



'''simple docstring'''





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
       import jax
       import jax.numpy as jnp







@slow
@require_flax
class     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     List[Any] ,     snake_case_					:     List[Any]						):
          return f"gaussian_noise_s={seed}_shape={'_'.join([str(snake_case_						) for s in shape]						)}.npy"




    def lowerCamelCase       (    self					:     Tuple						):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()




    def lowerCamelCase       (    self					:     Tuple ,     snake_case_					:     int=0 ,     snake_case_					:     int=(4, 4, 64, 64) ,     snake_case_					:     Any=False						):
          snake_case__  :    Any    						=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case__  :    Union[str, Any]    						=      jnp.array(load_hf_numpy(self.get_file_format(snake_case_ ,     snake_case_						)						) ,     dtype=snake_case_						)
          return image




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     Any=False ,     snake_case_					:     List[Any]="CompVis/stable-diffusion-v1-4"						):
          snake_case__  :    Union[str, Any]    						=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case__  :    Any    						=      """bf16""" if fpaa else None

          snake_case__						,				snake_case__  :    Tuple    						=      FlaxUNetaDConditionModel.from_pretrained(
              snake_case_ ,     subfolder="""unet""" ,     dtype=snake_case_ ,     revision=snake_case_						)
          return model, params




    def lowerCamelCase       (    self					:     List[str] ,     snake_case_					:     Optional[Any]=0 ,     snake_case_					:     Union[str, Any]=(4, 77, 768) ,     snake_case_					:     Any=False						):
          snake_case__  :    Any    						=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case__  :    Optional[int]    						=      jnp.array(load_hf_numpy(self.get_file_format(snake_case_ ,     snake_case_						)						) ,     dtype=snake_case_						)
          return hidden_states




    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
            [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
            [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
            [3, 1_000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
            # fmt: on
        ]						)
    def lowerCamelCase       (    self					:     Optional[int] ,     snake_case_					:     Optional[Any] ,     snake_case_					:     Optional[Any] ,     snake_case_					:     Tuple						):
          snake_case__						,				snake_case__  :    Tuple    						=      self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4""" ,     fpaa=snake_case_						)
          snake_case__  :    str    						=      self.get_latents(snake_case_ ,     fpaa=snake_case_						)
          snake_case__  :    Tuple    						=      self.get_encoder_hidden_states(snake_case_ ,     fpaa=snake_case_						)

          snake_case__  :    int    						=      model.apply(
              {"""params""": params} ,     snake_case_ ,     jnp.array(snake_case_ ,     dtype=jnp.intaa						) ,     encoder_hidden_states=snake_case_ ,     ).sample

          assert sample.shape == latents.shape

          snake_case__  :    Dict    						=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())						) ,     dtype=jnp.floataa						)
          snake_case__  :    str    						=      jnp.array(snake_case_ ,     dtype=jnp.floataa						)

          # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
          assert jnp.allclose(snake_case_ ,     snake_case_ ,     atol=1E-2						)




    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
            [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
            [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
            [3, 1_000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
            # fmt: on
        ]						)
    def lowerCamelCase       (    self					:     List[str] ,     snake_case_					:     Any ,     snake_case_					:     Any ,     snake_case_					:     int						):
          snake_case__						,				snake_case__  :    Tuple    						=      self.get_unet_model(model_id="""stabilityai/stable-diffusion-2""" ,     fpaa=snake_case_						)
          snake_case__  :    List[str]    						=      self.get_latents(snake_case_ ,     shape=(4, 4, 96, 96) ,     fpaa=snake_case_						)
          snake_case__  :    Optional[int]    						=      self.get_encoder_hidden_states(snake_case_ ,     shape=(4, 77, 1_024) ,     fpaa=snake_case_						)

          snake_case__  :    Any    						=      model.apply(
              {"""params""": params} ,     snake_case_ ,     jnp.array(snake_case_ ,     dtype=jnp.intaa						) ,     encoder_hidden_states=snake_case_ ,     ).sample

          assert sample.shape == latents.shape

          snake_case__  :    Optional[Any]    						=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())						) ,     dtype=jnp.floataa						)
          snake_case__  :    Union[str, Any]    						=      jnp.array(snake_case_ ,     dtype=jnp.floataa						)

          # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
          assert jnp.allclose(snake_case_ ,     snake_case_ ,     atol=1E-2						)





'''simple docstring'''





import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__a          =		logging.getLogger(__name__)







@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		field(default=_a		,		metadata={"help": "Whether to use SortishSampler or not."}	)
    lowercase					=		field(
        default=_a		,		metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}	)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `max_length` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `num_beams` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
        }		,		)




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    int    						=      super().to_dict()
          for k, v in d.items():
                if isinstance(snake_case_ ,     snake_case_						):
                      snake_case__  :    Optional[int]    						=      v.to_dict()
          return d
