


'''simple docstring'''



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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None	, UpperCamelCase__=None	, UpperCamelCase__=None	, UpperCamelCase__=None	, UpperCamelCase__=None	, ):




			if attention_mask is None:
						UpperCAmelCase__					:   int       				=       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=_a   )
			if decoder_head_mask is None:
						UpperCAmelCase__					:   Optional[int]       				=       torch.ones(config.decoder_layers	, config.decoder_attention_heads	, device=_a   )
			if cross_attn_head_mask is None:
						UpperCAmelCase__					:   Any       				=       torch.ones(config.decoder_layers	, config.decoder_attention_heads	, device=_a   )
			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 						_snake_case   :




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




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)
								UpperCAmelCase__					:   Any       				=       self.eos_token_id  # Eos Token
								UpperCAmelCase__					:   Optional[int]       				=       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__					:   Optional[int]       				=       input_ids.clamp(self.pad_token_id + 1)
								UpperCAmelCase__					:   List[str]       				=       decoder_input_ids.clamp(self.pad_token_id + 1)

								UpperCAmelCase__					:   List[str]       				=       self.get_config()
								UpperCAmelCase__					:   int       				=       prepare_mam_aaa_inputs_dict(lowerCamelCase_							,							lowerCamelCase_							,							lowerCamelCase_)
								return config, inputs_dict




					def 			snake_case__  ( self):
								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 			snake_case__  ( self):
								UpperCAmelCase__					:   str       				=       self.prepare_config_and_inputs()
								return config, inputs_dict




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   int       				=       MaMaaaModel(config=lowerCamelCase_).get_decoder().to(lowerCamelCase_).eval()
								UpperCAmelCase__					:   str       				=       inputs_dict["""input_ids"""]
								UpperCAmelCase__					:   List[Any]       				=       inputs_dict["""attention_mask"""]
								UpperCAmelCase__					:   str       				=       inputs_dict["""head_mask"""]

								# first forward pass
								UpperCAmelCase__					:   Optional[int]       				=       model(lowerCamelCase_							,							attention_mask=lowerCamelCase_							,							head_mask=lowerCamelCase_							,							use_cache=lowerCamelCase_)

								UpperCAmelCase__					:   str       				=       outputs.to_tuple()

								# create hypothetical multiple next token and extent to next_input_ids
								UpperCAmelCase__					:   Tuple       				=       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__					:   Optional[int]       				=       torch.cat([input_ids, next_tokens]							,							dim=-1)
								UpperCAmelCase__					:   str       				=       torch.cat([attention_mask, next_attn_mask]							,							dim=-1)

								UpperCAmelCase__					:   List[Any]       				=       model(lowerCamelCase_							,							attention_mask=lowerCamelCase_)["""last_hidden_state"""]
								UpperCAmelCase__					:   List[Any]       				=       model(lowerCamelCase_							,							attention_mask=lowerCamelCase_							,							past_key_values=lowerCamelCase_)[
								    """last_hidden_state"""
								]

								# select random slice
								UpperCAmelCase__					:   str       				=       ids_tensor((1,)							,							output_from_past.shape[-1]).item()
								UpperCAmelCase__					:   Tuple       				=       output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCAmelCase__					:   Union[str, 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(lowerCamelCase_							,							lowerCamelCase_							,							atol=1e-2))




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Union[str, Any]       				=       MaMaaaModel(config=lowerCamelCase_).to(lowerCamelCase_).eval()
								UpperCAmelCase__					:   Optional[Any]       				=       model(**lowerCamelCase_)

								UpperCAmelCase__					:   Dict       				=       outputs.encoder_last_hidden_state
								UpperCAmelCase__					:   Dict       				=       outputs.last_hidden_state

								with tempfile.TemporaryDirectory() as tmpdirname:
											UpperCAmelCase__					:   Union[str, Any]       				=       model.get_encoder()
											encoder.save_pretrained(lowerCamelCase_)
											UpperCAmelCase__					:   int       				=       MaMaaaEncoder.from_pretrained(lowerCamelCase_).to(lowerCamelCase_)

								UpperCAmelCase__					:   Optional[int]       				=       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__					:   Optional[int]       				=       model.get_decoder()
											decoder.save_pretrained(lowerCamelCase_)
											UpperCAmelCase__					:   List[str]       				=       MaMaaaDecoder.from_pretrained(lowerCamelCase_).to(lowerCamelCase_)

								UpperCAmelCase__					:   Tuple       				=       decoder(
								    input_ids=inputs_dict["""decoder_input_ids"""]							,							attention_mask=inputs_dict["""decoder_attention_mask"""]							,							encoder_hidden_states=lowerCamelCase_							,							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 						_snake_case   (       __snake_case    ,				__snake_case    ,				__snake_case    ,				unittest.TestCase		):
					lowerCAmelCase :str						=   (
					    (
					        MaMaaaModel,
					        MaMaaaForConditionalGeneration,
					    )
					    if is_torch_available()
					    else ()
					)
					lowerCAmelCase :List[Any]						=   (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
					lowerCAmelCase :List[Any]						=   (
					    {
					        "conversational": MaMaaaForConditionalGeneration,
					        "feature-extraction": MaMaaaModel,
					        "summarization": MaMaaaForConditionalGeneration,
					        "text2text-generation": MaMaaaForConditionalGeneration,
					        "translation": MaMaaaForConditionalGeneration,
					    }
					    if is_torch_available()
					    else {}
					)
					lowerCAmelCase :Dict						=   True
					lowerCAmelCase :Union[str, Any]						=   True
					lowerCAmelCase :Union[str, Any]						=   False
					lowerCAmelCase :Any						=   False




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								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 			snake_case__  ( self):
								UpperCAmelCase__					:   str       				=       MaMaaaModelTester(self)
								UpperCAmelCase__					:   Optional[Any]       				=       ConfigTester(self							,							config_class=lowerCamelCase_)




					def 			snake_case__  ( self):
								self.config_tester.run_common_tests()




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Dict       				=       self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
											UpperCAmelCase__					:   Union[str, Any]       				=       model_class(lowerCamelCase_)

											with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(lowerCamelCase_)
														UpperCAmelCase__					:   Any       				=       model_class.from_pretrained(lowerCamelCase_							,							output_loading_info=lowerCamelCase_)
											self.assertEqual(info["""missing_keys"""]							,							[])




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCamelCase_)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_encoder_decoder_model_standalone(*lowerCamelCase_)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
											UpperCAmelCase__					:   Dict       				=       model_class(lowerCamelCase_)
											model.to(lowerCamelCase_)
											model.eval()

											UpperCAmelCase__					:   Union[str, Any]       				=       copy.deepcopy(self._prepare_for_class(lowerCamelCase_							,							lowerCamelCase_))

											if not self.is_encoder_decoder:
														UpperCAmelCase__					:   str       				=       inputs["""input_ids"""]
														del inputs["input_ids"]
											else:
														UpperCAmelCase__					:   Any       				=       inputs["""input_ids"""]
														UpperCAmelCase__					:   Tuple       				=       inputs.get("""decoder_input_ids"""							,							lowerCamelCase_)
														del inputs["input_ids"]
														inputs.pop("""decoder_input_ids"""							,							lowerCamelCase_)

											UpperCAmelCase__					:   Optional[int]       				=       model.get_input_embeddings()
											if not self.is_encoder_decoder:
														UpperCAmelCase__					:   Optional[Any]       				=       wte(lowerCamelCase_)
											else:
														UpperCAmelCase__					:   str       				=       wte(lowerCamelCase_)
														UpperCAmelCase__					:   List[str]       				=       wte(lowerCamelCase_)

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




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Dict       				=       self.model_tester.prepare_config_and_inputs()
								UpperCAmelCase__					:   int       				=       input_dict["""input_ids"""]
								UpperCAmelCase__					:   Tuple       				=       input_ids.ne(1).to(lowerCamelCase_)
								UpperCAmelCase__					:   List[Any]       				=       MaMaaaForConditionalGeneration(lowerCamelCase_).eval().to(lowerCamelCase_)
								if torch_device == "cuda":
											model.half()
								model.generate(lowerCamelCase_							,							attention_mask=lowerCamelCase_)
								model.generate(num_beams=4							,							do_sample=lowerCamelCase_							,							early_stopping=lowerCamelCase_							,							num_return_sequences=3)







def       _UpperCamelCase (						UpperCamelCase__   ):
			return torch.tensor(_a	, dtype=torch.long	, device=_a   )


__A		=1E-4


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




					@cached_property
					def 			snake_case__  ( self):
								return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M""")




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       MaMaaaModel.from_pretrained("""facebook/m2m100_418M""").to(lowerCamelCase_)
								UpperCAmelCase__					:   List[Any]       				=       _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]])
								UpperCAmelCase__					:   int       				=       _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]])
								UpperCAmelCase__					:   Dict       				=       prepare_mam_aaa_inputs_dict(model.config							,							lowerCamelCase_							,							lowerCamelCase_)
								with torch.no_grad():
											UpperCAmelCase__					:   Union[str, Any]       				=       model(**lowerCamelCase_)[0]
								UpperCAmelCase__					:   str       				=       torch.Size((1, 11, 1024))
								self.assertEqual(output.shape							,							lowerCamelCase_)
								# change to expected output here
								UpperCAmelCase__					:   Union[str, Any]       				=       torch.tensor(
								    [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]							,							device=lowerCamelCase_)
								self.assertTrue(torch.allclose(output[:, :3, :3]							,							lowerCamelCase_							,							atol=lowerCamelCase_))




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M""").to(lowerCamelCase_)

								# change to intended input
								UpperCAmelCase__					:   Any       				=       _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]])
								UpperCAmelCase__					:   Any       				=       _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]])
								UpperCAmelCase__					:   int       				=       prepare_mam_aaa_inputs_dict(model.config							,							lowerCamelCase_							,							lowerCamelCase_)
								with torch.no_grad():
											UpperCAmelCase__					:   Optional[Any]       				=       model(**lowerCamelCase_)[0]
								UpperCAmelCase__					:   List[str]       				=       torch.Size((1, 11, model.config.vocab_size))
								self.assertEqual(output.shape							,							lowerCamelCase_)
								# change to expected output here
								UpperCAmelCase__					:   int       				=       torch.tensor(
								    [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]							,							device=lowerCamelCase_)
								self.assertTrue(torch.allclose(output[:, :3, :3]							,							lowerCamelCase_							,							atol=lowerCamelCase_))




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Union[str, Any]       				=       MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M""").to(lowerCamelCase_)
								UpperCAmelCase__					:   str       				=       MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""							,							src_lang="""fr"""							,							tgt_lang="""en""")

								UpperCAmelCase__					:   List[Any]       				=       [
								    """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__					:   Any       				=       tokenizer(lowerCamelCase_							,							padding=lowerCamelCase_							,							return_tensors="""pt""")

								UpperCAmelCase__					:   Dict       				=       model.generate(
								    input_ids=dct["""input_ids"""].to(lowerCamelCase_)							,							attention_mask=dct["""attention_mask"""].to(lowerCamelCase_)							,							num_beams=5							,							forced_bos_token_id=tokenizer.get_lang_id("""en""")							,							)

								UpperCAmelCase__					:   str       				=       [
								    """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__					:   Union[str, Any]       				=       tokenizer.batch_decode(
								    hypotheses_batch.tolist()							,							clean_up_tokenization_spaces=lowerCamelCase_							,							skip_special_tokens=lowerCamelCase_)
								assert generated == expected_en



'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel


@require_tf
class 						_snake_case   :
					lowerCAmelCase :str						=   PegasusConfig
					lowerCAmelCase :Optional[int]						=   {}
					lowerCAmelCase :Dict						=   '''gelu'''




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=13							,							_lowerCamelCase=7							,							_lowerCamelCase=True							,							_lowerCamelCase=False							,							_lowerCamelCase=99							,							_lowerCamelCase=32							,							_lowerCamelCase=2							,							_lowerCamelCase=4							,							_lowerCamelCase=37							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=40							,							_lowerCamelCase=2							,							_lowerCamelCase=1							,							_lowerCamelCase=0							,							):
								UpperCAmelCase__					:   Union[str, Any]       				=       parent
								UpperCAmelCase__					:   Any       				=       batch_size
								UpperCAmelCase__					:   Union[str, Any]       				=       seq_length
								UpperCAmelCase__					:   str       				=       is_training
								UpperCAmelCase__					:   Dict       				=       use_labels
								UpperCAmelCase__					:   Optional[int]       				=       vocab_size
								UpperCAmelCase__					:   int       				=       hidden_size
								UpperCAmelCase__					:   Union[str, Any]       				=       num_hidden_layers
								UpperCAmelCase__					:   str       				=       num_attention_heads
								UpperCAmelCase__					:   Any       				=       intermediate_size

								UpperCAmelCase__					:   int       				=       hidden_dropout_prob
								UpperCAmelCase__					:   Optional[Any]       				=       attention_probs_dropout_prob
								UpperCAmelCase__					:   List[str]       				=       max_position_embeddings
								UpperCAmelCase__					:   Union[str, Any]       				=       eos_token_id
								UpperCAmelCase__					:   Any       				=       pad_token_id
								UpperCAmelCase__					:   Dict       				=       bos_token_id




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       ids_tensor([self.batch_size, self.seq_length - 1]							,							self.vocab_size)
								UpperCAmelCase__					:   str       				=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)							,							1)
								UpperCAmelCase__					:   Union[str, Any]       				=       tf.concat([input_ids, eos_tensor]							,							axis=1)

								UpperCAmelCase__					:   int       				=       ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

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




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Optional[int]       				=       TFPegasusModel(config=_lowerCamelCase).get_decoder()
								UpperCAmelCase__					:   Optional[int]       				=       inputs_dict["""input_ids"""]

								UpperCAmelCase__					:   Any       				=       input_ids[:1, :]
								UpperCAmelCase__					:   List[str]       				=       inputs_dict["""attention_mask"""][:1, :]
								UpperCAmelCase__					:   int       				=       inputs_dict["""head_mask"""]
								UpperCAmelCase__					:   int       				=       1

								# first forward pass
								UpperCAmelCase__					:   int       				=       model(_lowerCamelCase							,							attention_mask=_lowerCamelCase							,							head_mask=_lowerCamelCase							,							use_cache=_lowerCamelCase)

								UpperCAmelCase__				,    UpperCAmelCase__					:   List[str]       				=       outputs.to_tuple()

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

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

								UpperCAmelCase__					:   Union[str, Any]       				=       model(_lowerCamelCase							,							attention_mask=_lowerCamelCase)[0]
								UpperCAmelCase__					:   Any       				=       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
								UpperCAmelCase__					:   Optional[Any]       				=       int(ids_tensor((1,)							,							output_from_past.shape[-1]))
								UpperCAmelCase__					:   int       				=       output_from_no_past[:, -3:, random_slice_idx]
								UpperCAmelCase__					:   List[str]       				=       output_from_past[:, :, random_slice_idx]

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







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None	, UpperCamelCase__=None	, UpperCamelCase__=None	, UpperCamelCase__=None	, UpperCamelCase__=None	, ):
			if attention_mask is None:
						UpperCAmelCase__					:   Any       				=       tf.cast(tf.math.not_equal(UpperCamelCase__	, config.pad_token_id   )	, tf.inta   )
			if decoder_attention_mask is None:
						UpperCAmelCase__					:   Optional[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__					:   Dict       				=       tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
			if cross_attn_head_mask is None:
						UpperCAmelCase__					:   Tuple       				=       tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}


@require_tf
class 						_snake_case   (       a__    ,				a__    ,				unittest.TestCase		):
					lowerCAmelCase :str						=   (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
					lowerCAmelCase :Any						=   (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
					lowerCAmelCase :List[str]						=   (
					    {
					        '''conversational''': TFPegasusForConditionalGeneration,
					        '''feature-extraction''': TFPegasusModel,
					        '''summarization''': TFPegasusForConditionalGeneration,
					        '''text2text-generation''': TFPegasusForConditionalGeneration,
					        '''translation''': TFPegasusForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					lowerCAmelCase :int						=   True
					lowerCAmelCase :Optional[int]						=   False
					lowerCAmelCase :Dict						=   False




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       TFPegasusModelTester(self)
								UpperCAmelCase__					:   Dict       				=       ConfigTester(self							,							config_class=_lowerCamelCase)




					def 			snake_case__  ( self):
								self.config_tester.run_common_tests()




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Dict       				=       self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*_lowerCamelCase)


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




					@cached_property
					def 			snake_case__  ( self):
								return AutoTokenizer.from_pretrained(self.model_name)




					@cached_property
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
								return model




					def 			snake_case__  ( self							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Dict       				=       self.translate_src_text(**_lowerCamelCase)
								assert self.expected_text == generated_words




					def 			snake_case__  ( self							,							**_lowerCamelCase):
								UpperCAmelCase__					:   List[Any]       				=       self.tokenizer(self.src_text							,							**_lowerCamelCase							,							padding=_lowerCamelCase							,							return_tensors="""tf""")
								UpperCAmelCase__					:   Dict       				=       self.model.generate(
								    model_inputs.input_ids							,							attention_mask=model_inputs.attention_mask							,							num_beams=2							,							use_cache=_lowerCamelCase							,							)
								UpperCAmelCase__					:   Optional[Any]       				=       self.tokenizer.batch_decode(generated_ids.numpy()							,							skip_special_tokens=_lowerCamelCase)
								return generated_words




					@slow
					def 			snake_case__  ( self):
								self._assert_generated_batch_equal_expected()