prefix not in flax_model_params) and (
												    model_prefix in {k.split("."   )[0] for k in pt_state_dict.keys()}
												)
												__magic_name__            =     (model_prefix in flax_model_params) and (
												    model_prefix not in {k.split("."   )[0] for k in pt_state_dict.keys()}
												)
												# Need to change some parameters name to match Flax names
												for pt_key, pt_tensor in pt_state_dict.items():
																		__magic_name__            =     tuple(pt_key.split("."   )   )

																		# remove base model prefix if necessary
																		__magic_name__            =     pt_tuple_key[0] == model_prefix
																		if load_model_with_head_into_base_model and has_base_model_prefix:
																								__magic_name__            =     pt_tuple_key[1:]

																		# Correctly rename weight parameters
																		__magic_name__      ,    __magic_name__            =     rename_key_and_reshape_tensor(
																		    __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )
																		# add model prefix if necessary
																		__magic_name__            =     (model_prefix,) + flax_key in random_flax_state_dict
																		if load_base_model_into_model_with_head and require_base_model_prefix:
																								__magic_name__            =     (model_prefix,) + flax_key

																		if flax_key in random_flax_state_dict:
																								if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																														raise ValueError(
																														    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																														    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''   )

            # add batch stats if the model contains batchnorm layers
																		if "batch_stats" in flax_model.params:
																								if "mean" in flax_key[-1]:
																														__magic_name__            =     jnp.asarray(__lowerCamelCase   )
																														continue
																								if "var" in flax_key[-1]:
																														__magic_name__            =     jnp.asarray(__lowerCamelCase   )
																														continue
																								# remove num_batches_tracked key
																								if "num_batches_tracked" in flax_key[-1]:
																														flax_state_dict.pop(__lowerCamelCase ,						__lowerCamelCase   )
																														continue

																								# also add unexpected weight so that warning is thrown
																								__magic_name__            =     jnp.asarray(__lowerCamelCase   )

																		else:
																								# also add unexpected weight so that warning is thrown
																								__magic_name__            =     jnp.asarray(__lowerCamelCase   )
						return unflatten_dict(__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:List[str] ,						__lowerCamelCase:Tuple   ):

						'''simple docstring'''






						__magic_name__            =     os.path.abspath(__lowerCamelCase   )
						logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''   )

						# import correct flax class
						__magic_name__            =     getattr(__lowerCamelCase ,						"Flax" + model.__class__.__name__   )

						# load flax weight dict
						with open(__lowerCamelCase ,						"rb"   ) as state_f:
												try:
																		__magic_name__            =     from_bytes(__lowerCamelCase ,						state_f.read()   )
												except UnpicklingError:
																		raise EnvironmentError(f'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. '''   )

						return load_flax_weights_in_pytorch_model(__lowerCamelCase ,						__lowerCamelCase   )






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

						'''simple docstring'''






						try:
												import torch  # noqa: F401
						except ImportError:
												logger.error(
												    "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
												    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
												    " instructions."   )
												raise

						# check if we have bf16 weights
						__magic_name__            =     flatten_dict(jax.tree_util.tree_map(lambda __lowerCamelCase   : x.dtype == jnp.bfloataa ,						__lowerCamelCase   )   ).values()
						if any(__lowerCamelCase   ):
												# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
												# and bf16 is not fully supported in PT yet.
												logger.warning(
												    "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
												    "before loading those in PyTorch model."   )
												__magic_name__            =     jax.tree_util.tree_map(
												    lambda __lowerCamelCase   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params ,						__lowerCamelCase   )

						__magic_name__            =     flatten_dict(__lowerCamelCase   )
						__magic_name__            =     pt_model.state_dict()

						__magic_name__            =     (pt_model.base_model_prefix in flax_state) and (
						    pt_model.base_model_prefix not in {k.split("."   )[0] for k in pt_model_dict.keys()}
						)
						__magic_name__            =     (pt_model.base_model_prefix not in flax_state) and (
						    pt_model.base_model_prefix in {k.split("."   )[0] for k in pt_model_dict.keys()}
						)

						# keep track of unexpected & missing keys
						__magic_name__            =     []
						__magic_name__            =     set(pt_model_dict.keys()   )

						for flax_key_tuple, flax_tensor in flax_state_dict.items():
												__magic_name__            =     flax_key_tuple[0] == pt_model.base_model_prefix
												__magic_name__            =     ".".join((pt_model.base_model_prefix,) + flax_key_tuple   ) in pt_model_dict

												# adapt flax_key to prepare for loading from/to base model only
												if load_model_with_head_into_base_model and has_base_model_prefix:
																		__magic_name__            =     flax_key_tuple[1:]
												elif load_base_model_into_model_with_head and require_base_model_prefix:
																		__magic_name__            =     (pt_model.base_model_prefix,) + flax_key_tuple

												# rename flax weights to PyTorch format
												if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__lowerCamelCase   ) not in pt_model_dict:
																		# conv layer
																		__magic_name__            =     flax_key_tuple[:-1] + ("weight",)
																		__magic_name__            =     jnp.transpose(__lowerCamelCase ,						(3, 2, 0, 1)   )
												elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCamelCase   ) not in pt_model_dict:
																		# linear layer
																		__magic_name__            =     flax_key_tuple[:-1] + ("weight",)
																		__magic_name__            =     flax_tensor.T
												elif flax_key_tuple[-1] in ["scale", "embedding"]:
																		__magic_name__            =     flax_key_tuple[:-1] + ("weight",)

												# adding batch stats from flax batch norm to pt
												elif "mean" in flax_key_tuple[-1]:
																		__magic_name__            =     flax_key_tuple[:-1] + ("running_mean",)
												elif "var" in flax_key_tuple[-1]:
																		__magic_name__            =     flax_key_tuple[:-1] + ("running_var",)

												if "batch_stats" in flax_state:
																		__magic_name__            =     ".".join(flax_key_tuple[1:]   )  # Remove the params/batch_stats header
												else:
																		__magic_name__            =     ".".join(__lowerCamelCase   )

												# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
												__magic_name__            =     {}
												# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
												for key in pt_model_dict:
																		__magic_name__            =     key.split("."   )
																		__magic_name__            =     None
																		if key_components[-3::2] == ["parametrizations", "original0"]:
																								__magic_name__            =     key_components[-2] + "_g"
																		elif key_components[-3::2] == ["parametrizations", "original1"]:
																								__magic_name__            =     key_components[-2] + "_v"
																		if name is not None:
																								__magic_name__            =     key_components[:-3] + [name]
																								__magic_name__            =     ".".join(__lowerCamelCase   )
																								__magic_name__            =     key

												if flax_key in special_pt_names:
																		__magic_name__            =     special_pt_names[flax_key]

												if flax_key in pt_model_dict:
																		if flax_tensor.shape != pt_model_dict[flax_key].shape:
																								raise ValueError(
																								    f'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
																								    f'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''   )
																		else:
																								# add weight to pytorch dict
																								__magic_name__            =     np.asarray(__lowerCamelCase   ) if not isinstance(__lowerCamelCase ,						np.ndarray   ) else flax_tensor
																								__magic_name__            =     torch.from_numpy(__lowerCamelCase   )
																								# remove from missing keys
																								missing_keys.remove(__lowerCamelCase   )
												else:
																		# weight is not expected by PyTorch model
																		unexpected_keys.append(__lowerCamelCase   )

						pt_model.load_state_dict(__lowerCamelCase   )

						# re-transform missing_keys to list
						__magic_name__            =     list(__lowerCamelCase   )

						if len(__lowerCamelCase   ) > 0:
												logger.warning(
												    "Some weights of the Flax model were not used when initializing the PyTorch model"
												    f''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
												    f''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
												    " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
												    f''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
												    " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
												    " FlaxBertForSequenceClassification model)."   )
						else:
												logger.warning(f'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'''   )
						if len(__lowerCamelCase   ) > 0:
												logger.warning(
												    f'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
												    f''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
												    " use it for predictions and inference."   )
						else:
												logger.warning(
												    f'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
												    "If your task is similar to the task the model of the checkpoint was trained on, "
												    f'''you can already use {pt_model.__class__.__name__} for predictions without further training.'''   )

						return pt_model











"""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	)



