

import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A_ :				List[Any]						 =    logging.get_logger(__name__)  # pylint: disable=invalid-name



class 					_a		(UpperCAmelCase__       ):


					'''simple docstring'''






					def __init__(				self					, A__					, A__=768    ):
						super().__init__(__lowerCAmelCase    )
						A__							:					Optional[Any]       =	proj_size

						A__							:					List[Any]       =	CLIPVisionModel(__lowerCAmelCase    )
						A__							:					List[str]       =	PaintByExampleMapper(__lowerCAmelCase    )
						A__							:					Union[str, Any]       =	nn.LayerNorm(config.hidden_size    )
						A__							:					List[Any]       =	nn.Linear(config.hidden_size					, self.proj_size    )

						# uncondition for scaling
						A__							:					Tuple       =	nn.Parameter(torch.randn((1, 1, self.proj_size)    )    )






					def 						__A    (				self					, A__					, A__=False    ):
						A__							:					Union[str, Any]       =	self.model(pixel_values=__lowerCAmelCase    )
						A__							:					Tuple       =	clip_output.pooler_output
						A__							:					Any       =	self.mapper(latent_states[:, None]    )
						A__							:					Any       =	self.final_layer_norm(__lowerCAmelCase    )
						A__							:					Tuple       =	self.proj_out(__lowerCAmelCase    )
						if return_uncond_vector:
							return latent_states, self.uncond_vector

						return latent_states



class 					_a		(nn.Module       ):


					'''simple docstring'''






					def __init__(				self					, A__    ):
						super().__init__()
						A__							:					Optional[Any]       =	(config.num_hidden_layers + 1) // 5
						A__							:					Optional[Any]       =	config.hidden_size
						A__							:					Any       =	1
						A__							:					Optional[Any]       =	nn.ModuleList(
						    [
						        BasicTransformerBlock(__lowerCAmelCase					, __lowerCAmelCase					, __lowerCAmelCase					, activation_fn="""gelu"""					, attention_bias=__lowerCAmelCase    )
						        for _ in range(__lowerCAmelCase    )
						    ]    )






					def 						__A    (				self					, A__    ):






						for block in self.blocks:
							A__							:					Union[str, Any]       =	block(__lowerCAmelCase    )

						return hidden_states






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 					_a		:


					'''simple docstring'''






					UpperCAmelCase__:     List[Any]													= PegasusConfig
					UpperCAmelCase__:     Optional[int]													= {}
					UpperCAmelCase__:     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					, ):
						A__							:					Dict       =	parent
						A__							:					Dict       =	batch_size
						A__							:					Any       =	seq_length
						A__							:					Optional[Any]       =	is_training
						A__							:					int       =	use_labels
						A__							:					Any       =	vocab_size
						A__							:					Union[str, Any]       =	hidden_size
						A__							:					Tuple       =	num_hidden_layers
						A__							:					Tuple       =	num_attention_heads
						A__							:					List[Any]       =	intermediate_size

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






					def 						__A    (				self    ):
						A__							:					str       =	ids_tensor([self.batch_size, self.seq_length - 1]					, self.vocab_size    )
						A__							:					Dict       =	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )					, 1    )
						A__							:					List[Any]       =	tf.concat([input_ids, eos_tensor]					, axis=1    )

						A__							:					Union[str, Any]       =	ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size    )

						A__							:					Tuple       =	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					, )
						A__							:					str       =	prepare_pegasus_inputs_dict(A__					, A__					, A__    )
						return config, inputs_dict






					def 						__A    (				self					, A__					, A__    ):
						A__							:					int       =	TFPegasusModel(config=A__    ).get_decoder()
						A__							:					List[Any]       =	inputs_dict["""input_ids"""]

						A__							:					Any       =	input_ids[:1, :]
						A__							:					Optional[Any]       =	inputs_dict["""attention_mask"""][:1, :]
						A__							:					Optional[int]       =	inputs_dict["""head_mask"""]
						A__							:					Any       =	1

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

						A__     ,     A__							:					Dict       =	outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						A__							:					Tuple       =	ids_tensor((self.batch_size, 3)					, config.vocab_size    )
						A__							:					Optional[Any]       =	tf.cast(ids_tensor((self.batch_size, 3)					, 2    )					, tf.inta    )

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

						A__							:					Optional[Any]       =	model(A__					, attention_mask=A__    )[0]
						A__							:					Any       =	model(A__					, attention_mask=A__					, past_key_values=A__    )[0]

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

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

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



def 		UpperCamelCase      (lowercase_:				Union[str, Any]					,   lowercase_:				Dict					,   lowercase_:				List[Any]					,   lowercase_:				Dict=None					,   lowercase_:				int=None					,   lowercase_:				List[Any]=None					,   lowercase_:				List[Any]=None					,   lowercase_:				str=None					,   )  ->   int:
	if attention_mask is None:
		A__							:					List[str]       =	tf.cast(tf.math.not_equal(lowercase_					,   config.pad_token_id      )					,   tf.inta      )
	if decoder_attention_mask is None:
		A__							:					Dict       =	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:
		A__							:					Any       =	tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
	if decoder_head_mask is None:
		A__							:					Tuple       =	tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
	if cross_attn_head_mask is None:
		A__							:					List[str]       =	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 					_a		(__magic_name__	,     __magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     List[Any]													= (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
					UpperCAmelCase__:     Tuple													= (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
					UpperCAmelCase__:     Tuple													= (
					    {
					        '''conversational''': TFPegasusForConditionalGeneration,
					        '''feature-extraction''': TFPegasusModel,
					        '''summarization''': TFPegasusForConditionalGeneration,
					        '''text2text-generation''': TFPegasusForConditionalGeneration,
					        '''translation''': TFPegasusForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					UpperCAmelCase__:     int													= True
					UpperCAmelCase__:     Union[str, Any]													= False
					UpperCAmelCase__:     List[str]													= False






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	TFPegasusModelTester(self    )
						A__							:					Tuple       =	ConfigTester(self					, config_class=A__    )






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






					def 						__A    (				self    ):
						A__							:					int       =	self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*A__    )



@require_sentencepiece
@require_tokenizers
@require_tf
class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     Optional[int]													= [
					    ''' 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!" ''',
					]
					UpperCAmelCase__:     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
					UpperCAmelCase__:     List[str]													= '''google/pegasus-xsum'''






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






					@cached_property
					def 						__A    (				self    ):
						A__							:					int       =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
						return model






					def 						__A    (				self					, **A__    ):
						A__							:					str       =	self.translate_src_text(**A__    )
						assert self.expected_text == generated_words






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






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



