


import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   logging.get_logger(__name__)  # pylint: disable=invalid-name

class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , ):
         super().__init__()

         if safety_checker is None:
              logger.warning(
                  F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                  ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
                  ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
                  ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
                  ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
                  ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''   )

         self.register_modules(
             speech_model=__snake_case      , speech_processor=__snake_case      , vae=__snake_case      , text_encoder=__snake_case      , tokenizer=__snake_case      , unet=__snake_case      , scheduler=__snake_case      , feature_extractor=__snake_case      , )
    def 		a_      (	self      , __snake_case = "auto"   ):
         if slice_size == "auto":
              snake_case					=    self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(__snake_case   )
    def 		a_      (	self   ):
         self.enable_attention_slicing(__snake_case   )




    @torch.no_grad()
    def __call__(	self      , __snake_case      , __snake_case=1_6_0_0_0      , __snake_case = 5_1_2      , __snake_case = 5_1_2      , __snake_case = 5_0      , __snake_case = 7.5      , __snake_case = None      , __snake_case = 1      , __snake_case = 0.0      , __snake_case = None      , __snake_case = None      , __snake_case = "pil"      , __snake_case = True      , __snake_case = None      , __snake_case = 1      , **__snake_case      , ):
         snake_case					=    self.speech_processor.feature_extractor(
             __snake_case      , return_tensors='''pt'''      , sampling_rate=__snake_case   ).input_features.to(self.device   )
         snake_case					=    self.speech_model.generate(__snake_case      , max_length=4_8_0_0_0_0   )

         snake_case					=    self.speech_processor.tokenizer.batch_decode(__snake_case      , skip_special_tokens=__snake_case      , normalize=__snake_case   )[
             0
         ]

         if isinstance(__snake_case      , __snake_case   ):
              snake_case					=    1
         elif isinstance(__snake_case      , __snake_case   ):
              snake_case					=    len(__snake_case   )
         else:
              raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__snake_case   )}'''   )

         if height % 8 != 0 or width % 8 != 0:
              raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''   )

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(__snake_case      , __snake_case   ) or callback_steps <= 0)
         ):
              raise ValueError(
                  F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                  F''' {type(__snake_case   )}.'''   )

         # get prompt text embeddings
         snake_case					=    self.tokenizer(
             __snake_case      , padding='''max_length'''      , max_length=self.tokenizer.model_max_length      , return_tensors='''pt'''      , )
         snake_case					=    text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
              snake_case					=    self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
              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}'''   )
              snake_case					=    text_input_ids[:, : self.tokenizer.model_max_length]
         snake_case					=    self.text_encoder(text_input_ids.to(self.device   )   )[0]

         # duplicate text embeddings for each generation per prompt, using mps friendly method
         snake_case			,  snake_case			,  snake_case					=    text_embeddings.shape
         snake_case					=    text_embeddings.repeat(1      , __snake_case      , 1   )
         snake_case					=    text_embeddings.view(bs_embed * num_images_per_prompt      , __snake_case      , -1   )

         # 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.
         snake_case					=    guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
              snake_case					=    42
              if negative_prompt is None:
                   snake_case					=    [''''''] * batch_size
              elif type(__snake_case   ) is not type(__snake_case   ):
                   raise TypeError(
                       F'''`negative_prompt` should be the same type to `prompt`, but got {type(__snake_case   )} !='''
                       F''' {type(__snake_case   )}.'''   )
              elif isinstance(__snake_case      , __snake_case   ):
                   snake_case					=    [negative_prompt]
              elif batch_size != len(__snake_case   ):
                   raise ValueError(
                       F'''`negative_prompt`: {negative_prompt} has batch size {len(__snake_case   )}, but `prompt`:'''
                       F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                       ''' the batch size of `prompt`.'''   )
              else:
                   snake_case					=    negative_prompt

              snake_case					=    text_input_ids.shape[-1]
              snake_case					=    self.tokenizer(
                  __snake_case      , padding='''max_length'''      , max_length=__snake_case      , truncation=__snake_case      , return_tensors='''pt'''      , )
              snake_case					=    self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

              # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
              snake_case					=    uncond_embeddings.shape[1]
              snake_case					=    uncond_embeddings.repeat(1      , __snake_case      , 1   )
              snake_case					=    uncond_embeddings.view(batch_size * num_images_per_prompt      , __snake_case      , -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
              snake_case					=    torch.cat([uncond_embeddings, text_embeddings]   )

         # get the initial random noise unless the user supplied it

         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
         snake_case					=    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         snake_case					=    text_embeddings.dtype
         if latents is None:
              if self.device.type == "mps":
                   # randn does not exist on mps
                   snake_case					=    torch.randn(__snake_case      , generator=__snake_case      , device='''cpu'''      , dtype=__snake_case   ).to(
                       self.device   )
              else:
                   snake_case					=    torch.randn(__snake_case      , generator=__snake_case      , device=self.device      , dtype=__snake_case   )
         else:
              if latents.shape != latents_shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
              snake_case					=    latents.to(self.device   )

         # set timesteps
         self.scheduler.set_timesteps(__snake_case   )

         # Some schedulers like PNDM have timesteps as arrays
         # It's more optimized to move all timesteps to correct device beforehand
         snake_case					=    self.scheduler.timesteps.to(self.device   )

         # scale the initial noise by the standard deviation required by the scheduler
         snake_case					=    latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         snake_case					=    '''eta''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
         snake_case					=    {}
         if accepts_eta:
              snake_case					=    eta

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

              # predict the noise residual
              snake_case					=    self.unet(__snake_case      , __snake_case      , encoder_hidden_states=__snake_case   ).sample

              # perform guidance
              if do_classifier_free_guidance:
                   snake_case			,  snake_case					=    noise_pred.chunk(2   )
                   snake_case					=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

              # compute the previous noisy sample x_t -> x_t-1
              snake_case					=    self.scheduler.step(__snake_case      , __snake_case      , __snake_case      , **__snake_case   ).prev_sample

              # call the callback, if provided
              if callback is not None and i % callback_steps == 0:
                   callback(__snake_case      , __snake_case      , __snake_case   )

         snake_case					=    1 / 0.1_8215 * latents
         snake_case					=    self.vae.decode(__snake_case   ).sample

         snake_case					=    (image / 2 + 0.5).clamp(0      , 1   )

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         snake_case					=    image.cpu().permute(0      , 2      , 3      , 1   ).float().numpy()

         if output_type == "pil":
              snake_case					=    self.numpy_to_pil(__snake_case   )

         if not return_dict:
              return image

         return StableDiffusionPipelineOutput(images=__snake_case      , nsfw_content_detected=__snake_case   )








import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=1_3      , __snake_case=7      , __snake_case=True      , __snake_case=True      , __snake_case=True      , __snake_case=True      , __snake_case=9_9      , __snake_case=3_2      , __snake_case=5      , __snake_case=4      , __snake_case=4      , __snake_case="gelu"      , __snake_case=0.0      , __snake_case=0.1      , __snake_case=True      , __snake_case=5_1_2      , __snake_case=1_6      , __snake_case=2      , __snake_case=0.02      , __snake_case=3      , __snake_case=4      , __snake_case=None      , ):
         snake_case					=    parent
         snake_case					=    batch_size
         snake_case					=    seq_length
         snake_case					=    is_training
         snake_case					=    use_input_mask
         snake_case					=    use_token_type_ids
         snake_case					=    use_labels
         snake_case					=    vocab_size
         snake_case					=    hidden_size
         snake_case					=    num_hidden_layers
         snake_case					=    num_attention_heads
         snake_case					=    intermediate_multiple_size
         snake_case					=    hidden_act
         snake_case					=    hidden_dropout
         snake_case					=    attention_dropout
         snake_case					=    weight_tying
         snake_case					=    max_position_embeddings
         snake_case					=    type_vocab_size
         snake_case					=    type_sequence_label_size
         snake_case					=    initializer_range
         snake_case					=    num_labels
         snake_case					=    num_choices
         snake_case					=    scope
    def 		a_      (	self   ):
         snake_case					=    ids_tensor([self.batch_size, self.seq_length]      , self.vocab_size   )

         snake_case					=    None
         if self.use_input_mask:
              snake_case					=    random_attention_mask([self.batch_size, self.seq_length]   )

         snake_case					=    None
         if self.use_labels:
              snake_case					=    ids_tensor([self.batch_size, self.seq_length]      , self.num_labels   )

         snake_case					=    self.get_config()

         return config, input_ids, input_mask, token_labels
    def 		a_      (	self   ):
         return GPTNeoXJapaneseConfig(
             vocab_size=self.vocab_size      , hidden_size=self.hidden_size      , num_hidden_layers=self.num_hidden_layers      , num_attention_heads=self.num_attention_heads      , intermediate_multiple_size=self.intermediate_multiple_size      , hidden_act=self.hidden_act      , hidden_dropout=self.hidden_dropout      , attention_dropout=self.attention_dropout      , weight_tying=self.weight_tying      , max_position_embeddings=self.max_position_embeddings      , type_vocab_size=self.type_vocab_size      , is_decoder=__snake_case      , initializer_range=self.initializer_range      , )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.prepare_config_and_inputs()

         snake_case					=    True

         return config, input_ids, input_mask, token_labels
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    GPTNeoXJapaneseModel(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case   )
         snake_case					=    model(__snake_case   )
         self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    True
         snake_case					=    GPTNeoXJapaneseModel(__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case   )
         self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    GPTNeoXJapaneseForCausalLM(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , labels=__snake_case   )
         self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.vocab_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    True
         snake_case					=    GPTNeoXJapaneseForCausalLM(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()

         # first forward pass
         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , use_cache=__snake_case   )
         snake_case					=    outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         snake_case					=    ids_tensor((self.batch_size, 3)      , config.vocab_size   )
         snake_case					=    ids_tensor((self.batch_size, 3)      , vocab_size=2   )

         # append to next input_ids and
         snake_case					=    torch.cat([input_ids, next_tokens]      , dim=-1   )
         snake_case					=    torch.cat([input_mask, next_mask]      , dim=-1   )

         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , output_hidden_states=__snake_case   )
         snake_case					=    output_from_no_past['''hidden_states'''][0]
         snake_case					=    model(
             __snake_case      , attention_mask=__snake_case      , past_key_values=__snake_case      , output_hidden_states=__snake_case      , )['''hidden_states'''][0]

         # select random slice
         snake_case					=    ids_tensor((1,)      , output_from_past.shape[-1]   ).item()
         snake_case					=    output_from_no_past[:, -3:, random_slice_idx].detach()
         snake_case					=    output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(__snake_case      , __snake_case      , atol=1E-3   )   )




    def 		a_      (	self   ):
         snake_case					=    self.prepare_config_and_inputs()
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    config_and_inputs
         snake_case					=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict






@require_torch
class   A__ (   snake_case__	, snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
    __magic_name__        =		(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
    __magic_name__        =		(
        {'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
        if is_torch_available()
        else {}
    )
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    def 		a_      (	self   ):
         snake_case					=    GPTNeoXJapaneseModelTester(self   )
         snake_case					=    ConfigTester(self      , config_class=__snake_case      , hidden_size=3_7   )
    def 		a_      (	self   ):
         self.config_tester.run_common_tests()
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         # This regression test was failing with PyTorch < 1.3
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_decoder()

         snake_case					=    None

         self.model_tester.create_and_check_model_as_decoder(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*__snake_case   )




    @slow
    def 		a_      (	self   ):
         snake_case					=    '''abeja/gpt-neox-japanese-2.7b'''

         snake_case					=    ['''データサイエンティストとは、''', '''100年後に必要とされる会社は、''', '''フルリモートの環境で働くために必要なことは、''', '''国境の長いトンネルを抜けると''', '''美味しい日本食といえば、''']

         snake_case					=    [
             '''データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。''',
             '''100年後に必要とされる会社は、「人」が中心の会社です。''',
             '''フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。''',
             '''国境の長いトンネルを抜けると、そこは雪国だった。''',
             '''美味しい日本食といえば、やっぱりお寿司ですよね。''',
         ]

         snake_case					=    GPTNeoXJapaneseTokenizer.from_pretrained(__snake_case   )
         snake_case					=    GPTNeoXJapaneseForCausalLM.from_pretrained(__snake_case   )

         snake_case					=    []
         for prompt in prompts:
              snake_case					=    tokenizer(__snake_case      , return_tensors='''pt'''   ).input_ids
              snake_case					=    model.generate(__snake_case      , max_length=5_0   )
              snake_case					=    tokenizer.batch_decode(__snake_case      , skip_special_tokens=__snake_case   )
              predicted_outputs += generated_string
         self.assertListEqual(__snake_case      , __snake_case   )




