

import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	lowerCamelCase_    :		int							=	"""platform"""
	import jax
	import jax.numpy as jnp
	import numpy as np

	from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel


@require_flax
class 						a__							:
			A__    :       str        =      PegasusConfig
			A__    :       List[str]        =      {}
			A__    :       str        =      """gelu"""



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase=1_3				,						UpperCAmelCase=7				,						UpperCAmelCase=True				,						UpperCAmelCase=False				,						UpperCAmelCase=9_9				,						UpperCAmelCase=3_2				,						UpperCAmelCase=5				,						UpperCAmelCase=4				,						UpperCAmelCase=3_7				,						UpperCAmelCase=0.1				,						UpperCAmelCase=0.1				,						UpperCAmelCase=2_0				,						UpperCAmelCase=2				,						UpperCAmelCase=1				,						UpperCAmelCase=0				,						)			->   Tuple:
								__a		 =       parent
								__a		 =       batch_size
								__a		 =       seq_length
								__a		 =       is_training
								__a		 =       use_labels
								__a		 =       vocab_size
								__a		 =       hidden_size
								__a		 =       num_hidden_layers
								__a		 =       num_attention_heads
								__a		 =       intermediate_size

								__a		 =       hidden_dropout_prob
								__a		 =       attention_probs_dropout_prob
								__a		 =       max_position_embeddings
								__a		 =       eos_token_id
								__a		 =       pad_token_id
								__a		 =       bos_token_id



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       ids_tensor([self.batch_size, self.seq_length - 1]				,						self.vocab_size   ).clip(3				,						self.vocab_size   )
								__a		 =       np.expand_dims(np.array([self.eos_token_id] * self.batch_size   )				,						1   )
								__a		 =       np.concatenate([input_ids, eos_tensor]				,						axis=1   )

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

								__a		 =       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		 =       prepare_pegasus_inputs_dict(_lowercase				,						_lowercase				,						_lowercase   )
								return config, inputs_dict



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Optional[Any]:
								__a		 =       2_0
								__a		 =       model_class_name(_lowercase   )

								__a		 =       model.encode(inputs_dict['input_ids']   )

								__a ,				__a		 =       (
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

								__a		 =       model.init_cache(decoder_input_ids.shape[0]				,						_lowercase				,						_lowercase   )
								__a		 =       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,						dtype='i4'   )

								__a		 =       jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]				,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,						)
								__a		 =       model.decode(
								    decoder_input_ids[:, :-1]				,						_lowercase				,						decoder_attention_mask=_lowercase				,						past_key_values=_lowercase				,						decoder_position_ids=_lowercase				,						)

								__a		 =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,						dtype='i4'   )
								__a		 =       model.decode(
								    decoder_input_ids[:, -1:]				,						_lowercase				,						decoder_attention_mask=_lowercase				,						past_key_values=outputs_cache.past_key_values				,						decoder_position_ids=_lowercase				,						)

								__a		 =       model.decode(_lowercase				,						_lowercase   )

								__a		 =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
								self.parent.assertTrue(diff < 1e-3				,						msg=f'''Max diff is {diff}'''   )







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Tuple:
								__a		 =       2_0
								__a		 =       model_class_name(_lowercase   )

								__a		 =       model.encode(inputs_dict['input_ids']   )

								__a ,				__a		 =       (
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

								__a		 =       jnp.concatenate(
								    [
								        decoder_attention_mask,
								        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
								    ]				,						axis=-1				,						)

								__a		 =       model.init_cache(decoder_input_ids.shape[0]				,						_lowercase				,						_lowercase   )
								__a		 =       jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]				,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,						)

								__a		 =       model.decode(
								    decoder_input_ids[:, :-1]				,						_lowercase				,						decoder_attention_mask=_lowercase				,						past_key_values=_lowercase				,						decoder_position_ids=_lowercase				,						)
								__a		 =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,						dtype='i4'   )
								__a		 =       model.decode(
								    decoder_input_ids[:, -1:]				,						_lowercase				,						past_key_values=outputs_cache.past_key_values				,						decoder_attention_mask=_lowercase				,						decoder_position_ids=_lowercase				,						)

								__a		 =       model.decode(_lowercase				,						_lowercase				,						decoder_attention_mask=_lowercase   )

								__a		 =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
								self.parent.assertTrue(diff < 1e-3				,						msg=f'''Max diff is {diff}'''   )






def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase=None				,    __lowerCamelCase=None				,    ):






					if attention_mask is None:
										__a		 =       np.not_equal(SCREAMING_SNAKE_CASE_				,    config.pad_token_id						).astype(np.inta						)
					if decoder_attention_mask is None:
										__a		 =       np.concatenate(
										    [
										        np.ones(decoder_input_ids[:, :1].shape				,    dtype=np.inta						),
										        np.not_equal(decoder_input_ids[:, 1:]				,    config.pad_token_id						).astype(np.inta						),
										    ]				,    axis=-1				,    )
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": decoder_attention_mask,
					}



@require_flax
class 						a__							(       __snake_case				,						unittest.TestCase		):
			A__    :       Dict        =      (
			    (
			        FlaxPegasusForConditionalGeneration,
			        FlaxPegasusModel,
			    )
			    if is_flax_available()
			    else ()
			)
			A__    :       Optional[int]        =      (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
			A__    :       List[str]        =      True
			A__    :       Any        =      False
			A__    :       Union[str, Any]        =      False
			A__    :       Tuple        =      False



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       FlaxPegasusModelTester(self   )
								__a		 =       ConfigTester(self				,						config_class=_lowercase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								self.config_tester.run_common_tests()



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a ,				__a		 =       self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(_lowercase				,						_lowercase				,						_lowercase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a ,				__a		 =       self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward_with_attn_mask(_lowercase				,						_lowercase				,						_lowercase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a ,				__a		 =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__   ):
																		__a		 =       self._prepare_for_class(_lowercase				,						_lowercase   )
																		__a		 =       model_class(_lowercase   )

																		@jax.jit
																		def encode_jitted(UpperCAmelCase				,						UpperCAmelCase=None				,						**UpperCAmelCase   ):
																							return model.encode(input_ids=_lowercase				,						attention_mask=_lowercase   )

																		with self.subTest('JIT Enabled'   ):
																							__a		 =       encode_jitted(**_lowercase   ).to_tuple()

																		with self.subTest('JIT Disabled'   ):
																							with jax.disable_jit():
																												__a		 =       encode_jitted(**_lowercase   ).to_tuple()

																		self.assertEqual(len(_lowercase   )				,						len(_lowercase   )   )
																		for jitted_output, output in zip(_lowercase				,						_lowercase   ):
																							self.assertEqual(jitted_output.shape				,						output.shape   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a ,				__a		 =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__   ):
																		__a		 =       model_class(_lowercase   )
																		__a		 =       model.encode(inputs_dict['input_ids']				,						inputs_dict['attention_mask']   )

																		__a		 =       {
																		    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																		    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																		    'encoder_outputs': encoder_outputs,
																		}

																		@jax.jit
																		def decode_jitted(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   ):
																							return model.decode(
																							    decoder_input_ids=_lowercase				,						decoder_attention_mask=_lowercase				,						encoder_outputs=_lowercase				,						)

																		with self.subTest('JIT Enabled'   ):
																							__a		 =       decode_jitted(**_lowercase   ).to_tuple()

																		with self.subTest('JIT Disabled'   ):
																							with jax.disable_jit():
																												__a		 =       decode_jitted(**_lowercase   ).to_tuple()

																		self.assertEqual(len(_lowercase   )				,						len(_lowercase   )   )
																		for jitted_output, output in zip(_lowercase				,						_lowercase   ):
																							self.assertEqual(jitted_output.shape				,						output.shape   )



			@slow
			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								for model_class_name in self.all_model_classes:
													__a		 =       model_class_name.from_pretrained('google/pegasus-large'				,						from_pt=_lowercase   )
													__a		 =       np.ones((1, 1)   )
													__a		 =       model(_lowercase   )
													self.assertIsNotNone(_lowercase   )







			@slow
			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'   )
								__a		 =       PegasusTokenizer.from_pretrained('google/pegasus-xsum'   )

								__a		 =       [
								    ' 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!\" ',
								]

								__a		 =       [
								    'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
								    'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
								]

								__a		 =       tokenizer(_lowercase				,						return_tensors='np'				,						truncation=_lowercase				,						max_length=5_1_2				,						padding=_lowercase   )
								__a		 =       model.generate(**_lowercase				,						num_beams=2   ).sequences
								__a		 =       tokenizer.batch_decode(_lowercase				,						skip_special_tokens=_lowercase   )
								assert tgt_text == decoded





import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np


class 						a__							:



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								torch.manual_seed(0   )
								__a		 =       TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       UNetaDConditionModel(
								    sample_size=3_2				,						layers_per_block=1				,						block_out_channels=[3_2, 6_4]				,						down_block_types=[
								        'ResnetDownsampleBlock2D',
								        'SimpleCrossAttnDownBlock2D',
								    ]				,						mid_block_type='UNetMidBlock2DSimpleCrossAttn'				,						up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']				,						in_channels=3				,						out_channels=6				,						cross_attention_dim=3_2				,						encoder_hid_dim=3_2				,						attention_head_dim=8				,						addition_embed_type='text'				,						addition_embed_type_num_heads=2				,						cross_attention_norm='group_norm'				,						resnet_time_scale_shift='scale_shift'				,						act_fn='gelu'				,						)
								unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								torch.manual_seed(0   )
								__a		 =       DDPMScheduler(
								    num_train_timesteps=1_0_0_0				,						beta_schedule='squaredcos_cap_v2'				,						beta_start=0.0_001				,						beta_end=0.02				,						thresholding=UpperCAmelCase				,						dynamic_thresholding_ratio=0.95				,						sample_max_value=1.0				,						prediction_type='epsilon'				,						variance_type='learned_range'				,						)

								torch.manual_seed(0   )
								__a		 =       IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								torch.manual_seed(0   )
								__a		 =       TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       UNetaDConditionModel(
								    sample_size=3_2				,						layers_per_block=[1, 2]				,						block_out_channels=[3_2, 6_4]				,						down_block_types=[
								        'ResnetDownsampleBlock2D',
								        'SimpleCrossAttnDownBlock2D',
								    ]				,						mid_block_type='UNetMidBlock2DSimpleCrossAttn'				,						up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']				,						in_channels=6				,						out_channels=6				,						cross_attention_dim=3_2				,						encoder_hid_dim=3_2				,						attention_head_dim=8				,						addition_embed_type='text'				,						addition_embed_type_num_heads=2				,						cross_attention_norm='group_norm'				,						resnet_time_scale_shift='scale_shift'				,						act_fn='gelu'				,						class_embed_type='timestep'				,						mid_block_scale_factor=1.414				,						time_embedding_act_fn='gelu'				,						time_embedding_dim=3_2				,						)
								unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								torch.manual_seed(0   )
								__a		 =       DDPMScheduler(
								    num_train_timesteps=1_0_0_0				,						beta_schedule='squaredcos_cap_v2'				,						beta_start=0.0_001				,						beta_end=0.02				,						thresholding=UpperCAmelCase				,						dynamic_thresholding_ratio=0.95				,						sample_max_value=1.0				,						prediction_type='epsilon'				,						variance_type='learned_range'				,						)

								torch.manual_seed(0   )
								__a		 =       DDPMScheduler(
								    num_train_timesteps=1_0_0_0				,						beta_schedule='squaredcos_cap_v2'				,						beta_start=0.0_001				,						beta_end=0.02				,						)

								torch.manual_seed(0   )
								__a		 =       IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "image_noising_scheduler": image_noising_scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}



			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       self.get_dummy_components()
								__a		 =       self.pipeline_class(**UpperCAmelCase   )
								pipe.to(UpperCAmelCase   )
								pipe.set_progress_bar_config(disable=UpperCAmelCase   )

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )

								__a		 =       inputs['prompt']
								__a		 =       inputs['generator']
								__a		 =       inputs['num_inference_steps']
								__a		 =       inputs['output_type']

								if "image" in inputs:
													__a		 =       inputs['image']
								else:
													__a		 =       None

								if "mask_image" in inputs:
													__a		 =       inputs['mask_image']
								else:
													__a		 =       None

								if "original_image" in inputs:
													__a		 =       inputs['original_image']
								else:
													__a		 =       None

								__a ,				__a		 =       pipe.encode_prompt(UpperCAmelCase   )

								# inputs with prompt converted to embeddings
								__a		 =       {
								    'prompt_embeds': prompt_embeds,
								    'negative_prompt_embeds': negative_prompt_embeds,
								    'generator': generator,
								    'num_inference_steps': num_inference_steps,
								    'output_type': output_type,
								}

								if image is not None:
													__a		 =       image

								if mask_image is not None:
													__a		 =       mask_image

								if original_image is not None:
													__a		 =       original_image

								# set all optional components to None
								for optional_component in pipe._optional_components:
													setattr(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )

								__a		 =       pipe(**UpperCAmelCase   )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(UpperCAmelCase   )
													__a		 =       self.pipeline_class.from_pretrained(UpperCAmelCase   )
													pipe_loaded.to(UpperCAmelCase   )
													pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase   )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								for optional_component in pipe._optional_components:
													self.assertTrue(
													    getattr(UpperCAmelCase				,						UpperCAmelCase   ) is None				,						f'''`{optional_component}` did not stay set to None after loading.'''				,						)

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )

								__a		 =       inputs['generator']
								__a		 =       inputs['num_inference_steps']
								__a		 =       inputs['output_type']

								# inputs with prompt converted to embeddings
								__a		 =       {
								    'prompt_embeds': prompt_embeds,
								    'negative_prompt_embeds': negative_prompt_embeds,
								    'generator': generator,
								    'num_inference_steps': num_inference_steps,
								    'output_type': output_type,
								}

								if image is not None:
													__a		 =       image

								if mask_image is not None:
													__a		 =       mask_image

								if original_image is not None:
													__a		 =       original_image

								__a		 =       pipe_loaded(**UpperCAmelCase   )[0]

								__a		 =       np.abs(to_np(UpperCAmelCase   ) - to_np(UpperCAmelCase   )   ).max()
								self.assertLess(UpperCAmelCase				,						1e-4   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       self.get_dummy_components()
								__a		 =       self.pipeline_class(**UpperCAmelCase   )
								pipe.to(UpperCAmelCase   )
								pipe.set_progress_bar_config(disable=UpperCAmelCase   )

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )
								__a		 =       pipe(**UpperCAmelCase   )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(UpperCAmelCase   )
													__a		 =       self.pipeline_class.from_pretrained(UpperCAmelCase   )
													pipe_loaded.to(UpperCAmelCase   )
													pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase   )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )
								__a		 =       pipe_loaded(**UpperCAmelCase   )[0]

								__a		 =       np.abs(to_np(UpperCAmelCase   ) - to_np(UpperCAmelCase   )   ).max()
								self.assertLess(UpperCAmelCase				,						1e-4   )




