


'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


A				:					List[Any]    						=			logging.get_logger(__name__)  # pylint: disable=invalid-name

A				:					Any    						=			'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      List[str]  ,lowerCamelCase					:      List[str]=8       ):
       _A					:     int     = height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       _A					:     List[Any]     = width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      int=512  ,lowerCamelCase					:      Optional[Any]=512       ):
       _A					:     Union[str, Any]     = pil_image.resize((w, h)  ,resample=Image.BICUBIC  ,reducing_gap=1       )
       _A					:     int     = np.array(pil_image.convert('RGB'       )       )
       _A					:     int     = arr.astype(np.floataa       ) / 127.5 - 1
       _A					:     Union[str, Any]     = np.transpose(lowerCamelCase  ,[2, 0, 1]       )
       _A					:     Optional[int]     = torch.from_numpy(lowerCamelCase       ).unsqueeze(0       )
       return image


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def __init__(  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							UNetaDConditionModel				,							SCREAMING_SNAKE_CASE      :							DDPMScheduler				,							SCREAMING_SNAKE_CASE      :							VQModel				,							):
           super().__init__()

           self.register_modules(
               unet=SCREAMING_SNAKE_CASE				,							scheduler=SCREAMING_SNAKE_CASE				,							movq=SCREAMING_SNAKE_CASE				,							)
           _A					:     Union[str, Any]     = 2 ** (len(self.movq.config.block_out_channels) - 1)


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int):
           # get the original timestep using init_timestep
           _A					:     str     = min(int(num_inference_steps * strength)				,							SCREAMING_SNAKE_CASE)

           _A					:     Union[str, Any]     = max(num_inference_steps - init_timestep				,							0)
           _A					:     int     = self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Tuple=None):
           if not isinstance(SCREAMING_SNAKE_CASE				,							(torch.Tensor, PIL.Image.Image, list)):
                  raise ValueError(
                      F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(SCREAMING_SNAKE_CASE)}')

           _A					:     List[str]     = image.to(device=SCREAMING_SNAKE_CASE				,							dtype=SCREAMING_SNAKE_CASE)

           _A					:     Union[str, Any]     = batch_size * num_images_per_prompt

           if image.shape[1] == 4:
                  _A					:     str     = image

           else:
                  if isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) and len(SCREAMING_SNAKE_CASE) != batch_size:
                         raise ValueError(
                             F'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE)}, but requested an effective batch'
                             F' size of {batch_size}. Make sure the batch size matches the length of the generators.')

                  elif isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                         _A					:     Tuple     = [
                             self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(SCREAMING_SNAKE_CASE)
                         ]
                         _A					:     int     = torch.cat(SCREAMING_SNAKE_CASE				,							dim=0)
                  else:
                         _A					:     Union[str, Any]     = self.movq.encode(SCREAMING_SNAKE_CASE).latent_dist.sample(SCREAMING_SNAKE_CASE)

                  _A					:     List[Any]     = self.movq.config.scaling_factor * init_latents

           _A					:     Any     = torch.cat([init_latents]				,							dim=0)

           _A					:     Union[str, Any]     = init_latents.shape
           _A					:     str     = randn_tensor(SCREAMING_SNAKE_CASE				,							generator=SCREAMING_SNAKE_CASE				,							device=SCREAMING_SNAKE_CASE				,							dtype=SCREAMING_SNAKE_CASE)

           # get latents
           _A					:     List[Any]     = self.scheduler.add_noise(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           _A					:     Optional[Any]     = init_latents

           return latents


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							int=0):
           if is_accelerate_available():
                  from accelerate import cpu_offload
           else:
                  raise ImportError('Please install accelerate via `pip install accelerate`')

           _A					:     Dict     = torch.device(F'cuda:{gpu_id}')

           _A					:     Optional[int]     = [
               self.unet,
               self.movq,
           ]
           for cpu_offloaded_model in models:
                  if cpu_offloaded_model is not None:
                         cpu_offload(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=0):
           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=SCREAMING_SNAKE_CASE)
                  torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

           _A					:     str     = None
           for cpu_offloaded_model in [self.unet, self.movq]:
                  _A  ,			_A					:     int     = cpu_offload_with_hook(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							prev_module_hook=SCREAMING_SNAKE_CASE)

           # We'll offload the last model manually.
           _A					:     List[Any]     = hook


    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def        A   (  self      :							Any):
           if not hasattr(self.unet				,							'_hf_hook'):
                  return self.device
           for module in self.unet.modules():
                  if (
                      hasattr(SCREAMING_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


    @torch.no_grad()
    @replace_example_docstring(SCREAMING_SNAKE_CASE)
    def __call__(  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Union[torch.FloatTensor, List[torch.FloatTensor]]				,							SCREAMING_SNAKE_CASE      :							Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]				,							SCREAMING_SNAKE_CASE      :							Union[torch.FloatTensor, List[torch.FloatTensor]]				,							SCREAMING_SNAKE_CASE      :							int = 512				,							SCREAMING_SNAKE_CASE      :							int = 512				,							SCREAMING_SNAKE_CASE      :							int = 100				,							SCREAMING_SNAKE_CASE      :							float = 4.0				,							SCREAMING_SNAKE_CASE      :							float = 0.3				,							SCREAMING_SNAKE_CASE      :							int = 1				,							SCREAMING_SNAKE_CASE      :							Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							SCREAMING_SNAKE_CASE      :							Optional[str] = "pil"				,							SCREAMING_SNAKE_CASE      :							bool = True				,							):
           _A					:     int     = self._execution_device

           _A					:     Optional[int]     = guidance_scale > 1.0

           if isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  _A					:     Tuple     = torch.cat(SCREAMING_SNAKE_CASE				,							dim=0)
           _A					:     Tuple     = image_embeds.shape[0]
           if isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  _A					:     List[str]     = torch.cat(SCREAMING_SNAKE_CASE				,							dim=0)

           if do_classifier_free_guidance:
                  _A					:     Any     = image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE				,							dim=0)
                  _A					:     Dict     = negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE				,							dim=0)

           _A					:     List[Any]     = torch.cat([negative_image_embeds, image_embeds]				,							dim=0).to(dtype=self.unet.dtype				,							device=SCREAMING_SNAKE_CASE)

           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  _A					:     List[Any]     = [image]
           if not all(isinstance(SCREAMING_SNAKE_CASE				,							(PIL.Image.Image, torch.Tensor)) for i in image):
                  raise ValueError(
                      F'Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE) for i in image]}. Currently, we only support  PIL image and pytorch tensor')

           _A					:     str     = torch.cat([prepare_image(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) for i in image]				,							dim=0)
           _A					:     List[Any]     = image.to(dtype=image_embeds.dtype				,							device=SCREAMING_SNAKE_CASE)

           _A					:     Optional[int]     = self.movq.encode(SCREAMING_SNAKE_CASE)['latents']
           _A					:     List[str]     = latents.repeat_interleave(SCREAMING_SNAKE_CASE				,							dim=0)
           self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE				,							device=SCREAMING_SNAKE_CASE)
           _A  ,			_A					:     Optional[Any]     = self.get_timesteps(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           _A					:     Tuple     = timesteps[:1].repeat(batch_size * num_images_per_prompt)
           _A  ,			_A					:     Tuple     = downscale_height_and_width(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							self.movq_scale_factor)
           _A					:     int     = self.prepare_latents(
               SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							image_embeds.dtype				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE)):
                  # expand the latents if we are doing classifier free guidance
                  _A					:     Dict     = torch.cat([latents] * 2) if do_classifier_free_guidance else latents

                  _A					:     List[str]     = {'image_embeds': image_embeds}
                  _A					:     List[str]     = self.unet(
                      sample=SCREAMING_SNAKE_CASE				,							timestep=SCREAMING_SNAKE_CASE				,							encoder_hidden_states=SCREAMING_SNAKE_CASE				,							added_cond_kwargs=SCREAMING_SNAKE_CASE				,							return_dict=SCREAMING_SNAKE_CASE				,							)[0]

                  if do_classifier_free_guidance:
                         _A  ,			_A					:     Tuple     = noise_pred.split(latents.shape[1]				,							dim=1)
                         _A  ,			_A					:     Union[str, Any]     = noise_pred.chunk(2)
                         _A  ,			_A					:     List[Any]     = variance_pred.chunk(2)
                         _A					:     Union[str, Any]     = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                         _A					:     List[Any]     = torch.cat([noise_pred, variance_pred_text]				,							dim=1)

                  if not (
                      hasattr(self.scheduler.config				,							'variance_type')
                      and self.scheduler.config.variance_type in ["learned", "learned_range"]
                  ):
                         _A  ,			_A					:     int     = noise_pred.split(latents.shape[1]				,							dim=1)

                  # compute the previous noisy sample x_t -> x_t-1
                  _A					:     Tuple     = self.scheduler.step(
                      SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							generator=SCREAMING_SNAKE_CASE				,							)[0]

           # post-processing
           _A					:     Any     = self.movq.decode(SCREAMING_SNAKE_CASE				,							force_not_quantize=SCREAMING_SNAKE_CASE)['sample']

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

           if output_type in ["np", "pil"]:
                  _A					:     int     = image * 0.5 + 0.5
                  _A					:     Union[str, Any]     = image.clamp(0				,							1)
                  _A					:     Union[str, Any]     = image.cpu().permute(0				,							2				,							3				,							1).float().numpy()

           if output_type == "pil":
                  _A					:     str     = self.numpy_to_pil(SCREAMING_SNAKE_CASE)

           if not return_dict:
                  return (image,)

           return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE)










'''simple docstring'''







import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]       ):
       if model.config.model_type == "gpt2":
              return model.transformer.h[0].mlp.c_fc
       return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
   import torch
   import torch.nn as nn


   class 			__lowerCamelCase			(	nn.Module	):
       """simple docstring"""


       def __init__(  self      :							int				,							SCREAMING_SNAKE_CASE      :							nn.Module				,							SCREAMING_SNAKE_CASE      :							int):
              super().__init__()
              _A					:     Tuple     = module
              _A					:     str     = nn.Sequential(
                  nn.Linear(module.in_features				,							SCREAMING_SNAKE_CASE				,							bias=SCREAMING_SNAKE_CASE)				,							nn.Linear(SCREAMING_SNAKE_CASE				,							module.out_features				,							bias=SCREAMING_SNAKE_CASE)				,							)
              _A					:     Any     = (2.0 / (5 * min(module.in_features				,							module.out_features))) ** 0.5
              nn.init.normal_(self.adapter[0].weight				,							std=SCREAMING_SNAKE_CASE)
              nn.init.zeros_(self.adapter[1].weight)
              self.adapter.to(module.weight.device)


       def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Any				,							*SCREAMING_SNAKE_CASE      :							List[str]				,							**SCREAMING_SNAKE_CASE      :							int):
              return self.module(SCREAMING_SNAKE_CASE				,							*SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE) + self.adapter(SCREAMING_SNAKE_CASE)


@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""
    # We keep the constants inside the init function and model loading inside setUp function

    # We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
    # Therefore here we use only bloom-1b3 to test our module
    a													=   "bigscience/bloom-1b7"

    # Constant values
    a													=   2.109_6595_5269_2574

    a													=   "Hello my name is"
    a													=   set()
    EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I"	)
    EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n"	)
    EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University"	)
    a													=   10


    def        A   (  self      :							List[Any]):
           # Models and tokenizer
           _A					:     List[Any]     = AutoTokenizer.from_pretrained(self.model_name)




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def        A   (  self      :							List[str]):
           super().setUp()

           # Models and tokenizer
           _A					:     Tuple     = AutoModelForCausalLM.from_pretrained(
               self.model_name				,							torch_dtype=torch.floataa				,							device_map='auto')
           _A					:     str     = AutoModelForCausalLM.from_pretrained(self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')


    def        A   (  self      :							List[Any]):
           del self.model_fpaa
           del self.model_abit

           gc.collect()
           torch.cuda.empty_cache()


    def        A   (  self      :							str):
           _A					:     Any     = self.model_abit.config

           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'quantization_config'))

           _A					:     str     = config.to_dict()
           _A					:     Dict     = config.to_diff_dict()

           _A					:     int     = config.to_json_string()


    def        A   (  self      :							List[str]):
           from bitsandbytes.nn import Paramsabit

           _A					:     List[str]     = self.model_fpaa.get_memory_footprint()
           _A					:     List[Any]     = self.model_abit.get_memory_footprint()

           self.assertAlmostEqual(mem_fpaa / mem_abit				,							self.EXPECTED_RELATIVE_DIFFERENCE)
           _A					:     str     = get_some_linear_layer(self.model_abit)
           self.assertTrue(linear.weight.__class__ == Paramsabit)


    def        A   (  self      :							List[Any]):
           from transformers import TaPreTrainedModel

           self.model_fpaa.get_memory_footprint()
           self.model_abit.get_memory_footprint()

           for name, module in self.model_abit.named_modules():
                  if isinstance(SCREAMING_SNAKE_CASE				,							torch.nn.Linear):
                         if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
                                # 4-bit parameters are packed in uint8 variables
                                self.assertTrue(module.weight.dtype == torch.uinta)


    def        A   (  self      :							Any):
           _A					:     List[str]     = self.tokenizer(self.input_text				,							return_tensors='pt')
           _A					:     Any     = self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0)				,							max_new_tokens=10)

           self.assertIn(self.tokenizer.decode(output_sequences[0]				,							skip_special_tokens=SCREAMING_SNAKE_CASE)				,							self.EXPECTED_OUTPUTS)


    def        A   (  self      :							List[str]):
           _A					:     List[Any]     = BitsAndBytesConfig()
           _A					:     Optional[int]     = True

           _A					:     Union[str, Any]     = AutoModelForCausalLM.from_pretrained(
               self.model_name				,							quantization_config=SCREAMING_SNAKE_CASE				,							device_map='auto')

           _A					:     int     = self.tokenizer(self.input_text				,							return_tensors='pt')
           _A					:     Any     = model_abit_from_config.generate(
               input_ids=encoded_input['input_ids'].to(0)				,							max_new_tokens=10)

           self.assertIn(self.tokenizer.decode(output_sequences[0]				,							skip_special_tokens=SCREAMING_SNAKE_CASE)				,							self.EXPECTED_OUTPUTS)


    def        A   (  self      :							Union[str, Any]):
           with self.assertRaises(SCREAMING_SNAKE_CASE), tempfile.TemporaryDirectory() as tmpdirname:
                  self.model_abit.save_pretrained(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           _A					:     Any     = BitsAndBytesConfig()

           with self.assertRaises(SCREAMING_SNAKE_CASE):
                  _A					:     Optional[Any]     = AutoModelForCausalLM.from_pretrained(
                      self.model_name				,							quantization_config=SCREAMING_SNAKE_CASE				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto'				,							bnb_abit_quant_type='nf4'				,							)


    def        A   (  self      :							Optional[Any]):
           with self.assertRaises(SCREAMING_SNAKE_CASE):
                  # Tries with `str`
                  self.model_abit.to('cpu')

           with self.assertRaises(SCREAMING_SNAKE_CASE):
                  # Tries with a `dtype``
                  self.model_abit.to(torch.floataa)

           with self.assertRaises(SCREAMING_SNAKE_CASE):
                  # Tries with a `device`
                  self.model_abit.to(torch.device('cuda:0'))

           with self.assertRaises(SCREAMING_SNAKE_CASE):
                  # Tries with a `device`
                  self.model_abit.float()

           with self.assertRaises(SCREAMING_SNAKE_CASE):
                  # Tries with a `device`
                  self.model_abit.half()

           # Test if we did not break anything
           _A					:     str     = self.tokenizer(self.input_text				,							return_tensors='pt')

           _A					:     Optional[Any]     = self.model_fpaa.to(torch.floataa)
           _A					:     Optional[int]     = self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0)				,							max_new_tokens=10)

           # Check this does not throw an error
           _A					:     Tuple     = self.model_fpaa.to('cpu')

           # Check this does not throw an error
           _A					:     Optional[int]     = self.model_fpaa.half()

           # Check this does not throw an error
           _A					:     int     = self.model_fpaa.float()


    def        A   (  self      :							Dict):
           _A					:     Any     = AutoModelForSeqaSeqLM.from_pretrained('t5-small'				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')
           self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa)




@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    @classmethod
    def        A   (  cls      :							Union[str, Any]):
           _A					:     Tuple     = 't5-small'
           _A					:     Union[str, Any]     = 'google/flan-t5-small'  # flan-t5 uses dense-act instead of dense-relu-dense
           _A					:     str     = AutoTokenizer.from_pretrained(cls.model_name)
           _A					:     Optional[int]     = 'Translate in German: Hello, my dog is cute'


    def        A   (  self      :							Any):
           gc.collect()
           torch.cuda.empty_cache()


    def        A   (  self      :							List[str]):
           from transformers import TaForConditionalGeneration

           _A					:     Union[str, Any]     = TaForConditionalGeneration._keep_in_fpaa_modules
           _A					:     List[str]     = None

           # test with `t5-small`
           _A					:     Optional[int]     = TaForConditionalGeneration.from_pretrained(self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')
           _A					:     int     = self.tokenizer(self.input_text				,							return_tensors='pt').to(0)
           _A					:     List[str]     = model.generate(**SCREAMING_SNAKE_CASE)

           # test with `flan-t5-small`
           _A					:     List[str]     = TaForConditionalGeneration.from_pretrained(
               self.dense_act_model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')
           _A					:     Dict     = self.tokenizer(self.input_text				,							return_tensors='pt').to(0)
           _A					:     Any     = model.generate(**SCREAMING_SNAKE_CASE)
           _A					:     List[str]     = modules


    def        A   (  self      :							Dict):
           import bitsandbytes as bnb

           from transformers import TaForConditionalGeneration

           # test with `t5-small`
           _A					:     str     = TaForConditionalGeneration.from_pretrained(self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')

           # there was a bug with decoders - this test checks that it is fixed
           self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q				,							bnb.nn.Linearabit))

           _A					:     int     = self.tokenizer(self.input_text				,							return_tensors='pt').to(0)
           _A					:     Union[str, Any]     = model.generate(**SCREAMING_SNAKE_CASE)

           # test with `flan-t5-small`
           _A					:     Union[str, Any]     = TaForConditionalGeneration.from_pretrained(
               self.dense_act_model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')
           _A					:     str     = self.tokenizer(self.input_text				,							return_tensors='pt').to(0)
           _A					:     Optional[int]     = model.generate(**SCREAMING_SNAKE_CASE)




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def        A   (  self      :							Union[str, Any]):
           super().setUp()
           # model_name
           _A					:     Dict     = 'bigscience/bloom-560m'
           _A					:     List[str]     = 't5-small'

           # Different types of model

           _A					:     Optional[Any]     = AutoModel.from_pretrained(self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')
           # Sequence classification model
           _A					:     Tuple     = AutoModelForSequenceClassification.from_pretrained(
               self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')
           # CausalLM model
           _A					:     List[Any]     = AutoModelForCausalLM.from_pretrained(self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')
           # Seq2seq model
           _A					:     Dict     = AutoModelForSeqaSeqLM.from_pretrained(
               self.seq_to_seq_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')


    def        A   (  self      :							str):
           del self.base_model
           del self.sequence_model
           del self.model_abit
           del self.seq_to_seq_model

           gc.collect()
           torch.cuda.empty_cache()


    def        A   (  self      :							Any):
           from bitsandbytes.nn import Paramsabit

           self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit)

           # Other heads should be nn.Parameter
           self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter)
           self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter)
           self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter)




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def        A   (  self      :							Optional[Any]):
           super().setUp()


    def        A   (  self      :							Any):
           del self.pipe

           gc.collect()
           torch.cuda.empty_cache()


    def        A   (  self      :							Optional[int]):
           _A					:     Dict     = pipeline(
               'text-generation'				,							model=self.model_name				,							model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa}				,							max_new_tokens=self.MAX_NEW_TOKENS				,							)

           # Real second forward pass
           _A					:     Union[str, Any]     = self.pipe(self.input_text)
           self.assertIn(pipeline_output[0]['generated_text']				,							self.EXPECTED_OUTPUTS)




@require_torch_multi_gpu
class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def        A   (  self      :							List[Any]):
           super().setUp()


    def        A   (  self      :							Any):
           _A					:     int     = AutoModelForCausalLM.from_pretrained(
               self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='balanced')

           # Check correct device map
           self.assertEqual(set(model_parallel.hf_device_map.values())				,							{0, 1})

           # Check that inference pass works on the model
           _A					:     Tuple     = self.tokenizer(self.input_text				,							return_tensors='pt')

           # Second real batch
           _A					:     Union[str, Any]     = model_parallel.generate(input_ids=encoded_input['input_ids'].to(0)				,							max_new_tokens=10)
           self.assertIn(self.tokenizer.decode(output_parallel[0]				,							skip_special_tokens=SCREAMING_SNAKE_CASE)				,							self.EXPECTED_OUTPUTS)




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def        A   (  self      :							Optional[Any]):
           _A					:     Any     = 'facebook/opt-350m'
           super().setUp()


    def        A   (  self      :							List[Any]):
           if version.parse(importlib.metadata.version('bitsandbytes')) < version.parse('0.37.0'):
                  return

           # Step 1: freeze all parameters
           _A					:     Any     = AutoModelForCausalLM.from_pretrained(self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE)

           self.assertEqual(set(model.hf_device_map.values())				,							{torch.cuda.current_device()})

           for param in model.parameters():
                  _A					:     Optional[Any]     = False  # freeze the model - train adapters later
                  if param.ndim == 1:
                         # cast the small parameters (e.g. layernorm) to fp32 for stability
                         _A					:     Tuple     = param.data.to(torch.floataa)

        # Step 2: add adapters
           for _, module in model.named_modules():
                  if "OPTAttention" in repr(type(SCREAMING_SNAKE_CASE)):
                         _A					:     List[Any]     = LoRALayer(module.q_proj				,							rank=16)
                         _A					:     Union[str, Any]     = LoRALayer(module.k_proj				,							rank=16)
                         _A					:     str     = LoRALayer(module.v_proj				,							rank=16)

        # Step 3: dummy batch
           _A					:     Union[str, Any]     = self.tokenizer('Test batch '				,							return_tensors='pt').to(0)

           # Step 4: Check if the gradient is not None
           with torch.cuda.amp.autocast():
                  _A					:     Union[str, Any]     = model.forward(**SCREAMING_SNAKE_CASE)
                  out.logits.norm().backward()

           for module in model.modules():
                  if isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                         self.assertTrue(module.adapter[1].weight.grad is not None)
                         self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0)
                  elif isinstance(SCREAMING_SNAKE_CASE				,							nn.Embedding):
                         self.assertTrue(module.weight.grad is None)




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   "gpt2-xl"
    a													=   3.3191_8548_5415_2187






