





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

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


_lowerCAmelCase							:      Dict						=	logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCAmelCase							:      Dict						=	'''
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif

        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        >>> repo = "openai/shap-e"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 15.0
        >>> prompt = "a shark"

        >>> images = pipe(
        ...     prompt,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")
        ```
'''


@dataclass
class 					__magic_name__  (							lowerCamelCase__							):
     __UpperCamelCase					    =     42




class 					__magic_name__  (							lowerCamelCase__							):



     def __init__(							self       :Union[str, Any]			,      snake_case       :PriorTransformer			,      snake_case       :CLIPTextModelWithProjection			,      snake_case       :CLIPTokenizer			,      snake_case       :HeunDiscreteScheduler			,      snake_case       :ShapERenderer			,      ):





        '''simple docstring'''




        super().__init__()

        self.register_modules(
            prior=snake_case			,      text_encoder=snake_case			,      tokenizer=snake_case			,      scheduler=snake_case			,      renderer=snake_case			,      )



     def 						SCREAMING_SNAKE_CASE		(							self       :Any			,      snake_case       :Optional[int]			,      snake_case       :Optional[int]			,      snake_case       :Dict			,      snake_case       :int			,      snake_case       :List[Any]			,      snake_case       :Dict  ):





        '''simple docstring'''




        if latents is None:
           A_					:				Union[str, Any]          =			randn_tensor(snake_case			,      generator=snake_case			,      device=snake_case			,      dtype=snake_case  )
        else:
           if latents.shape != shape:
              raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"  )
           A_					:				Dict          =			latents.to(snake_case  )

        A_					:				Optional[int]          =			latents * scheduler.init_noise_sigma
        return latents



     def 						SCREAMING_SNAKE_CASE		(							self       :str			,      snake_case       :Union[str, Any]=0  ):





        '''simple docstring'''




        if is_accelerate_available():
           from accelerate import cpu_offload
        else:
           raise ImportError("Please install accelerate via `pip install accelerate`"  )

        A_					:				Optional[int]          =			torch.device(f"cuda:{gpu_id}"  )

        A_					:				int          =			[self.text_encoder, self.prior]
        for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
              cpu_offload(snake_case			,      snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :str			,      snake_case       :str=0  ):





        '''simple docstring'''




        if is_accelerate_available() and is_accelerate_version(">="			,      "0.17.0.dev0"  ):
           from accelerate import cpu_offload_with_hook
        else:
           raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."  )

        A_					:				Optional[int]          =			torch.device(f"cuda:{gpu_id}"  )

        if self.device.type != "cpu":
           self.to("cpu"			,      silence_dtype_warnings=snake_case  )
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        A_					:				Union[str, Any]          =			None
        for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
           A_					:				Optional[Any]          =			cpu_offload_with_hook(snake_case			,      snake_case			,      prev_module_hook=snake_case  )

        if self.safety_checker is not None:
           A_					:				Dict          =			cpu_offload_with_hook(self.safety_checker			,      snake_case			,      prev_module_hook=snake_case  )

        # We'll offload the last model manually.
        A_					:				str          =			hook



     @property
     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        if self.device != torch.device("meta"  ) or not hasattr(self.text_encoder			,      "_hf_hook"  ):
           return self.device
        for module in self.text_encoder.modules():
           if (
               hasattr(snake_case			,      "_hf_hook"  )
               and hasattr(module._hf_hook			,      "execution_device"  )
               and module._hf_hook.execution_device is not None
           ):
              return torch.device(module._hf_hook.execution_device  )
        return self.device



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict			,      snake_case       :str			,      snake_case       :Optional[Any]			,      snake_case       :int			,      snake_case       :Any			,      ):





        '''simple docstring'''




        len(snake_case  ) if isinstance(snake_case			,      snake_case  ) else 1

        # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
        A_					:				int          =			0
        # get prompt text embeddings
        A_					:				Optional[Any]          =			self.tokenizer(
            snake_case			,      padding="max_length"			,      max_length=self.tokenizer.model_max_length			,      truncation=snake_case			,      return_tensors="pt"			,      )
        A_					:				Optional[int]          =			text_inputs.input_ids
        A_					:				Optional[Any]          =			self.tokenizer(snake_case			,      padding="longest"			,      return_tensors="pt"  ).input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(snake_case			,      snake_case  ):
           A_					:				Tuple          =			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}"  )

        A_					:				Tuple          =			self.text_encoder(text_input_ids.to(snake_case  )  )
        A_					:				str          =			text_encoder_output.text_embeds

        A_					:				Dict          =			prompt_embeds.repeat_interleave(snake_case			,      dim=0  )
        # in Shap-E it normalize the prompt_embeds and then later rescale it
        A_					:				str          =			prompt_embeds / torch.linalg.norm(snake_case			,      dim=-1			,      keepdim=snake_case  )

        if do_classifier_free_guidance:
           A_					:				str          =			torch.zeros_like(snake_case  )

           # 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_					:				Optional[int]          =			torch.cat([negative_prompt_embeds, prompt_embeds]  )

        # Rescale the features to have unit variance
        A_					:				Dict          =			math.sqrt(prompt_embeds.shape[1]  ) * prompt_embeds

        return prompt_embeds





     @torch.no_grad()
     @replace_example_docstring(snake_case  )
     def __call__(							self       :Tuple			,      snake_case       :str			,      snake_case       :int = 1			,      snake_case       :int = 25			,      snake_case       :Optional[Union[torch.Generator, List[torch.Generator]]] = None			,      snake_case       :Optional[torch.FloatTensor] = None			,      snake_case       :float = 4.0			,      snake_case       :int = 64			,      snake_case       :Optional[str] = "pil"			,      snake_case       :bool = True			,      ):





        '''simple docstring'''




        if isinstance(snake_case			,      snake_case  ):
           A_					:				Union[str, Any]          =			1
        elif isinstance(snake_case			,      snake_case  ):
           A_					:				List[str]          =			len(snake_case  )
        else:
           raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(snake_case  )}"  )

        A_					:				Dict          =			self._execution_device

        A_					:				Dict          =			batch_size * num_images_per_prompt

        A_					:				Any          =			guidance_scale > 1.0
        A_					:				Optional[Any]          =			self._encode_prompt(snake_case			,      snake_case			,      snake_case			,      snake_case  )

        # prior

        self.scheduler.set_timesteps(snake_case			,      device=snake_case  )
        A_					:				Union[str, Any]          =			self.scheduler.timesteps

        A_					:				Optional[int]          =			self.prior.config.num_embeddings
        A_					:				Any          =			self.prior.config.embedding_dim

        A_					:				int          =			self.prepare_latents(
            (batch_size, num_embeddings * embedding_dim)			,      prompt_embeds.dtype			,      snake_case			,      snake_case			,      snake_case			,      self.scheduler			,      )

        # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
        A_					:				Union[str, Any]          =			latents.reshape(latents.shape[0]			,      snake_case			,      snake_case  )

        for i, t in enumerate(self.progress_bar(snake_case  )  ):
           # expand the latents if we are doing classifier free guidance
           A_					:				Optional[Any]          =			torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
           A_					:				List[Any]          =			self.scheduler.scale_model_input(snake_case			,      snake_case  )

           A_					:				Tuple          =			self.prior(
               snake_case			,      timestep=snake_case			,      proj_embedding=snake_case			,      ).predicted_image_embedding

           # remove the variance
           A_					:				Dict          =			noise_pred.split(
               scaled_model_input.shape[2]			,      dim=2  )  # batch_size, num_embeddings, embedding_dim

           if do_classifier_free_guidance is not None:
              A_					:				List[Any]          =			noise_pred.chunk(2  )
              A_					:				List[Any]          =			noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

           A_					:				Dict          =			self.scheduler.step(
               snake_case			,      timestep=snake_case			,      sample=snake_case			,      ).prev_sample

        if output_type == "latent":
           return ShapEPipelineOutput(images=snake_case  )

        A_					:				Optional[Any]          =			[]
        for i, latent in enumerate(snake_case  ):
           A_					:				List[str]          =			self.renderer.decode(
               latent[None, :]			,      snake_case			,      size=snake_case			,      ray_batch_size=4_096			,      n_coarse_samples=64			,      n_fine_samples=128			,      )
           images.append(snake_case  )

        A_					:				Optional[int]          =			torch.stack(snake_case  )

        if output_type not in ["np", "pil"]:
           raise ValueError(f"Only the output types `pil` and `np` are supported not output_type={output_type}"  )

        A_					:				List[str]          =			images.cpu().numpy()

        if output_type == "pil":
           A_					:				Tuple          =			[self.numpy_to_pil(snake_case  ) for image in images]

        # Offload last model to CPU
        if hasattr(self			,      "final_offload_hook"  ) and self.final_offload_hook is not None:
           self.final_offload_hook.offload()

        if not return_dict:
           return (images,)

        return ShapEPipelineOutput(images=snake_case  )







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
 _lowerCAmelCase							:      List[Any]						=	'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def      __snake_case						(					_lowerCAmelCase : List[str]				,				_lowerCAmelCase : int				,				_lowerCAmelCase : Dict=None				,				_lowerCAmelCase : Dict=None	) ->						Tuple:
   if attention_mask is None:
      A_					:				Tuple          =			np.where(input_ids != config.pad_token_id				,				1				,				0	)
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
   }


@require_flax
class 					__magic_name__  :





     """simple docstring"""



     def __init__(							self       :Dict			,      snake_case       :List[Any]			,      snake_case       :str=13			,      snake_case       :Dict=7			,      snake_case       :List[Any]=True			,      snake_case       :Dict=False			,      snake_case       :List[Any]=99			,      snake_case       :Dict=16			,      snake_case       :Optional[Any]=2			,      snake_case       :Tuple=4			,      snake_case       :List[Any]=4			,      snake_case       :List[Any]="gelu"			,      snake_case       :List[str]=0.1			,      snake_case       :Optional[int]=0.1			,      snake_case       :Union[str, Any]=20			,      snake_case       :Dict=2			,      snake_case       :Any=1			,      snake_case       :Tuple=0			,      snake_case       :Optional[Any]=16			,      snake_case       :Union[str, Any]=16			,      snake_case       :Optional[Any]=0.02			,      ):





        '''simple docstring'''




        A_					:				str          =			parent
        A_					:				Tuple          =			batch_size
        A_					:				Dict          =			seq_length
        A_					:				Any          =			is_training
        A_					:				Optional[Any]          =			use_labels
        A_					:				Optional[Any]          =			vocab_size
        A_					:				Optional[int]          =			hidden_size
        A_					:				Dict          =			num_hidden_layers
        A_					:				Dict          =			num_attention_heads
        A_					:				Tuple          =			intermediate_size
        A_					:				Tuple          =			hidden_act
        A_					:				Union[str, Any]          =			hidden_dropout_prob
        A_					:				Tuple          =			attention_probs_dropout_prob
        A_					:				Any          =			max_position_embeddings
        A_					:				Tuple          =			eos_token_id
        A_					:				Any          =			pad_token_id
        A_					:				Tuple          =			bos_token_id
        A_					:				str          =			embed_dim
        A_					:				List[str]          =			word_embed_proj_dim
        A_					:				Dict          =			initializer_range
        A_					:				Any          =			False



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]  ):





        '''simple docstring'''




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

        A_					:				Any          =			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=snake_case			,      word_embed_proj_dim=self.word_embed_proj_dim			,      initializer_range=self.initializer_range			,      use_cache=snake_case			,      )
        A_					:				List[str]          =			prepare_opt_inputs_dict(snake_case			,      snake_case  )
        return config, inputs_dict



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict  ):





        '''simple docstring'''




        A_      ,						A_					:				List[Any]          =			self.prepare_config_and_inputs()
        return config, inputs_dict



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]			,      snake_case       :Tuple			,      snake_case       :List[Any]			,      snake_case       :List[str]  ):





        '''simple docstring'''




        A_					:				int          =			20
        A_					:				int          =			model_class_name(snake_case  )

        A_					:				Any          =			inputs_dict["input_ids"]
        A_					:				Union[str, Any]          =			inputs_dict["attention_mask"]

        A_					:				List[Any]          =			model.init_cache(input_ids.shape[0]			,      snake_case  )
        A_					:				Dict          =			jnp.ones((input_ids.shape[0], max_length)			,      dtype="i4"  )

        A_					:				str          =			jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1  )[None, :]			,      (input_ids.shape[0], input_ids.shape[-1] - 1)			,      )
        A_					:				Union[str, Any]          =			model(
            input_ids[:, :-1]			,      attention_mask=snake_case			,      past_key_values=snake_case			,      position_ids=snake_case			,      )

        A_					:				Union[str, Any]          =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,      dtype="i4"  )
        A_					:				Dict          =			model(
            input_ids[:, -1:]			,      attention_mask=snake_case			,      past_key_values=outputs_cache.past_key_values			,      position_ids=snake_case			,      )

        A_					:				Union[str, Any]          =			model(snake_case  )

        A_					:				Dict          =			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       :Union[str, Any]			,      snake_case       :int			,      snake_case       :Dict			,      snake_case       :Any  ):





        '''simple docstring'''




        A_					:				Optional[int]          =			20
        A_					:				Any          =			model_class_name(snake_case  )

        A_      ,						A_					:				List[str]          =			(
            inputs_dict["input_ids"],
            inputs_dict["attention_mask"],
        )

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

        A_					:				Optional[int]          =			model.init_cache(input_ids.shape[0]			,      snake_case  )
        A_					:				Any          =			jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1  )[None, :]			,      (input_ids.shape[0], input_ids.shape[-1] - 1)			,      )

        A_					:				int          =			model(
            input_ids[:, :-1]			,      attention_mask=snake_case			,      past_key_values=snake_case			,      position_ids=snake_case			,      )
        A_					:				List[Any]          =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,      dtype="i4"  )
        A_					:				Union[str, Any]          =			model(
            input_ids[:, -1:]			,      past_key_values=outputs_cache.past_key_values			,      attention_mask=snake_case			,      position_ids=snake_case			,      )

        A_					:				str          =			model(snake_case			,      attention_mask=snake_case  )

        A_					:				List[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}"  )




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





     """simple docstring"""



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



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Tuple          =			FlaxOPTModelTester(self  )



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]  ):





        '''simple docstring'''




        A_      ,						A_					:				List[Any]          =			self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(snake_case			,      snake_case			,      snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        A_      ,						A_					:				Any          =			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(snake_case			,      snake_case			,      snake_case  )





     @slow
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        for model_class_name in self.all_model_classes:
           A_					:				Dict          =			model_class_name.from_pretrained("facebook/opt-125m"  )
           A_					:				str          =			np.ones((1, 1)  ) * model.config.eos_token_id
           A_					:				List[str]          =			model(snake_case  )
           self.assertIsNotNone(snake_case  )




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





     """simple docstring"""



     @slow
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]  ):





        '''simple docstring'''




        A_					:				str          =			FlaxOPTModel.from_pretrained("facebook/opt-350m"  )
        A_					:				int          =			jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  )
        A_					:				int          =			model(input_ids=snake_case  ).last_hidden_state
        A_					:				int          =			(1, 11, 512)
        self.assertEqual(output.shape			,      snake_case  )
        A_					:				List[Any]          =			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]			,      snake_case			,      atol=4e-2  )  )




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





     """simple docstring"""



     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        super().setUp()
        A_					:				Dict          =			"facebook/opt-350m"





     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Any          =			FlaxOPTForCausalLM.from_pretrained(self.path_model  )
        A_					:				List[Any]          =			GPTaTokenizer.from_pretrained(self.path_model  )

        A_					:				int          =			[
            "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_					:				Any          =			tokenizer(snake_case			,      return_tensors="jax"			,      padding=snake_case			,      add_special_tokens=snake_case  )
        A_					:				int          =			model(inputs.input_ids			,      attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
        A_					:				Dict          =			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(snake_case			,      snake_case			,      atol=4e-2  )  )

        A_					:				Any          =			jax.jit(snake_case  )
        A_					:				List[str]          =			model(inputs.input_ids			,      attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
        self.assertTrue(jnp.allclose(snake_case			,      snake_case			,      atol=4e-2  )  )




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





     """simple docstring"""



     @property
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        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       :int  ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			"facebook/opt-125m"

        A_					:				Optional[int]          =			[
            "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_					:				str          =			[]

        A_					:				str          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )
        A_					:				Any          =			GPTaTokenizer.from_pretrained(snake_case  )

        for prompt in self.prompts:
           A_					:				List[str]          =			tokenizer(snake_case			,      return_tensors="jax"  ).input_ids

           A_					:				List[Any]          =			model.generate(snake_case			,      max_length=10  )
           A_					:				Optional[int]          =			generated_ids[0]

           A_					:				str          =			tokenizer.batch_decode(snake_case			,      skip_special_tokens=snake_case  )
           predicted_outputs += generated_string

        self.assertListEqual(snake_case			,      snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple  ):





        '''simple docstring'''




        A_					:				Dict          =			"facebook/opt-350m"

        A_					:				int          =			[
            "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_					:				List[Any]          =			[]
        A_					:				Tuple          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )
        A_					:				Dict          =			GPTaTokenizer.from_pretrained(snake_case  )

        for prompt in self.prompts:
           A_					:				Optional[int]          =			tokenizer(snake_case			,      return_tensors="jax"  ).input_ids

           A_					:				Optional[int]          =			model.generate(snake_case			,      max_length=10  )
           A_					:				Tuple          =			generated_ids[0]

           A_					:				Tuple          =			tokenizer.batch_decode(snake_case			,      skip_special_tokens=snake_case  )
           predicted_outputs += generated_string

        self.assertListEqual(snake_case			,      snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict  ):





        '''simple docstring'''




        A_					:				str          =			"facebook/opt-125m"
        A_					:				Tuple          =			[
            "Today is a beautiful day and I want to thank",
            "In the city of Rome Canaver Canaver Canaver Canaver",
        ]
        A_					:				str          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )
        A_					:				Optional[int]          =			GPTaTokenizer.from_pretrained(snake_case  )
        A_					:				Optional[Any]          =			tokenizer(
            [
                "Today is a beautiful day and I want to",
                "In the city of",
            ]			,      return_tensors="jax"			,      padding=snake_case			,      )

        A_					:				Any          =			jax.jit(model.generate  )

        A_					:				Tuple          =			jit_generate(inputs["input_ids"]			,      attention_mask=inputs["attention_mask"]  ).sequences

        A_					:				Tuple          =			tokenizer.batch_decode(snake_case			,      skip_special_tokens=snake_case  )

        self.assertIsNotNone(snake_case			,      snake_case  )





     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        A_					:				Optional[int]          =			"facebook/opt-350m"

        A_					:				Any          =			GPTaTokenizer.from_pretrained(snake_case  )
        A_					:				int          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )

        A_					:				Tuple          =			"left"

        # use different length sentences to test batching
        A_					:				Tuple          =			[
            "Hello, my dog is a little",
            "Today, I",
        ]

        A_					:				List[Any]          =			tokenizer(snake_case			,      return_tensors="jax"			,      padding=snake_case  )
        A_					:				Any          =			inputs["input_ids"]

        A_					:				int          =			model.generate(input_ids=snake_case			,      attention_mask=inputs["attention_mask"]			,      trace=snake_case  )

        A_					:				Optional[int]          =			tokenizer(sentences[0]			,      return_tensors="jax"  ).input_ids
        A_					:				Dict          =			model.generate(input_ids=snake_case  )

        A_					:				int          =			inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
        A_					:				Union[str, Any]          =			tokenizer(sentences[1]			,      return_tensors="jax"  ).input_ids
        A_					:				Optional[int]          =			model.generate(input_ids=snake_case			,      max_length=model.config.max_length - num_paddings  )

        A_					:				List[Any]          =			tokenizer.batch_decode(outputs[0]			,      skip_special_tokens=snake_case  )
        A_					:				Dict          =			tokenizer.decode(output_non_padded[0][0]			,      skip_special_tokens=snake_case  )
        A_					:				Any          =			tokenizer.decode(output_padded[0][0]			,      skip_special_tokens=snake_case  )

        A_					:				Optional[Any]          =			[
            "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(snake_case			,      snake_case  )
        self.assertListEqual(snake_case			,      [non_padded_sentence, padded_sentence]  )
