



import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
						from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder


def 				snake_case_  (      _lowerCAmelCase						:		Dict     ,   _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		Optional[Any]=None     ,   _lowerCAmelCase						:		Union[str, Any]=None     ,   _lowerCAmelCase						:		Any=None     ,   _lowerCAmelCase						:		Tuple=None     ,   _lowerCAmelCase						:		Dict=None     ,   )   ->     Optional[int]:
							if attention_mask is None:
														UpperCAmelCase				: Tuple											=	input_ids.ne(config.pad_token_id						)
							if decoder_attention_mask is None:
														UpperCAmelCase				: Any											=	decoder_input_ids.ne(config.pad_token_id						)
							if head_mask is None:
														UpperCAmelCase				: Optional[int]											=	torch.ones(config.encoder_layers     ,   config.encoder_attention_heads     ,   device=_lowerCAmelCase						)
							if decoder_head_mask is None:
														UpperCAmelCase				: str											=	torch.ones(config.decoder_layers     ,   config.decoder_attention_heads     ,   device=_lowerCAmelCase						)
							if cross_attn_head_mask is None:
														UpperCAmelCase				: Tuple											=	torch.ones(config.decoder_layers     ,   config.decoder_attention_heads     ,   device=_lowerCAmelCase						)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  Optional[Any]	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  List[Any]=13	,    __snake_case    :  int=7	,    __snake_case    :  Union[str, Any]=True	,    __snake_case    :  str=False	,    __snake_case    :  Optional[Any]=99	,    __snake_case    :  Tuple=16	,    __snake_case    :  Any=2	,    __snake_case    :  List[Any]=4	,    __snake_case    :  str=4	,    __snake_case    :  int="relu"	,    __snake_case    :  Union[str, Any]=0.1	,    __snake_case    :  List[Any]=0.1	,    __snake_case    :  Any=0.0	,    __snake_case    :  Any=0.0	,    __snake_case    :  Optional[int]=20	,    __snake_case    :  Tuple=2	,    __snake_case    :  Tuple=1	,    __snake_case    :  str=0	,    )    ->	List[str]:
								UpperCAmelCase				: int											=	parent
								UpperCAmelCase				: Any											=	batch_size
								UpperCAmelCase				: int											=	seq_length
								UpperCAmelCase				: Any											=	is_training
								UpperCAmelCase				: List[Any]											=	use_labels
								UpperCAmelCase				: Union[str, Any]											=	vocab_size
								UpperCAmelCase				: List[str]											=	hidden_size
								UpperCAmelCase				: Dict											=	num_hidden_layers
								UpperCAmelCase				: Optional[Any]											=	num_attention_heads
								UpperCAmelCase				: str											=	intermediate_size
								UpperCAmelCase				: List[str]											=	hidden_act
								UpperCAmelCase				: str											=	hidden_dropout_prob
								UpperCAmelCase				: int											=	attention_probs_dropout_prob
								UpperCAmelCase				: Tuple											=	encoder_layerdrop
								UpperCAmelCase				: int											=	decoder_layerdrop
								UpperCAmelCase				: List[Any]											=	max_position_embeddings
								UpperCAmelCase				: List[str]											=	eos_token_id
								UpperCAmelCase				: str											=	pad_token_id
								UpperCAmelCase				: Optional[int]											=	bos_token_id







	def   A			(		self    :  Tuple	)    ->	Union[str, Any]:
								UpperCAmelCase				: str											=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size	)
								UpperCAmelCase				: Tuple											=	self.eos_token_id  # Eos Token
								UpperCAmelCase				: Dict											=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size	)

								# we need to clamp the input ids here to avoid having pad token in between
								# this is because for M2M100 the position_ids are prepared such that
								# all pad tokens have pos id = 2 and rest are between 2..seq_length
								# and the seq_length here is seq_length - num_pad_tokens
								# but when using past, there is no way of knowing if the past input ids had
								# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
								# position_ids being off by num_pad_tokens in past input
								UpperCAmelCase				: Union[str, Any]											=	input_ids.clamp(self.pad_token_id + 1	)
								UpperCAmelCase				: Any											=	decoder_input_ids.clamp(self.pad_token_id + 1	)

								UpperCAmelCase				: str											=	self.get_config()
								UpperCAmelCase				: Optional[int]											=	prepare_mam_aaa_inputs_dict(A__	,    A__	,    A__	)
								return config, inputs_dict







	def   A			(		self    :  Union[str, Any]	)    ->	int:
								return MaMaaaConfig(
								    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	,    encoder_layerdrop=self.encoder_layerdrop	,    decoder_layerdrop=self.decoder_layerdrop	,    max_position_embeddings=self.max_position_embeddings	,    eos_token_id=self.eos_token_id	,    bos_token_id=self.bos_token_id	,    pad_token_id=self.pad_token_id	,    )







	def   A			(		self    :  Tuple	)    ->	Union[str, Any]:
								UpperCAmelCase							,	UpperCAmelCase				: Optional[Any]											=	self.prepare_config_and_inputs()
								return config, inputs_dict







	def   A			(		self    :  int	,    __snake_case    :  int	,    __snake_case    :  Any	)    ->	List[Any]:
								UpperCAmelCase				: Tuple											=	MaMaaaModel(config=A__	).get_decoder().to(A__	).eval()
								UpperCAmelCase				: List[str]											=	inputs_dict['''input_ids''']
								UpperCAmelCase				: int											=	inputs_dict['''attention_mask''']
								UpperCAmelCase				: str											=	inputs_dict['''head_mask''']

								# first forward pass
								UpperCAmelCase				: Tuple											=	model(A__	,    attention_mask=A__	,    head_mask=A__	,    use_cache=A__	)

								UpperCAmelCase							,	UpperCAmelCase				: Tuple											=	outputs.to_tuple()

								# create hypothetical multiple next token and extent to next_input_ids
								UpperCAmelCase				: List[Any]											=	ids_tensor((self.batch_size, 3)	,    config.vocab_size	)
								UpperCAmelCase				: Optional[Any]											=	ids_tensor((self.batch_size, 3)	,    2	)

								# append to next input_ids and
								UpperCAmelCase				: Any											=	torch.cat([input_ids, next_tokens]	,    dim=-1	)
								UpperCAmelCase				: Tuple											=	torch.cat([attention_mask, next_attn_mask]	,    dim=-1	)

								UpperCAmelCase				: List[Any]											=	model(A__	,    attention_mask=A__	)['''last_hidden_state''']
								UpperCAmelCase				: Optional[Any]											=	model(A__	,    attention_mask=A__	,    past_key_values=A__	)[
								    '''last_hidden_state'''
								]

								# select random slice
								UpperCAmelCase				: int											=	ids_tensor((1,)	,    output_from_past.shape[-1]	).item()
								UpperCAmelCase				: Optional[int]											=	output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCAmelCase				: Any											=	output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(A__	,    A__	,    atol=1E-2	)	)







	def   A			(		self    :  str	,    __snake_case    :  int	,    __snake_case    :  Union[str, Any]	)    ->	str:
								UpperCAmelCase				: List[Any]											=	MaMaaaModel(config=A__	).to(A__	).eval()
								UpperCAmelCase				: Union[str, Any]											=	model(**A__	)

								UpperCAmelCase				: Dict											=	outputs.encoder_last_hidden_state
								UpperCAmelCase				: int											=	outputs.last_hidden_state

								with tempfile.TemporaryDirectory() as tmpdirname:
															UpperCAmelCase				: List[Any]											=	model.get_encoder()
															encoder.save_pretrained(A__	)
															UpperCAmelCase				: List[str]											=	MaMaaaEncoder.from_pretrained(A__	).to(A__	)

								UpperCAmelCase				: Union[str, Any]											=	encoder(inputs_dict['''input_ids''']	,    attention_mask=inputs_dict['''attention_mask''']	)[
								    0
								]

								self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3	)

								with tempfile.TemporaryDirectory() as tmpdirname:
															UpperCAmelCase				: Dict											=	model.get_decoder()
															decoder.save_pretrained(A__	)
															UpperCAmelCase				: Union[str, Any]											=	MaMaaaDecoder.from_pretrained(A__	).to(A__	)

								UpperCAmelCase				: List[Any]											=	decoder(
								    input_ids=inputs_dict['''decoder_input_ids''']	,    attention_mask=inputs_dict['''decoder_attention_mask''']	,    encoder_hidden_states=A__	,    encoder_attention_mask=inputs_dict['''attention_mask''']	,    )[0]

								self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3	)

@require_torch
class        SCREAMING_SNAKE_CASE(							__a							,	__a							,	__a							,	unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= (
	    (
	        MaMaaaModel,
	        MaMaaaForConditionalGeneration,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCamelCase__    				= (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
	lowerCamelCase__    				= (
	    {
	        """conversational""": MaMaaaForConditionalGeneration,
	        """feature-extraction""": MaMaaaModel,
	        """summarization""": MaMaaaForConditionalGeneration,
	        """text2text-generation""": MaMaaaForConditionalGeneration,
	        """translation""": MaMaaaForConditionalGeneration,
	    }
	    if is_torch_available()
	    else {}
	)
	lowerCamelCase__    				= True
	lowerCamelCase__    				= True
	lowerCamelCase__    				= False
	lowerCamelCase__    				= False







	def   A			(		self    :  Optional[int]	,    __snake_case    :  str	,    __snake_case    :  List[Any]	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  str	,    __snake_case    :  Optional[Any]	)    ->	Any:
								if pipeline_test_casse_name == "TranslationPipelineTests":
															# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
															# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
															return True

								return False







	def   A			(		self    :  List[str]	)    ->	List[Any]:
								UpperCAmelCase				: Any											=	MaMaaaModelTester(self	)
								UpperCAmelCase				: Dict											=	ConfigTester(self	,    config_class=A__	)







	def   A			(		self    :  Tuple	)    ->	Tuple:
								self.config_tester.run_common_tests()







	def   A			(		self    :  str	)    ->	str:
								UpperCAmelCase							,	UpperCAmelCase				: List[Any]											=	self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
															UpperCAmelCase				: List[Any]											=	model_class(A__	)

															with tempfile.TemporaryDirectory() as tmpdirname:
																						model.save_pretrained(A__	)
																						UpperCAmelCase							,	UpperCAmelCase				: List[str]											=	model_class.from_pretrained(A__	,    output_loading_info=A__	)
															self.assertEqual(info['''missing_keys''']	,    []	)







	def   A			(		self    :  Tuple	)    ->	str:
								UpperCAmelCase				: int											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(*A__	)







	def   A			(		self    :  int	)    ->	Union[str, Any]:
								UpperCAmelCase				: Union[str, Any]											=	self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_encoder_decoder_model_standalone(*A__	)







	def   A			(		self    :  List[str]	)    ->	List[Any]:
								UpperCAmelCase							,	UpperCAmelCase				: Dict											=	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
															UpperCAmelCase				: Optional[int]											=	model_class(A__	)
															model.to(A__	)
															model.eval()

															UpperCAmelCase				: Optional[int]											=	copy.deepcopy(self._prepare_for_class(A__	,    A__	)	)

															if not self.is_encoder_decoder:
																						UpperCAmelCase				: Tuple											=	inputs['''input_ids''']
																						del inputs["input_ids"]
															else:
																						UpperCAmelCase				: Optional[int]											=	inputs['''input_ids''']
																						UpperCAmelCase				: Dict											=	inputs.get('''decoder_input_ids'''	,    A__	)
																						del inputs["input_ids"]
																						inputs.pop('''decoder_input_ids'''	,    A__	)

															UpperCAmelCase				: Tuple											=	model.get_input_embeddings()
															if not self.is_encoder_decoder:
																						UpperCAmelCase				: int											=	wte(A__	)
															else:
																						UpperCAmelCase				: str											=	wte(A__	)
																						UpperCAmelCase				: List[Any]											=	wte(A__	)

															with torch.no_grad():
																						model(**A__	)[0]







	def   A			(		self    :  Any	)    ->	int:
								UpperCAmelCase							,	UpperCAmelCase				: str											=	self.model_tester.prepare_config_and_inputs()
								UpperCAmelCase				: Optional[Any]											=	input_dict['''input_ids''']
								UpperCAmelCase				: Union[str, Any]											=	input_ids.ne(1	).to(A__	)
								UpperCAmelCase				: Tuple											=	MaMaaaForConditionalGeneration(A__	).eval().to(A__	)
								if torch_device == "cuda":
															model.half()
								model.generate(A__	,    attention_mask=A__	)
								model.generate(num_beams=4	,    do_sample=A__	,    early_stopping=A__	,    num_return_sequences=3	)


def 				snake_case_  (      _lowerCAmelCase						:		List[str]						)   ->     Any:
							return torch.tensor(_lowerCAmelCase     ,   dtype=torch.long     ,   device=_lowerCAmelCase						)


UpperCamelCase__:     Optional[int]										=    1E-4




@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	@cached_property
	def   A			(		self    :  Optional[Any]	)    ->	Union[str, Any]:
								return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''	)







	def   A			(		self    :  str	)    ->	Optional[Any]:
								UpperCAmelCase				: str											=	MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''	).to(A__	)
								UpperCAmelCase				: str											=	_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]	)
								UpperCAmelCase				: List[str]											=	_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]	)
								UpperCAmelCase				: List[str]											=	prepare_mam_aaa_inputs_dict(model.config	,    A__	,    A__	)
								with torch.no_grad():
															UpperCAmelCase				: Any											=	model(**A__	)[0]
								UpperCAmelCase				: Optional[int]											=	torch.Size((1, 11, 1024)	)
								self.assertEqual(output.shape	,    A__	)
								# change to expected output here
								UpperCAmelCase				: Any											=	torch.tensor(
								    [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]]	,    device=A__	)
								self.assertTrue(torch.allclose(output[:, :3, :3]	,    A__	,    atol=A__	)	)







	def   A			(		self    :  Optional[int]	)    ->	Optional[Any]:
								UpperCAmelCase				: Union[str, Any]											=	MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''	).to(A__	)

								# change to intended input
								UpperCAmelCase				: int											=	_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]	)
								UpperCAmelCase				: Optional[Any]											=	_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]	)
								UpperCAmelCase				: Tuple											=	prepare_mam_aaa_inputs_dict(model.config	,    A__	,    A__	)
								with torch.no_grad():
															UpperCAmelCase				: Optional[int]											=	model(**A__	)[0]
								UpperCAmelCase				: Optional[Any]											=	torch.Size((1, 11, model.config.vocab_size)	)
								self.assertEqual(output.shape	,    A__	)
								# change to expected output here
								UpperCAmelCase				: str											=	torch.tensor(
								    [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]]	,    device=A__	)
								self.assertTrue(torch.allclose(output[:, :3, :3]	,    A__	,    atol=A__	)	)







	def   A			(		self    :  Optional[Any]	)    ->	Tuple:
								UpperCAmelCase				: List[Any]											=	MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''	).to(A__	)
								UpperCAmelCase				: List[Any]											=	MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''	,    src_lang='''fr'''	,    tgt_lang='''en'''	)

								UpperCAmelCase				: str											=	[
								    '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
								    '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
								    '''Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'''
								    ''' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'''
								    ''' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.''',
								]

								# The below article tests that we don't add any hypotheses outside of the top n_beams
								UpperCAmelCase				: Optional[Any]											=	tokenizer(A__	,    padding=A__	,    return_tensors='''pt'''	)

								UpperCAmelCase				: Optional[int]											=	model.generate(
								    input_ids=dct['''input_ids'''].to(A__	)	,    attention_mask=dct['''attention_mask'''].to(A__	)	,    num_beams=5	,    forced_bos_token_id=tokenizer.get_lang_id('''en'''	)	,    )

								UpperCAmelCase				: int											=	[
								    '''The NSA case highlights the total absence of intelligence debate''',
								    '''I think there are two levels of response from the French government.''',
								    '''When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'''
								    ''' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'''
								    ''' communications in France.''',
								]

								UpperCAmelCase				: List[Any]											=	tokenizer.batch_decode(
								    hypotheses_batch.tolist()	,    clean_up_tokenization_spaces=A__	,    skip_special_tokens=A__	)
								assert generated == expected_en










'''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        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	lowerCamelCase__    				= LEDConfig
	lowerCamelCase__    				= {}
	lowerCamelCase__    				= """gelu"""







	def __init__(		self    :  List[str]	,    __snake_case    :  int	,    __snake_case    :  Dict=13	,    __snake_case    :  Dict=7	,    __snake_case    :  Union[str, Any]=True	,    __snake_case    :  Dict=False	,    __snake_case    :  List[str]=99	,    __snake_case    :  str=32	,    __snake_case    :  Optional[int]=2	,    __snake_case    :  int=4	,    __snake_case    :  str=37	,    __snake_case    :  Optional[int]=0.1	,    __snake_case    :  Any=0.1	,    __snake_case    :  int=20	,    __snake_case    :  List[str]=2	,    __snake_case    :  List[str]=1	,    __snake_case    :  str=0	,    __snake_case    :  List[str]=4	,    )    ->	Optional[Any]:
								UpperCAmelCase				: List[str]											=	parent
								UpperCAmelCase				: Optional[int]											=	batch_size
								UpperCAmelCase				: int											=	seq_length
								UpperCAmelCase				: Optional[Any]											=	is_training
								UpperCAmelCase				: List[Any]											=	use_labels
								UpperCAmelCase				: int											=	vocab_size
								UpperCAmelCase				: Optional[int]											=	hidden_size
								UpperCAmelCase				: Union[str, Any]											=	num_hidden_layers
								UpperCAmelCase				: str											=	num_attention_heads
								UpperCAmelCase				: Optional[int]											=	intermediate_size

								UpperCAmelCase				: Any											=	hidden_dropout_prob
								UpperCAmelCase				: int											=	attention_probs_dropout_prob
								UpperCAmelCase				: Union[str, Any]											=	max_position_embeddings
								UpperCAmelCase				: List[Any]											=	eos_token_id
								UpperCAmelCase				: Dict											=	pad_token_id
								UpperCAmelCase				: List[str]											=	bos_token_id
								UpperCAmelCase				: Tuple											=	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
								UpperCAmelCase				: Optional[int]											=	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
								UpperCAmelCase				: Union[str, Any]											=	(
								    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
								)







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

								UpperCAmelCase				: Tuple											=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size	)

								UpperCAmelCase				: int											=	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	,    )
								UpperCAmelCase				: Optional[Any]											=	prepare_led_inputs_dict(__snake_case	,    __snake_case	,    __snake_case	)
								UpperCAmelCase				: Any											=	tf.concat(
								    [tf.zeros_like(__snake_case	)[:, :-1], tf.ones_like(__snake_case	)[:, -1:]]	,    axis=-1	,    )
								UpperCAmelCase				: Any											=	global_attention_mask
								return config, inputs_dict







	def   A			(		self    :  Optional[int]	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  List[Any]	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	TFLEDModel(config=__snake_case	).get_decoder()
								UpperCAmelCase				: List[str]											=	inputs_dict['''input_ids''']

								UpperCAmelCase				: Any											=	input_ids[:1, :]
								UpperCAmelCase				: Tuple											=	inputs_dict['''attention_mask'''][:1, :]
								UpperCAmelCase				: str											=	1

								# first forward pass
								UpperCAmelCase				: Dict											=	model(__snake_case	,    attention_mask=__snake_case	,    use_cache=__snake_case	)

								UpperCAmelCase							,	UpperCAmelCase				: str											=	outputs.to_tuple()

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

								# append to next input_ids and
								UpperCAmelCase				: int											=	tf.concat([input_ids, next_tokens]	,    axis=-1	)
								UpperCAmelCase				: List[Any]											=	tf.concat([attention_mask, next_attn_mask]	,    axis=-1	)

								UpperCAmelCase				: Optional[Any]											=	model(__snake_case	,    attention_mask=__snake_case	)[0]
								UpperCAmelCase				: List[str]											=	model(__snake_case	,    attention_mask=__snake_case	,    past_key_values=__snake_case	)[0]

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

								# select random slice
								UpperCAmelCase				: Dict											=	int(ids_tensor((1,)	,    output_from_past.shape[-1]	)	)
								UpperCAmelCase				: Optional[Any]											=	output_from_no_past[:, -3:, random_slice_idx]
								UpperCAmelCase				: Tuple											=	output_from_past[:, :, random_slice_idx]

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


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		List[Any]=None     ,   _lowerCAmelCase						:		List[str]=None     ,   _lowerCAmelCase						:		Tuple=None     ,   _lowerCAmelCase						:		Dict=None     ,   )   ->     Optional[int]:
							if attention_mask is None:
														UpperCAmelCase				: Dict											=	tf.cast(tf.math.not_equal(_lowerCAmelCase     ,   config.pad_token_id						)     ,   tf.inta						)
							if decoder_attention_mask is None:
														UpperCAmelCase				: int											=	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:
														UpperCAmelCase				: Tuple											=	tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
							if decoder_head_mask is None:
														UpperCAmelCase				: List[str]											=	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        SCREAMING_SNAKE_CASE(							A__							,	A__							,	unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
	lowerCamelCase__    				= (TFLEDForConditionalGeneration,) if is_tf_available() else ()
	lowerCamelCase__    				= (
	    {
	        """conversational""": TFLEDForConditionalGeneration,
	        """feature-extraction""": TFLEDModel,
	        """summarization""": TFLEDForConditionalGeneration,
	        """text2text-generation""": TFLEDForConditionalGeneration,
	        """translation""": TFLEDForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	lowerCamelCase__    				= True
	lowerCamelCase__    				= False
	lowerCamelCase__    				= False
	lowerCamelCase__    				= False







	def   A			(		self    :  str	)    ->	Tuple:
								UpperCAmelCase				: Optional[Any]											=	TFLEDModelTester(self	)
								UpperCAmelCase				: Tuple											=	ConfigTester(self	,    config_class=__snake_case	)







	def   A			(		self    :  Any	)    ->	Tuple:
								self.config_tester.run_common_tests()







	def   A			(		self    :  int	)    ->	List[str]:
								UpperCAmelCase				: Tuple											=	self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*__snake_case	)







	def   A			(		self    :  Any	)    ->	List[Any]:
								UpperCAmelCase							,	UpperCAmelCase				: Any											=	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase				: Optional[Any]											=	tf.zeros_like(inputs_dict['''attention_mask''']	)
								UpperCAmelCase				: Optional[int]											=	2
								UpperCAmelCase				: Dict											=	tf.where(
								    tf.range(self.model_tester.seq_length	)[None, :] < num_global_attn_indices	,    1	,    inputs_dict['''global_attention_mask''']	,    )

								UpperCAmelCase				: Optional[int]											=	True
								UpperCAmelCase				: str											=	self.model_tester.seq_length
								UpperCAmelCase				: Optional[Any]											=	self.model_tester.encoder_seq_length

								def check_decoder_attentions_output(__snake_case    :  List[Any]	):
															UpperCAmelCase				: List[str]											=	outputs.decoder_attentions
															self.assertEqual(len(__snake_case	)	,    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(__snake_case    :  int	):
															UpperCAmelCase				: List[Any]											=	[t.numpy() for t in outputs.encoder_attentions]
															UpperCAmelCase				: str											=	[t.numpy() for t in outputs.encoder_global_attentions]
															self.assertEqual(len(__snake_case	)	,    self.model_tester.num_hidden_layers	)
															self.assertEqual(len(__snake_case	)	,    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:
															UpperCAmelCase				: Tuple											=	True
															UpperCAmelCase				: Optional[int]											=	False
															UpperCAmelCase				: int											=	False
															UpperCAmelCase				: Tuple											=	model_class(__snake_case	)
															UpperCAmelCase				: Tuple											=	model(self._prepare_for_class(__snake_case	,    __snake_case	)	)
															UpperCAmelCase				: Optional[int]											=	len(__snake_case	)
															self.assertEqual(config.output_hidden_states	,    __snake_case	)
															check_encoder_attentions_output(__snake_case	)

															if self.is_encoder_decoder:
																						UpperCAmelCase				: Optional[Any]											=	model_class(__snake_case	)
																						UpperCAmelCase				: Optional[int]											=	model(self._prepare_for_class(__snake_case	,    __snake_case	)	)
																						self.assertEqual(config.output_hidden_states	,    __snake_case	)
																						check_decoder_attentions_output(__snake_case	)

															# Check that output attentions can also be changed via the config
															del inputs_dict["output_attentions"]
															UpperCAmelCase				: Optional[int]											=	True
															UpperCAmelCase				: Union[str, Any]											=	model_class(__snake_case	)
															UpperCAmelCase				: Optional[Any]											=	model(self._prepare_for_class(__snake_case	,    __snake_case	)	)
															self.assertEqual(config.output_hidden_states	,    __snake_case	)
															check_encoder_attentions_output(__snake_case	)

															# Check attention is always last and order is fine
															UpperCAmelCase				: Any											=	True
															UpperCAmelCase				: Any											=	True
															UpperCAmelCase				: List[str]											=	model_class(__snake_case	)
															UpperCAmelCase				: Dict											=	model(self._prepare_for_class(__snake_case	,    __snake_case	)	)

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







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







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


def 				snake_case_  (      _lowerCAmelCase						:		str						)   ->     Tuple:
							return tf.constant(_lowerCAmelCase     ,   dtype=tf.intaa						)


UpperCamelCase__:     Tuple										=    1E-4




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







	"""simple docstring"""






	def   A			(		self    :  Any	)    ->	Optional[int]:
								UpperCAmelCase				: Optional[Any]											=	TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''	).led

								# change to intended input here
								UpperCAmelCase				: Optional[int]											=	_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]	)
								UpperCAmelCase				: Dict											=	_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]	)
								UpperCAmelCase				: Dict											=	prepare_led_inputs_dict(model.config	,    __snake_case	,    __snake_case	)
								UpperCAmelCase				: Optional[Any]											=	model(**__snake_case	)[0]
								UpperCAmelCase				: Optional[int]											=	(1, 1024, 768)
								self.assertEqual(output.shape	,    __snake_case	)
								# change to expected output here
								UpperCAmelCase				: List[str]											=	tf.convert_to_tensor(
								    [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]	,    )
								tf.debugging.assert_near(output[:, :3, :3]	,    __snake_case	,    atol=1E-3	)







	def   A			(		self    :  Dict	)    ->	Union[str, Any]:
								UpperCAmelCase				: Dict											=	TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''	)

								# change to intended input here
								UpperCAmelCase				: Optional[int]											=	_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]	)
								UpperCAmelCase				: Optional[Any]											=	_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]	)
								UpperCAmelCase				: Optional[int]											=	prepare_led_inputs_dict(model.config	,    __snake_case	,    __snake_case	)
								UpperCAmelCase				: Any											=	model(**__snake_case	)[0]
								UpperCAmelCase				: Union[str, Any]											=	(1, 1024, model.config.vocab_size)
								self.assertEqual(output.shape	,    __snake_case	)
								# change to expected output here
								UpperCAmelCase				: Dict											=	tf.convert_to_tensor(
								    [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]	,    )
								tf.debugging.assert_near(output[:, :3, :3]	,    __snake_case	,    atol=1E-3	,    rtol=1E-3	)





