





'''simple docstring'''

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
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
   lowercase__		:     Any			=							'platform'

   import jax
   import jax.numpy as jnp

   from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
       FlaxBlenderbotSmallForConditionalGeneration,
       FlaxBlenderbotSmallModel,
       shift_tokens_right,
   )







def    a__							(							lowercase   :	Optional[Any],   lowercase   :	List[str],   lowercase   :	Tuple=None,   lowercase   :	Tuple=None,   lowercase   :	int=None,   lowercase   :	Optional[int]=None,   lowercase   :	Any=None,   lowercase   :	Optional[int]=None,   )						->						Any:





     """simple docstring"""

     if attention_mask is None:
          _UpperCamelCase							       =			np.where(input_ids != config.pad_token_id,   1,   0 )
     if decoder_attention_mask is None:
          _UpperCamelCase							       =			np.where(decoder_input_ids != config.pad_token_id,   1,   0 )
     if head_mask is None:
          _UpperCamelCase							       =			np.ones((config.encoder_layers, config.encoder_attention_heads) )
     if decoder_head_mask is None:
          _UpperCamelCase							       =			np.ones((config.decoder_layers, config.decoder_attention_heads) )
     if cross_attn_head_mask is None:
          _UpperCamelCase							       =			np.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": attention_mask,
     }
class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						str			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						Tuple=13			,					lowerCAmelCase__  :						Optional[Any]=7			,					lowerCAmelCase__  :						Optional[int]=True			,					lowerCAmelCase__  :						Tuple=False			,					lowerCAmelCase__  :						Dict=99			,					lowerCAmelCase__  :						Union[str, Any]=16			,					lowerCAmelCase__  :						Tuple=2			,					lowerCAmelCase__  :						Dict=4			,					lowerCAmelCase__  :						str=4			,					lowerCAmelCase__  :						List[str]="gelu"			,					lowerCAmelCase__  :						str=0.1			,					lowerCAmelCase__  :						str=0.1			,					lowerCAmelCase__  :						Any=32			,					lowerCAmelCase__  :						Dict=2			,					lowerCAmelCase__  :						List[Any]=1			,					lowerCAmelCase__  :						Any=0			,					lowerCAmelCase__  :						Dict=0.02			,					)       ->	int:

            '''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_act
            _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
            _UpperCamelCase							       =			initializer_range





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

            '''simple docstring'''




            _UpperCamelCase							       =			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,					self.vocab_size	)			,					3			,					self.vocab_size	)
            _UpperCamelCase							       =			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,					dtype=np.intaa	))			,					-1	)

            _UpperCamelCase							       =			shift_tokens_right(lowerCAmelCase__			,					1			,					2	)

            _UpperCamelCase							       =			BlenderbotSmallConfig(
                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_id=self.eos_token_id			,					bos_token_id=self.bos_token_id			,					pad_token_id=self.pad_token_id			,					initializer_range=self.initializer_range			,					use_cache=lowerCAmelCase__			,					)
            _UpperCamelCase							       =			prepare_blenderbot_inputs_dict(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)
            return config, inputs_dict





       def snake_case__		(				self  :						List[str]	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.prepare_config_and_inputs()
            return config, inputs_dict





       def snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						Any			,					lowerCAmelCase__  :						List[str]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			20
            _UpperCamelCase							       =			model_class_name(lowerCAmelCase__	)

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

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

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

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

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

            _UpperCamelCase							       =			model.decode(lowerCAmelCase__			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			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 snake_case__		(				self  :						Any			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						Optional[int]	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			20
            _UpperCamelCase							       =			model_class_name(lowerCAmelCase__	)

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

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

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

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

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

            _UpperCamelCase							       =			model.decode(lowerCAmelCase__			,					lowerCAmelCase__			,					decoder_attention_mask=lowerCAmelCase__	)

            _UpperCamelCase							       =			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}"""	)

@require_flax
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	List[Any]									=       9_9





       def snake_case__		(				self  :						Tuple	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			np.array(
                [
                    [71, 82, 18, 33, 46, 91, 2],
                    [68, 34, 26, 58, 30, 82, 2],
                    [5, 97, 17, 39, 94, 40, 2],
                    [76, 83, 94, 25, 70, 78, 2],
                    [87, 59, 41, 35, 48, 66, 2],
                    [55, 13, 16, 58, 5, 2, 1],  # note padding
                    [64, 27, 31, 51, 12, 75, 2],
                    [52, 64, 86, 17, 83, 39, 2],
                    [48, 61, 9, 24, 71, 82, 2],
                    [26, 1, 60, 48, 22, 13, 2],
                    [21, 5, 62, 28, 14, 76, 2],
                    [45, 98, 37, 86, 59, 48, 2],
                    [70, 70, 50, 9, 28, 0, 2],
                ]			,					dtype=np.intaa			,					)

            _UpperCamelCase							       =			input_ids.shape[0]
            _UpperCamelCase							       =			BlenderbotSmallConfig(
                vocab_size=self.vocab_size			,					d_model=24			,					encoder_layers=2			,					decoder_layers=2			,					encoder_attention_heads=2			,					decoder_attention_heads=2			,					encoder_ffn_dim=32			,					decoder_ffn_dim=32			,					max_position_embeddings=48			,					eos_token_id=2			,					pad_token_id=1			,					bos_token_id=0			,					)
            return config, input_ids, batch_size





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

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase							       =			self._get_config_and_data()
            _UpperCamelCase							       =			FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase__	)
            _UpperCamelCase							       =			lm_model(input_ids=lowerCAmelCase__	)
            _UpperCamelCase							       =			(batch_size, input_ids.shape[1], config.vocab_size)
            self.assertEqual(outputs['''logits'''].shape			,					lowerCAmelCase__	)





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

            '''simple docstring'''




            _UpperCamelCase							       =			BlenderbotSmallConfig(
                vocab_size=self.vocab_size			,					d_model=14			,					encoder_layers=2			,					decoder_layers=2			,					encoder_attention_heads=2			,					decoder_attention_heads=2			,					encoder_ffn_dim=8			,					decoder_ffn_dim=8			,					max_position_embeddings=48			,					)
            _UpperCamelCase							       =			FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase__	)
            _UpperCamelCase							       =			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,					dtype=np.intaa	)
            _UpperCamelCase							       =			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,					dtype=np.intaa	)
            _UpperCamelCase							       =			lm_model(input_ids=lowerCAmelCase__			,					decoder_input_ids=lowerCAmelCase__	)
            _UpperCamelCase							       =			(*summary.shape, config.vocab_size)
            self.assertEqual(outputs['''logits'''].shape			,					lowerCAmelCase__	)







       def snake_case__		(				self  :						Optional[int]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,					dtype=np.intaa	)
            _UpperCamelCase							       =			shift_tokens_right(lowerCAmelCase__			,					1			,					2	)
            _UpperCamelCase							       =			np.equal(lowerCAmelCase__			,					1	).astype(np.floataa	).sum()
            _UpperCamelCase							       =			np.equal(lowerCAmelCase__			,					1	).astype(np.floataa	).sum()
            self.assertEqual(shifted.shape			,					input_ids.shape	)
            self.assertEqual(lowerCAmelCase__			,					n_pad_before - 1	)
            self.assertTrue(np.equal(shifted[:, 0]			,					2	).all()	)

@require_flax
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase		,		__magic_name__					):






       """simple docstring"""
       _snake_case      :	List[Any]									=       True
       _snake_case      :	str									=       (
           (
               FlaxBlenderbotSmallModel,
               FlaxBlenderbotSmallForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       _snake_case      :	Union[str, Any]									=       (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()





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

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxBlenderbotSmallModelTester(self	)





       def snake_case__		(				self  :						Optional[Any]	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Optional[int]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						int	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__	):
                      _UpperCamelCase							       =			self._prepare_for_class(lowerCAmelCase__			,					lowerCAmelCase__	)
                      _UpperCamelCase							       =			model_class(lowerCAmelCase__	)

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

                      with self.subTest('''JIT Enabled'''	):
                           _UpperCamelCase							       =			encode_jitted(**lowerCAmelCase__	).to_tuple()

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

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





       def snake_case__		(				self  :						Any	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

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

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

                      with self.subTest('''JIT Enabled'''	):
                           _UpperCamelCase							       =			decode_jitted(**lowerCAmelCase__	).to_tuple()

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

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







       @slow
       def snake_case__		(				self  :						Union[str, Any]	)       ->	Optional[int]:

            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 _UpperCamelCase							       =			model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''	)
                 # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                 _UpperCamelCase							       =			np.ones((1, 1)	) * model.config.eos_token_id
                 _UpperCamelCase							       =			model(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)










'''simple docstring'''

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__		:     int			=							argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
lowercase__		:     Union[str, Any]			=							parser.parse_args()


lowercase__		:     Union[str, Any]			=							'cpu'
lowercase__		:     Tuple			=							'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

lowercase__		:     List[Any]			=							'path-to-your-trained-model'
lowercase__		:     Optional[Any]			=							StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
   lowercase__		:     Union[str, Any]			=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase__		:     Dict			=							pipe.to(device)

# to channels last
lowercase__		:     Any			=							pipe.unet.to(memory_format=torch.channels_last)
lowercase__		:     Dict			=							pipe.vae.to(memory_format=torch.channels_last)
lowercase__		:     Any			=							pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
   lowercase__		:     Union[str, Any]			=							pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowercase__		:     List[str]			=							torch.randn(2, 4, 64, 64)
lowercase__		:     int			=							torch.rand(1) * 9_99
lowercase__		:     Optional[int]			=							torch.randn(2, 77, 7_68)
lowercase__		:     List[str]			=							(sample, timestep, encoder_hidden_status)
try:
   lowercase__		:     Optional[Any]			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
   lowercase__		:     Any			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Tuple			=							ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Any			=							ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   lowercase__		:     Union[str, Any]			=							ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase__		:     Optional[Any]			=							6_66
lowercase__		:     Tuple			=							torch.Generator(device).manual_seed(seed)
lowercase__		:     Dict			=							{'generator': generator}
if args.steps is not None:
   lowercase__		:     int			=							args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
   lowercase__		:     Dict			=							pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('generated.png')



