


'''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 						UpperCamelCase__       :



     """simple docstring"""






     __magic_name__           =     PegasusConfig
     __magic_name__           =     {}
     __magic_name__           =     "gelu"







     def __init__(			self   ,						snake_case__   ,						snake_case__=13   ,						snake_case__=7   ,						snake_case__=True   ,						snake_case__=False   ,						snake_case__=99   ,						snake_case__=32   ,						snake_case__=2   ,						snake_case__=4   ,						snake_case__=37   ,						snake_case__=0.1   ,						snake_case__=0.1   ,						snake_case__=40   ,						snake_case__=2   ,						snake_case__=1   ,						snake_case__=0   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							parent
            _lowerCAmelCase     :				Any       =							batch_size
            _lowerCAmelCase     :				int       =							seq_length
            _lowerCAmelCase     :				Union[str, Any]       =							is_training
            _lowerCAmelCase     :				Optional[int]       =							use_labels
            _lowerCAmelCase     :				Dict       =							vocab_size
            _lowerCAmelCase     :				Any       =							hidden_size
            _lowerCAmelCase     :				str       =							num_hidden_layers
            _lowerCAmelCase     :				Tuple       =							num_attention_heads
            _lowerCAmelCase     :				List[Any]       =							intermediate_size

            _lowerCAmelCase     :				Tuple       =							hidden_dropout_prob
            _lowerCAmelCase     :				Optional[Any]       =							attention_probs_dropout_prob
            _lowerCAmelCase     :				Dict       =							max_position_embeddings
            _lowerCAmelCase     :				Optional[int]       =							eos_token_id
            _lowerCAmelCase     :				str       =							pad_token_id
            _lowerCAmelCase     :				Tuple       =							bos_token_id







     def a						(			self ):

            '''simple docstring'''

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

            _lowerCAmelCase     :				Any       =							ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size )

            _lowerCAmelCase     :				Optional[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   ,						**self.config_updates   ,						)
            _lowerCAmelCase     :				Dict       =							prepare_pegasus_inputs_dict(snake_case__   ,						snake_case__   ,						snake_case__ )
            return config, inputs_dict







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							TFPegasusModel(config=snake_case__ ).get_decoder()
            _lowerCAmelCase     :				List[Any]       =							inputs_dict['input_ids']

            _lowerCAmelCase     :				Any       =							input_ids[:1, :]
            _lowerCAmelCase     :				str       =							inputs_dict['attention_mask'][:1, :]
            _lowerCAmelCase     :				Optional[int]       =							inputs_dict['head_mask']
            _lowerCAmelCase     :				Union[str, Any]       =							1

            # first forward pass
            _lowerCAmelCase     :				Optional[Any]       =							model(snake_case__   ,						attention_mask=snake_case__   ,						head_mask=snake_case__   ,						use_cache=snake_case__ )

            _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[Any]       =							outputs.to_tuple()

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

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

            _lowerCAmelCase     :				Optional[Any]       =							model(snake_case__   ,						attention_mask=snake_case__ )[0]
            _lowerCAmelCase     :				int       =							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
            _lowerCAmelCase     :				List[Any]       =							int(ids_tensor((1,)   ,						output_from_past.shape[-1] ) )
            _lowerCAmelCase     :				str       =							output_from_no_past[:, -3:, random_slice_idx]
            _lowerCAmelCase     :				Optional[int]       =							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       lowercase					(_A ,    _A ,    _A ,    _A=None ,    _A=None ,    _A=None ,    _A=None ,    _A=None ,    ):







       """simple docstring"""







       if attention_mask is None:
              _lowerCAmelCase     :				Optional[int]       =							tf.cast(tf.math.not_equal(_A ,    config.pad_token_id       ) ,    tf.inta       )
       if decoder_attention_mask is None:
              _lowerCAmelCase     :				List[str]       =							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:
              _lowerCAmelCase     :				Union[str, Any]       =							tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
       if decoder_head_mask is None:
              _lowerCAmelCase     :				Dict       =							tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
       if cross_attn_head_mask is None:
              _lowerCAmelCase     :				Optional[int]       =							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 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
     __magic_name__           =     (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
     __magic_name__           =     (
         {
             "conversational": TFPegasusForConditionalGeneration,
             "feature-extraction": TFPegasusModel,
             "summarization": TFPegasusForConditionalGeneration,
             "text2text-generation": TFPegasusForConditionalGeneration,
             "translation": TFPegasusForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __magic_name__           =     True
     __magic_name__           =     False
     __magic_name__           =     False







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							TFPegasusModelTester(self )
            _lowerCAmelCase     :				int       =							ConfigTester(self   ,						config_class=snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*snake_case__ )
@require_sentencepiece
@require_tokenizers
@require_tf
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     [
         " 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!\" ",
     ]
     __magic_name__           =     [
         "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
     __magic_name__           =     "google/pegasus-xsum"







     @cached_property
     def a						(			self ):

            '''simple docstring'''

            return AutoTokenizer.from_pretrained(self.model_name )







     @cached_property
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
            return model







     def a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							self.translate_src_text(**snake_case__ )
            assert self.expected_text == generated_words







     def a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							self.tokenizer(self.src_text   ,						**snake_case__   ,						padding=snake_case__   ,						return_tensors='tf' )
            _lowerCAmelCase     :				Dict       =							self.model.generate(
                model_inputs.input_ids   ,						attention_mask=model_inputs.attention_mask   ,						num_beams=2   ,						use_cache=snake_case__   ,						)
            _lowerCAmelCase     :				Dict       =							self.tokenizer.batch_decode(generated_ids.numpy()   ,						skip_special_tokens=snake_case__ )
            return generated_words







     @slow
     def a						(			self ):

            '''simple docstring'''

            self._assert_generated_batch_equal_expected()





'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowerCAmelCase     :  str											=    False
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     pass
@nightly
@require_torch_gpu
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
            # remove text_unet
            pipe.remove_unused_weights()
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Any       =							'A painting of a squirrel eating a burger '
            _lowerCAmelCase     :				List[Any]       =							torch.manual_seed(0 )
            _lowerCAmelCase     :				Any       =							pipe(
                prompt=snake_case__   ,						generator=snake_case__   ,						guidance_scale=7.5   ,						num_inference_steps=2   ,						output_type='numpy' ).images

            with tempfile.TemporaryDirectory() as tmpdirname:
                   pipe.save_pretrained(snake_case__ )
                   _lowerCAmelCase     :				Any       =							VersatileDiffusionTextToImagePipeline.from_pretrained(snake_case__ )
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Optional[int]       =							generator.manual_seed(0 )
            _lowerCAmelCase     :				Tuple       =							pipe(
                prompt=snake_case__   ,						generator=snake_case__   ,						guidance_scale=7.5   ,						num_inference_steps=2   ,						output_type='numpy' ).images

            assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							VersatileDiffusionTextToImagePipeline.from_pretrained(
                'shi-labs/versatile-diffusion'   ,						torch_dtype=torch.floataa )
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Dict       =							'A painting of a squirrel eating a burger '
            _lowerCAmelCase     :				Any       =							torch.manual_seed(0 )
            _lowerCAmelCase     :				int       =							pipe(
                prompt=snake_case__   ,						generator=snake_case__   ,						guidance_scale=7.5   ,						num_inference_steps=50   ,						output_type='numpy' ).images

            _lowerCAmelCase     :				Optional[int]       =							image[0, 253:256, 253:256, -1]

            assert image.shape == (1, 512, 512, 3)
            _lowerCAmelCase     :				Dict       =							np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

            assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2

