
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     lowercase :
		__lowercase	:	List[Any]     					=    PegasusConfig
		__lowercase	:	Tuple     					=    {}
		__lowercase	:	List[str]     					=    "gelu"



		def __init__(			self    ,  A_    ,  A_=13    ,  A_=7    ,  A_=True    ,  A_=False    ,  A_=99    ,  A_=32    ,  A_=2    ,  A_=4    ,  A_=37    ,  A_=0.1    ,  A_=0.1    ,  A_=40    ,  A_=2    ,  A_=1    ,  A_=0    ,  )  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					seq_length
				UpperCamelCase          =					is_training
				UpperCamelCase          =					use_labels
				UpperCamelCase          =					vocab_size
				UpperCamelCase          =					hidden_size
				UpperCamelCase          =					num_hidden_layers
				UpperCamelCase          =					num_attention_heads
				UpperCamelCase          =					intermediate_size

				UpperCamelCase          =					hidden_dropout_prob
				UpperCamelCase          =					attention_probs_dropout_prob
				UpperCamelCase          =					max_position_embeddings
				UpperCamelCase          =					eos_token_id
				UpperCamelCase          =					pad_token_id
				UpperCamelCase          =					bos_token_id



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					ids_tensor([self.batch_size, self.seq_length - 1]    ,  self.vocab_size	)
				UpperCamelCase          =					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)    ,  1	)
				UpperCamelCase          =					tf.concat([input_ids, eos_tensor]    ,  axis=1	)

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

				UpperCamelCase          =					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          =					prepare_pegasus_inputs_dict(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_	)
				return config, inputs_dict



		def __UpperCamelCase			(			self    ,  A_    ,  A_	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					TFPegasusModel(config=SCREAMING_SNAKE_CASE_	).get_decoder()
				UpperCamelCase          =					inputs_dict["""input_ids"""]

				UpperCamelCase          =					input_ids[:1, :]
				UpperCamelCase          =					inputs_dict["""attention_mask"""][:1, :]
				UpperCamelCase          =					inputs_dict["""head_mask"""]
				UpperCamelCase          =					1

				# first forward pass
				UpperCamelCase          =					model(SCREAMING_SNAKE_CASE_    ,  attention_mask=SCREAMING_SNAKE_CASE_    ,  head_mask=SCREAMING_SNAKE_CASE_    ,  use_cache=SCREAMING_SNAKE_CASE_	)

				UpperCamelCase          =					outputs.to_tuple()

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

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

				UpperCamelCase          =					model(SCREAMING_SNAKE_CASE_    ,  attention_mask=SCREAMING_SNAKE_CASE_	)[0]
				UpperCamelCase          =					model(SCREAMING_SNAKE_CASE_    ,  attention_mask=SCREAMING_SNAKE_CASE_    ,  past_key_values=SCREAMING_SNAKE_CASE_	)[0]

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

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

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






def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase=None   ,	lowercase=None   ,	lowercase=None   ,	lowercase=None   ,	lowercase=None   ,	)       ->  List[Any]:
		'''simple docstring'''

		if attention_mask is None:
				UpperCamelCase          =					tf.cast(tf.math.not_equal(lowerCAmelCase__   ,	config.pad_token_id			)   ,	tf.inta			)
		if decoder_attention_mask is None:
				UpperCamelCase          =					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          =					tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
		if decoder_head_mask is None:
				UpperCamelCase          =					tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
		if cross_attn_head_mask is None:
				UpperCamelCase          =					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     lowercase (		_lowerCAmelCase  ,       _lowerCAmelCase  ,       unittest.TestCase       ):
		__lowercase	:	Tuple     					=    (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
		__lowercase	:	str     					=    (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
		__lowercase	:	Optional[int]     					=    (
		    {
		        "conversational": TFPegasusForConditionalGeneration,
		        "feature-extraction": TFPegasusModel,
		        "summarization": TFPegasusForConditionalGeneration,
		        "text2text-generation": TFPegasusForConditionalGeneration,
		        "translation": TFPegasusForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		__lowercase	:	List[str]     					=    True
		__lowercase	:	List[str]     					=    False
		__lowercase	:	List[str]     					=    False



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					TFPegasusModelTester(self	)
				UpperCamelCase          =					ConfigTester(self    ,  config_class=SCREAMING_SNAKE_CASE_	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_	)






@require_sentencepiece
@require_tokenizers
@require_tf
class     lowercase (		unittest.TestCase       ):
		__lowercase	:	str     					=    [
		    " 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!\" ",
		]
		__lowercase	:	Optional[int]     					=    [
		    "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
		__lowercase	:	List[Any]     					=    "google/pegasus-xsum"



		@cached_property
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				return AutoTokenizer.from_pretrained(self.model_name	)



		@cached_property
		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
				return model



		def __UpperCamelCase			(			self    ,  **A_	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					self.translate_src_text(**SCREAMING_SNAKE_CASE_	)
				assert self.expected_text == generated_words



		def __UpperCamelCase			(			self    ,  **A_	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.tokenizer(self.src_text    ,  **SCREAMING_SNAKE_CASE_    ,  padding=SCREAMING_SNAKE_CASE_    ,  return_tensors='tf'	)
				UpperCamelCase          =					self.model.generate(
				    model_inputs.input_ids    ,  attention_mask=model_inputs.attention_mask    ,  num_beams=2    ,  use_cache=SCREAMING_SNAKE_CASE_    ,  )
				UpperCamelCase          =					self.tokenizer.batch_decode(generated_ids.numpy()    ,  skip_special_tokens=SCREAMING_SNAKE_CASE_	)
				return generated_words



		@slow
		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				self._assert_generated_batch_equal_expected()


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel
