





from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


_UpperCamelCase   :							Tuple     =	getLogger(__name__)


_UpperCamelCase   :							List[str]     =	4
_UpperCamelCase   :							Tuple     =	7

_UpperCamelCase   :							Dict     =	{
    """float16""": torch.floataa,
    """float32""": torch.floataa,
}
def 				__UpperCamelCase  (		snake_case     )  ->						str:


   '''simple docstring'''







   if isinstance(snake_case    ,	torch.Tensor     ):
      return image
   elif isinstance(snake_case    ,	PIL.Image.Image     ):
      __A				 =       [image]

   if isinstance(image[0]    ,	PIL.Image.Image     ):
      __A ,						__A				 =       image[0].size
      __A ,						__A				 =       (x - x % 6_4 for x in (w, h))  # resize to integer multiple of 32

      __A				 =       [np.array(i.resize((w, h)     )     )[None, :] for i in image]
      __A				 =       np.concatenate(snake_case    ,	axis=0     )
      __A				 =       np.array(snake_case     ).astype(np.floataa     ) / 255.0
      __A				 =       image.transpose(0    ,	3    ,	1    ,	2     )
      __A				 =       2.0 * image - 1.0
      __A				 =       torch.from_numpy(snake_case     )
   elif isinstance(image[0]    ,	torch.Tensor     ):
      __A				 =       torch.cat(snake_case    ,	dim=0     )

   return image







class 						_lowerCAmelCase(						lowerCAmelCase__):

  """simple docstring"""







  def __init__( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase = 3_50  ,    )-> List[Any]:
     super().__init__(
         vae=_UpperCAmelCase  ,    text_encoder=_UpperCAmelCase  ,    tokenizer=_UpperCAmelCase  ,    unet=_UpperCAmelCase  ,    low_res_scheduler=_UpperCAmelCase  ,    scheduler=_UpperCAmelCase  ,    safety_checker=_UpperCAmelCase  ,    feature_extractor=_UpperCAmelCase  ,    watermarker=_UpperCAmelCase  ,    max_noise_level=_UpperCAmelCase  ,    )

  def __call__( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase = 75  ,    UpperCAmelCase = 9.0  ,    UpperCAmelCase = 20  ,    UpperCAmelCase = None  ,    UpperCAmelCase = 1  ,    UpperCAmelCase = 0.0  ,    UpperCAmelCase = None  ,    UpperCAmelCase = None  ,    UpperCAmelCase = None  ,    UpperCAmelCase = None  ,    UpperCAmelCase = "pil"  ,    UpperCAmelCase = True  ,    UpperCAmelCase = None  ,    UpperCAmelCase = 1  ,    )-> Any:
     self.check_inputs(_UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase     )

     # 2. Define call parameters
     if prompt is not None and isinstance(_UpperCAmelCase  ,    _UpperCAmelCase     ):
        __A				 =       1
     elif prompt is not None and isinstance(_UpperCAmelCase  ,    _UpperCAmelCase     ):
        __A				 =       len(_UpperCAmelCase     )
     else:
        __A				 =       prompt_embeds.shape[0]

     __A				 =       self._execution_device
     # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
     # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
     # corresponds to doing no classifier free guidance.
     __A				 =       guidance_scale > 1.0

     # 3. Encode input prompt
     __A				 =       self._encode_prompt(
         _UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    prompt_embeds=_UpperCAmelCase  ,    negative_prompt_embeds=_UpperCAmelCase  ,    )

     __A				 =       ORT_TO_PT_TYPE[str(text_embeddings.dtype     )]

     # 4. Preprocess image
     __A				 =       preprocess(_UpperCAmelCase     )
     __A				 =       image.cpu()

     # 5. set timesteps
     self.scheduler.set_timesteps(_UpperCAmelCase  ,    device=_UpperCAmelCase     )
     __A				 =       self.scheduler.timesteps

     # 5. Add noise to image
     __A				 =       torch.tensor([noise_level]  ,    dtype=torch.long  ,    device=_UpperCAmelCase     )
     __A				 =       torch.randn(image.shape  ,    generator=_UpperCAmelCase  ,    device=_UpperCAmelCase  ,    dtype=_UpperCAmelCase     )
     __A				 =       self.low_res_scheduler.add_noise(_UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase     )

     __A				 =       2 if do_classifier_free_guidance else 1
     __A				 =       np.concatenate([image] * batch_multiplier * num_images_per_prompt     )
     __A				 =       np.concatenate([noise_level] * image.shape[0]     )

     # 6. Prepare latent variables
     __A ,						__A				 =       image.shape[2:]
     __A				 =       self.prepare_latents(
         batch_size * num_images_per_prompt  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    )

     # 7. Check that sizes of image and latents match
     __A				 =       image.shape[1]
     if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
        raise ValueError(
            '''Incorrect configuration settings! The config of `pipeline.unet` expects'''
            f" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"
            f" `num_channels_image`: {num_channels_image} "
            f" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"
            ''' `pipeline.unet` or your `image` input.'''     )

     # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
     __A				 =       self.prepare_extra_step_kwargs(_UpperCAmelCase  ,    _UpperCAmelCase     )

     __A				 =       next(
         (input.type for input in self.unet.model.get_inputs() if input.name == '''timestep''')  ,    '''tensor(float)'''     )
     __A				 =       ORT_TO_NP_TYPE[timestep_dtype]

     # 9. Denoising loop
     __A				 =       len(_UpperCAmelCase     ) - num_inference_steps * self.scheduler.order
     with self.progress_bar(total=_UpperCAmelCase     ) as progress_bar:
        for i, t in enumerate(_UpperCAmelCase     ):
           # expand the latents if we are doing classifier free guidance
           __A				 =       np.concatenate([latents] * 2     ) if do_classifier_free_guidance else latents

           # concat latents, mask, masked_image_latents in the channel dimension
           __A				 =       self.scheduler.scale_model_input(_UpperCAmelCase  ,    _UpperCAmelCase     )
           __A				 =       np.concatenate([latent_model_input, image]  ,    axis=1     )

           # timestep to tensor
           __A				 =       np.array([t]  ,    dtype=_UpperCAmelCase     )

           # predict the noise residual
           __A				 =       self.unet(
               sample=_UpperCAmelCase  ,    timestep=_UpperCAmelCase  ,    encoder_hidden_states=_UpperCAmelCase  ,    class_labels=noise_level.astype(np.intaa     )  ,    )[0]

           # perform guidance
           if do_classifier_free_guidance:
              __A ,						__A				 =       np.split(_UpperCAmelCase  ,    2     )
           __A				 =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

           # compute the previous noisy sample x_t -> x_t-1
           __A				 =       self.scheduler.step(
               torch.from_numpy(_UpperCAmelCase     )  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    **_UpperCAmelCase     ).prev_sample

           # call the callback, if provided
           if i == len(_UpperCAmelCase     ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
              progress_bar.update()
              if callback is not None and i % callback_steps == 0:
                 callback(_UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase     )

        # 10. Post-processing
     __A				 =       self.decode_latents(latents.float()     )

     # 11. Convert to PIL
     if output_type == "pil":
        __A				 =       self.numpy_to_pil(_UpperCAmelCase     )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> Union[str, Any]:
     __A				 =       1 / 0.08333 * latents
     __A				 =       self.vae(latent_sample=_UpperCAmelCase     )[0]
     __A				 =       np.clip(image / 2 + 0.5  ,    0  ,    1     )
     __A				 =       image.transpose((0, 2, 3, 1)     )
     return image





  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase = None  ,    UpperCAmelCase = None  ,    )-> Optional[int]:


     if prompt is not None and isinstance(_UpperCAmelCase  ,    _UpperCAmelCase     ):
        __A				 =       1
     elif prompt is not None and isinstance(_UpperCAmelCase  ,    _UpperCAmelCase     ):
        __A				 =       len(_UpperCAmelCase     )
     else:
        __A				 =       prompt_embeds.shape[0]

     if prompt_embeds is None:
        __A				 =       self.tokenizer(
            _UpperCAmelCase  ,    padding='''max_length'''  ,    max_length=self.tokenizer.model_max_length  ,    truncation=_UpperCAmelCase  ,    return_tensors='''pt'''  ,    )
        __A				 =       text_inputs.input_ids
        __A				 =       self.tokenizer(_UpperCAmelCase  ,    padding='''longest'''  ,    return_tensors='''pt'''     ).input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
            _UpperCAmelCase  ,    _UpperCAmelCase     ):
           __A				 =       self.tokenizer.batch_decode(
               untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]     )
           logger.warning(
               '''The following part of your input was truncated because CLIP can only handle sequences up to'''
               f" {self.tokenizer.model_max_length} tokens: {removed_text}"     )

        # no positional arguments to text_encoder
        __A				 =       self.text_encoder(
            input_ids=text_input_ids.int().to(_UpperCAmelCase     )  ,    )
        __A				 =       prompt_embeds[0]

     __A ,						__A ,						__A				 =       prompt_embeds.shape
     # duplicate text embeddings for each generation per prompt, using mps friendly method
     __A				 =       prompt_embeds.repeat(1  ,    _UpperCAmelCase     )
     __A				 =       prompt_embeds.reshape(bs_embed * num_images_per_prompt  ,    _UpperCAmelCase  ,    -1     )

     # get unconditional embeddings for classifier free guidance
     if do_classifier_free_guidance and negative_prompt_embeds is None:
        __A				 =       42
        if negative_prompt is None:
           __A				 =       [''''''] * batch_size
        elif type(_UpperCAmelCase     ) is not type(_UpperCAmelCase     ):
           raise TypeError(
               f"`negative_prompt` should be the same type to `prompt`, but got {type(_UpperCAmelCase     )} !="
               f" {type(_UpperCAmelCase     )}."     )
        elif isinstance(_UpperCAmelCase  ,    _UpperCAmelCase     ):
           __A				 =       [negative_prompt]
        elif batch_size != len(_UpperCAmelCase     ):
           raise ValueError(
               f"`negative_prompt`: {negative_prompt} has batch size {len(_UpperCAmelCase     )}, but `prompt`:"
               f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
               ''' the batch size of `prompt`.'''     )
        else:
           __A				 =       negative_prompt

        __A				 =       text_input_ids.shape[-1]
        __A				 =       self.tokenizer(
            _UpperCAmelCase  ,    padding='''max_length'''  ,    max_length=_UpperCAmelCase  ,    truncation=_UpperCAmelCase  ,    return_tensors='''pt'''  ,    )

        # if hasattr(uncond_input, "attention_mask"):
        #     attention_mask = uncond_input.attention_mask.to(device)
        # else:
        #     attention_mask = None

        __A				 =       self.text_encoder(
            input_ids=uncond_input.input_ids.int().to(_UpperCAmelCase     )  ,    )
        __A				 =       uncond_embeddings[0]

     if do_classifier_free_guidance:
        __A				 =       uncond_embeddings.shape[1]
        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        __A				 =       uncond_embeddings.repeat(1  ,    _UpperCAmelCase     )
        __A				 =       uncond_embeddings.reshape(batch_size * num_images_per_prompt  ,    _UpperCAmelCase  ,    -1     )

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        __A				 =       np.concatenate([uncond_embeddings, prompt_embeds]     )

     return prompt_embeds








import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, 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
    _UpperCamelCase   :							Dict     =	"""platform"""

    import jax
    import jax.numpy as jnp

    from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case=None    ,	snake_case=None     )  ->						str:


   '''simple docstring'''







   if attention_mask is None:
      __A				 =       np.where(input_ids != config.pad_token_id    ,	1    ,	0     )
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
   }







@require_flax
class 						_lowerCAmelCase:

  """simple docstring"""







  def __init__( self  ,    UpperCAmelCase  ,    UpperCAmelCase=13  ,    UpperCAmelCase=7  ,    UpperCAmelCase=True  ,    UpperCAmelCase=False  ,    UpperCAmelCase=99  ,    UpperCAmelCase=16  ,    UpperCAmelCase=2  ,    UpperCAmelCase=4  ,    UpperCAmelCase=4  ,    UpperCAmelCase="gelu"  ,    UpperCAmelCase=0.1  ,    UpperCAmelCase=0.1  ,    UpperCAmelCase=20  ,    UpperCAmelCase=2  ,    UpperCAmelCase=1  ,    UpperCAmelCase=0  ,    UpperCAmelCase=16  ,    UpperCAmelCase=16  ,    UpperCAmelCase=0.02  ,    )-> List[str]:
     __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_act
     __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
     __A				 =       embed_dim
     __A				 =       word_embed_proj_dim
     __A				 =       initializer_range
     __A				 =       False

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A				 =       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,    self.vocab_size     )  ,    3  ,    self.vocab_size     )
     __A				 =       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,    dtype=np.intaa     ))  ,    -1     )

     __A				 =       OPTConfig(
         vocab_size=self.vocab_size  ,    hidden_size=self.hidden_size  ,    num_hidden_layers=self.num_hidden_layers  ,    num_attention_heads=self.num_attention_heads  ,    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  ,    embed_dim=self.embed_dim  ,    is_encoder_decoder=UpperCAmelCase  ,    word_embed_proj_dim=self.word_embed_proj_dim  ,    initializer_range=self.initializer_range  ,    use_cache=UpperCAmelCase  ,    )
     __A				 =       prepare_opt_inputs_dict(UpperCAmelCase  ,    UpperCAmelCase     )
     return config, inputs_dict

  def 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     __A ,						__A				 =       self.prepare_config_and_inputs()
     return config, inputs_dict

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> Any:
     __A				 =       20
     __A				 =       model_class_name(UpperCAmelCase     )

     __A				 =       inputs_dict['''input_ids''']
     __A				 =       inputs_dict['''attention_mask''']

     __A				 =       model.init_cache(input_ids.shape[0]  ,    UpperCAmelCase     )
     __A				 =       jnp.ones((input_ids.shape[0], max_length)  ,    dtype='''i4'''     )

     __A				 =       jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1     )[None, :]  ,    (input_ids.shape[0], input_ids.shape[-1] - 1)  ,    )
     __A				 =       model(
         input_ids[:, :-1]  ,    attention_mask=UpperCAmelCase  ,    past_key_values=UpperCAmelCase  ,    position_ids=UpperCAmelCase  ,    )

     __A				 =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,    dtype='''i4'''     )
     __A				 =       model(
         input_ids[:, -1:]  ,    attention_mask=UpperCAmelCase  ,    past_key_values=outputs_cache.past_key_values  ,    position_ids=UpperCAmelCase  ,    )

     __A				 =       model(UpperCAmelCase     )

     __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     )-> str:
     __A				 =       20
     __A				 =       model_class_name(UpperCAmelCase     )

     __A ,						__A				 =       (
         inputs_dict['''input_ids'''],
         inputs_dict['''attention_mask'''],
     )

     __A				 =       jnp.concatenate(
         [
             attention_mask,
             jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])     ),
         ]  ,    axis=-1  ,    )

     __A				 =       model.init_cache(input_ids.shape[0]  ,    UpperCAmelCase     )
     __A				 =       jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1     )[None, :]  ,    (input_ids.shape[0], input_ids.shape[-1] - 1)  ,    )

     __A				 =       model(
         input_ids[:, :-1]  ,    attention_mask=UpperCAmelCase  ,    past_key_values=UpperCAmelCase  ,    position_ids=UpperCAmelCase  ,    )
     __A				 =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,    dtype='''i4'''     )
     __A				 =       model(
         input_ids[:, -1:]  ,    past_key_values=outputs_cache.past_key_values  ,    attention_mask=UpperCAmelCase  ,    position_ids=UpperCAmelCase  ,    )

     __A				 =       model(UpperCAmelCase  ,    attention_mask=UpperCAmelCase     )

     __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}"     )







@require_flax
class 						_lowerCAmelCase(						_a		,   unittest.TestCase		,   _a):

  """simple docstring"""







  lowerCamelCase__      					=   (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
  lowerCamelCase__      					=   () if is_flax_available() else ()

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     __A				 =       FlaxOPTModelTester(self     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     __A ,						__A				 =       self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     __A ,						__A				 =       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(UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase     )





  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
     for model_class_name in self.all_model_classes:
        __A				 =       model_class_name.from_pretrained('''facebook/opt-125m'''     )
        __A				 =       np.ones((1, 1)     ) * model.config.eos_token_id
        __A				 =       model(UpperCAmelCase     )
        self.assertIsNotNone(UpperCAmelCase     )







@require_sentencepiece
@require_flax
class 						_lowerCAmelCase(						unittest.TestCase):

  """simple docstring"""







  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Tuple:
     __A				 =       FlaxOPTModel.from_pretrained('''facebook/opt-350m'''     )
     __A				 =       jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]     )
     __A				 =       model(input_ids=UpperCAmelCase     ).last_hidden_state
     __A				 =       (1, 11, 5_12)
     self.assertEqual(output.shape  ,    UpperCAmelCase     )
     __A				 =       jnp.array(
         [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]     )
     self.assertTrue(jnp.allclose(output[:, :3, :3]  ,    UpperCAmelCase  ,    atol=4e-2     )     )







@require_flax
@slow
class 						_lowerCAmelCase(						unittest.TestCase):

  """simple docstring"""







  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[str]:
     super().setUp()
     __A				 =       '''facebook/opt-350m'''





  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       FlaxOPTForCausalLM.from_pretrained(self.path_model     )
     __A				 =       GPTaTokenizer.from_pretrained(self.path_model     )

     __A				 =       [
         '''Today is a beautiful day and I want to''',
         '''In the city of''',
         '''Paris is the capital of France and''',
         '''Computers and mobile phones have taken''',
     ]
     # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
     __A				 =       tokenizer(UpperCAmelCase  ,    return_tensors='''jax'''  ,    padding=UpperCAmelCase  ,    add_special_tokens=UpperCAmelCase     )
     __A				 =       model(inputs.input_ids  ,    attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
     __A				 =       jnp.array(
         [
             [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
             [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
             [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
             [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
         ]     )
     self.assertTrue(jnp.allclose(UpperCAmelCase  ,    UpperCAmelCase  ,    atol=4e-2     )     )

     __A				 =       jax.jit(UpperCAmelCase     )
     __A				 =       model(inputs.input_ids  ,    attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
     self.assertTrue(jnp.allclose(UpperCAmelCase  ,    UpperCAmelCase  ,    atol=4e-2     )     )







@require_flax
@slow
class 						_lowerCAmelCase(						unittest.TestCase):

  """simple docstring"""







  @property
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
     return [
         "Today is a beautiful day and I want",
         "In the city of",
         "Paris is the capital of France and",
         "Computers and mobile phones have taken",
     ]

  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     __A				 =       '''facebook/opt-125m'''

     __A				 =       [
         '''Today is a beautiful day and I want to''',
         '''In the city of New York, the city''',
         '''Paris is the capital of France and the capital''',
         '''Computers and mobile phones have taken over the''',
     ]

     __A				 =       []

     __A				 =       FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase     )
     __A				 =       GPTaTokenizer.from_pretrained(UpperCAmelCase     )

     for prompt in self.prompts:
        __A				 =       tokenizer(UpperCAmelCase  ,    return_tensors='''jax'''     ).input_ids

        __A				 =       model.generate(UpperCAmelCase  ,    max_length=10     )
        __A				 =       generated_ids[0]

        __A				 =       tokenizer.batch_decode(UpperCAmelCase  ,    skip_special_tokens=UpperCAmelCase     )
        predicted_outputs += generated_string

     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     __A				 =       '''facebook/opt-350m'''

     __A				 =       [
         '''Today is a beautiful day and I want to''',
         '''In the city of San Francisco, the city''',
         '''Paris is the capital of France and the capital''',
         '''Computers and mobile phones have taken over the''',
     ]

     __A				 =       []
     __A				 =       FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase     )
     __A				 =       GPTaTokenizer.from_pretrained(UpperCAmelCase     )

     for prompt in self.prompts:
        __A				 =       tokenizer(UpperCAmelCase  ,    return_tensors='''jax'''     ).input_ids

        __A				 =       model.generate(UpperCAmelCase  ,    max_length=10     )
        __A				 =       generated_ids[0]

        __A				 =       tokenizer.batch_decode(UpperCAmelCase  ,    skip_special_tokens=UpperCAmelCase     )
        predicted_outputs += generated_string

     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       '''facebook/opt-125m'''
     __A				 =       [
         '''Today is a beautiful day and I want to thank''',
         '''In the city of Rome Canaver Canaver Canaver Canaver''',
     ]
     __A				 =       FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase     )
     __A				 =       GPTaTokenizer.from_pretrained(UpperCAmelCase     )
     __A				 =       tokenizer(
         [
             '''Today is a beautiful day and I want to''',
             '''In the city of''',
         ]  ,    return_tensors='''jax'''  ,    padding=UpperCAmelCase  ,    )

     __A				 =       jax.jit(model.generate     )

     __A				 =       jit_generate(inputs['''input_ids''']  ,    attention_mask=inputs['''attention_mask''']     ).sequences

     __A				 =       tokenizer.batch_decode(UpperCAmelCase  ,    skip_special_tokens=UpperCAmelCase     )

     self.assertIsNotNone(UpperCAmelCase  ,    UpperCAmelCase     )





  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A				 =       '''facebook/opt-350m'''

     __A				 =       GPTaTokenizer.from_pretrained(UpperCAmelCase     )
     __A				 =       FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase     )

     __A				 =       '''left'''

     # use different length sentences to test batching
     __A				 =       [
         '''Hello, my dog is a little''',
         '''Today, I''',
     ]

     __A				 =       tokenizer(UpperCAmelCase  ,    return_tensors='''jax'''  ,    padding=UpperCAmelCase     )
     __A				 =       inputs['''input_ids''']

     __A				 =       model.generate(input_ids=UpperCAmelCase  ,    attention_mask=inputs['''attention_mask''']  ,    trace=UpperCAmelCase     )

     __A				 =       tokenizer(sentences[0]  ,    return_tensors='''jax'''     ).input_ids
     __A				 =       model.generate(input_ids=UpperCAmelCase     )

     __A				 =       inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
     __A				 =       tokenizer(sentences[1]  ,    return_tensors='''jax'''     ).input_ids
     __A				 =       model.generate(input_ids=UpperCAmelCase  ,    max_length=model.config.max_length - num_paddings     )

     __A				 =       tokenizer.batch_decode(outputs[0]  ,    skip_special_tokens=UpperCAmelCase     )
     __A				 =       tokenizer.decode(output_non_padded[0][0]  ,    skip_special_tokens=UpperCAmelCase     )
     __A				 =       tokenizer.decode(output_padded[0][0]  ,    skip_special_tokens=UpperCAmelCase     )

     __A				 =       [
         '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
         '''Today, I was in the middle of a conversation with a friend about the''',
     ]
     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )
     self.assertListEqual(UpperCAmelCase  ,    [non_padded_sentence, padded_sentence]     )

