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
      SCREAMING_SNAKE_CASE__		:       Dict       						=       "platform"
      import jax
      import jax.numpy as jnp
      import numpy as np

      from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class    snake_case       :
  lowercase_								=  PegasusConfig
  lowercase_								=  {}
  lowercase_								=  'gelu'




  def __init__(   self  :						Dict  ,			a_  :						Any  ,			a_  :						Tuple=13  ,			a_  :						Optional[Any]=7  ,			a_  :						List[Any]=True  ,			a_  :						Dict=False  ,			a_  :						Union[str, Any]=99  ,			a_  :						Dict=32  ,			a_  :						int=5  ,			a_  :						Optional[int]=4  ,			a_  :						Optional[int]=37  ,			a_  :						List[Any]=0.1  ,			a_  :						Any=0.1  ,			a_  :						Dict=20  ,			a_  :						List[str]=2  ,			a_  :						Optional[Any]=1  ,			a_  :						Optional[Any]=0  ,			)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		parent
       SCREAMING_SNAKE_CASE__					:					Tuple      =		batch_size
       SCREAMING_SNAKE_CASE__					:					str      =		seq_length
       SCREAMING_SNAKE_CASE__					:					int      =		is_training
       SCREAMING_SNAKE_CASE__					:					str      =		use_labels
       SCREAMING_SNAKE_CASE__					:					int      =		vocab_size
       SCREAMING_SNAKE_CASE__					:					Any      =		hidden_size
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		num_hidden_layers
       SCREAMING_SNAKE_CASE__					:					Any      =		num_attention_heads
       SCREAMING_SNAKE_CASE__					:					List[str]      =		intermediate_size

       SCREAMING_SNAKE_CASE__					:					str      =		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					:					Any      =		max_position_embeddings
       SCREAMING_SNAKE_CASE__					:					Dict      =		eos_token_id
       SCREAMING_SNAKE_CASE__					:					Dict      =		pad_token_id
       SCREAMING_SNAKE_CASE__					:					Any      =		bos_token_id




  def 						__lowercase(   self  :						Dict					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		ids_tensor([self.batch_size, self.seq_length - 1]  ,			self.vocab_size					).clip(3  ,			self.vocab_size					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		np.expand_dims(np.array([self.eos_token_id] * self.batch_size					)  ,			1					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		np.concatenate([input_ids, eos_tensor]  ,			axis=1					)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		ids_tensor([self.batch_size, self.seq_length]  ,			self.vocab_size					)

       SCREAMING_SNAKE_CASE__					:					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  ,			)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		prepare_pegasus_inputs_dict(a_  ,			a_  ,			a_					)
       return config, inputs_dict




  def 						__lowercase(   self  :						str  ,			a_  :						Any  ,			a_  :						str  ,			a_  :						str					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		20
       SCREAMING_SNAKE_CASE__					:					int      =		model_class_name(a_					)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		model.encode(inputs_dict['input_ids']					)

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					int      =		(
           inputs_dict['decoder_input_ids'],
           inputs_dict['decoder_attention_mask'],
       )

       SCREAMING_SNAKE_CASE__					:					List[str]      =		model.init_cache(decoder_input_ids.shape[0]  ,			a_  ,			a_					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,			dtype='i4'					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]  ,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,			)
       SCREAMING_SNAKE_CASE__					:					str      =		model.decode(
           decoder_input_ids[:, :-1]  ,			a_  ,			decoder_attention_mask=a_  ,			past_key_values=a_  ,			decoder_position_ids=a_  ,			)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,			dtype='i4'					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		model.decode(
           decoder_input_ids[:, -1:]  ,			a_  ,			decoder_attention_mask=a_  ,			past_key_values=outputs_cache.past_key_values  ,			decoder_position_ids=a_  ,			)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		model.decode(a_  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		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 						__lowercase(   self  :						List[str]  ,			a_  :						Union[str, Any]  ,			a_  :						Dict  ,			a_  :						Tuple					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		20
       SCREAMING_SNAKE_CASE__					:					str      =		model_class_name(a_					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		model.encode(inputs_dict['input_ids']					)

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Tuple      =		(
           inputs_dict['decoder_input_ids'],
           inputs_dict['decoder_attention_mask'],
       )

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

       SCREAMING_SNAKE_CASE__					:					str      =		model.init_cache(decoder_input_ids.shape[0]  ,			a_  ,			a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]  ,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,			)

       SCREAMING_SNAKE_CASE__					:					Dict      =		model.decode(
           decoder_input_ids[:, :-1]  ,			a_  ,			decoder_attention_mask=a_  ,			past_key_values=a_  ,			decoder_position_ids=a_  ,			)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,			dtype='i4'					)
       SCREAMING_SNAKE_CASE__					:					int      =		model.decode(
           decoder_input_ids[:, -1:]  ,			a_  ,			past_key_values=outputs_cache.past_key_values  ,			decoder_attention_mask=a_  ,			decoder_position_ids=a_  ,			)

       SCREAMING_SNAKE_CASE__					:					Any      =		model.decode(a_  ,			a_  ,			decoder_attention_mask=a_					)

       SCREAMING_SNAKE_CASE__					:					str      =		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      _a					(   lowercase__	:      Tuple  ,	lowercase__	:      Any  ,	lowercase__	:      Optional[int]  ,	lowercase__	:      str=None  ,	lowercase__	:      Union[str, Any]=None  ,	):







     '''simple docstring'''


     if attention_mask is None:
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		np.not_equal(lowercase__  ,	config.pad_token_id			).astype(np.inta			)
     if decoder_attention_mask is None:
          SCREAMING_SNAKE_CASE__					:					str      =		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    snake_case       (   UpperCamelCase_ ,    unittest.TestCase    ):
  lowercase_								=  (
      (
          FlaxPegasusForConditionalGeneration,
          FlaxPegasusModel,
      )
      if is_flax_available()
      else ()
  )
  lowercase_								=  (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
  lowercase_								=  True
  lowercase_								=  False
  lowercase_								=  False
  lowercase_								=  False




  def 						__lowercase(   self  :						Union[str, Any]					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		FlaxPegasusModelTester(self					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		ConfigTester(self  ,			config_class=a_					)




  def 						__lowercase(   self  :						Union[str, Any]					)->							int:




       """simple docstring"""
       self.config_tester.run_common_tests()




  def 						__lowercase(   self  :						List[str]					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Dict      =		self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(a_  ,			a_  ,			a_					)




  def 						__lowercase(   self  :						Optional[int]					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Tuple      =		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(a_  ,			a_  ,			a_					)




  def 						__lowercase(   self  :						Optional[int]					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					int      =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__					):
                 SCREAMING_SNAKE_CASE__					:					Tuple      =		self._prepare_for_class(a_  ,			a_					)
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		model_class(a_					)

                 @jax.jit
                 def encode_jitted(a_  :						Any  ,			a_  :						Any=None  ,			**a_  :						int					):
                      return model.encode(input_ids=a_  ,			attention_mask=a_					)

                 with self.subTest('JIT Enabled'					):
                      SCREAMING_SNAKE_CASE__					:					str      =		encode_jitted(**a_					).to_tuple()

                 with self.subTest('JIT Disabled'					):
                      with jax.disable_jit():
                           SCREAMING_SNAKE_CASE__					:					Dict      =		encode_jitted(**a_					).to_tuple()

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




  def 						__lowercase(   self  :						List[Any]					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__					):
                 SCREAMING_SNAKE_CASE__					:					int      =		model_class(a_					)
                 SCREAMING_SNAKE_CASE__					:					str      =		model.encode(inputs_dict['input_ids']  ,			inputs_dict['attention_mask']					)

                 SCREAMING_SNAKE_CASE__					:					List[str]      =		{
                     '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(a_  :						List[Any]  ,			a_  :						Dict  ,			a_  :						int					):
                      return model.decode(
                          decoder_input_ids=a_  ,			decoder_attention_mask=a_  ,			encoder_outputs=a_  ,			)

                 with self.subTest('JIT Enabled'					):
                      SCREAMING_SNAKE_CASE__					:					Any      =		decode_jitted(**a_					).to_tuple()

                 with self.subTest('JIT Disabled'					):
                      with jax.disable_jit():
                           SCREAMING_SNAKE_CASE__					:					List[Any]      =		decode_jitted(**a_					).to_tuple()

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




  @slow
  def 						__lowercase(   self  :						Tuple					)->							int:




       """simple docstring"""
       for model_class_name in self.all_model_classes:
            SCREAMING_SNAKE_CASE__					:					int      =		model_class_name.from_pretrained('google/pegasus-large'  ,			from_pt=a_					)
            SCREAMING_SNAKE_CASE__					:					Dict      =		np.ones((1, 1)					)
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		model(a_					)
            self.assertIsNotNone(a_					)




  @slow
  def 						__lowercase(   self  :						Any					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		PegasusTokenizer.from_pretrained('google/pegasus-xsum'					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		[
           ' 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!" ',
       ]

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		[
           '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.',
       ]

       SCREAMING_SNAKE_CASE__					:					int      =		tokenizer(a_  ,			return_tensors='np'  ,			truncation=a_  ,			max_length=512  ,			padding=a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		model.generate(**a_  ,			num_beams=2					).sequences
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		tokenizer.batch_decode(a_  ,			skip_special_tokens=a_					)
       assert tgt_text == decoded





from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)



@dataclass
class    a_ (			_a    ):
			a	:			Union[List[PIL.Image.Image], np.ndarray]
			a	:			Optional[List[bool]]


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .pipeline_cycle_diffusion import CycleDiffusionPipeline
		from .pipeline_stable_diffusion import StableDiffusionPipeline
		from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
		from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
		from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
		from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
		from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
		from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
		from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
		from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
		from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
		from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
		from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
		from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
		from .pipeline_stable_unclip import StableUnCLIPPipeline
		from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
		from .safety_checker import StableDiffusionSafetyChecker
		from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

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 StableDiffusionImageVariationPipeline
else:
		from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
		if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.26.0''')):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import (
		    StableDiffusionDepthaImgPipeline,
		    StableDiffusionDiffEditPipeline,
		    StableDiffusionPixaPixZeroPipeline,
		)
else:
		from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
		from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
		from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
		if not (
		    is_torch_available()
		    and is_transformers_available()
		    and is_k_diffusion_available()
		    and is_k_diffusion_version('''>=''', '''0.0.12''')
		):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
		from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
		if not (is_transformers_available() and is_onnx_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_onnx_objects import *  # noqa F403
else:
		from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
		from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
		from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
		from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
		from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
		import flax



		@flax.struct.dataclass
		class    a_ (			_a    ):
					a	:			np.ndarray
					a	:			List[bool]







		from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
		from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
		from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
		from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
		from .safety_checker_flax import FlaxStableDiffusionSafetyChecker