






"""simple docstring"""
import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


lowerCAmelCase__    						=			logging.get_logger(__name__)


class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    question_encoder
										_SCREAMING_SNAKE_CASE   :		int       		=    generator
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.question_encoder







						def 			A      (    self   ,				lowerCAmelCase_							)		->							int:
										if os.path.isfile(lowerCAmelCase_							):
														raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""							)
										os.makedirs(lowerCAmelCase_   ,				exist_ok=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    os.path.join(lowerCAmelCase_   ,				'question_encoder_tokenizer'							)
										_SCREAMING_SNAKE_CASE   :		str       		=    os.path.join(lowerCAmelCase_   ,				'generator_tokenizer'							)
										self.question_encoder.save_pretrained(lowerCAmelCase_							)
										self.generator.save_pretrained(lowerCAmelCase_							)







						@classmethod
						def 			A      (    cls   ,				lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							int:
										# dynamically import AutoTokenizer
										from ..auto.tokenization_auto import AutoTokenizer

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    kwargs.pop('config'   ,				lowerCAmelCase_							)

										if config is None:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    RagConfig.from_pretrained(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    AutoTokenizer.from_pretrained(
										    lowerCAmelCase_   ,				config=config.question_encoder   ,				subfolder='question_encoder_tokenizer'							)
										_SCREAMING_SNAKE_CASE   :		int       		=    AutoTokenizer.from_pretrained(
										    lowerCAmelCase_   ,				config=config.generator   ,				subfolder='generator_tokenizer'							)
										return cls(question_encoder=lowerCAmelCase_   ,				generator=lowerCAmelCase_							)







						def __call__(    self   ,				*lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							List[Any]:
										return self.current_tokenizer(*lowerCAmelCase_   ,				**lowerCAmelCase_							)







						def 			A      (    self   ,				*lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							Union[str, Any]:
										return self.generator.batch_decode(*lowerCAmelCase_   ,				**lowerCAmelCase_							)







						def 			A      (    self   ,				*lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							Optional[Any]:
										return self.generator.decode(*lowerCAmelCase_   ,				**lowerCAmelCase_							)







						def 			A      (    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.question_encoder







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.generator







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = "longest"   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = True   ,				**lowerCAmelCase_   ,				)		->							BatchEncoding:
										warnings.warn(
										    '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
										    'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
										    'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
										    'details'   ,				lowerCAmelCase_   ,				)
										if max_length is None:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    self.current_tokenizer.model_max_length
										_SCREAMING_SNAKE_CASE   :		int       		=    self(
										    lowerCAmelCase_   ,				add_special_tokens=lowerCAmelCase_   ,				return_tensors=lowerCAmelCase_   ,				max_length=lowerCAmelCase_   ,				padding=lowerCAmelCase_   ,				truncation=lowerCAmelCase_   ,				**lowerCAmelCase_   ,				)
										if tgt_texts is None:
														return model_inputs
										# Process tgt_texts
										if max_target_length is None:
														_SCREAMING_SNAKE_CASE   :		Any       		=    self.current_tokenizer.model_max_length
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self(
										    text_target=lowerCAmelCase_   ,				add_special_tokens=lowerCAmelCase_   ,				return_tensors=lowerCAmelCase_   ,				padding=lowerCAmelCase_   ,				max_length=lowerCAmelCase_   ,				truncation=lowerCAmelCase_   ,				**lowerCAmelCase_   ,				)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    labels['input_ids']
										return model_inputs









"""simple docstring"""
import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
			import jax
			import jax.numpy as jnp
			from flax.jax_utils import replicate
			from flax.training.common_utils import shard


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







						def 			A      (    self							)		->							Any:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    'stabilityai/stable-diffusion-2'   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		int       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		str       		=    'stabilityai/stable-diffusion-2'
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase_   ,				subfolder='scheduler'							)
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    scheduler_params

										_SCREAMING_SNAKE_CASE   :		int       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		int       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		str       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		str       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		int       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

