


"""simple docstring"""






from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)






@flax.struct.dataclass
class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							42






@flax_register_to_config
class      A(  nn.Module				,							lowerCamelCase__				,							lowerCamelCase__		):

		"""simple docstring"""





		A    =							32
		A    =							4
		A    =							4
		A    =							(
		    "CrossAttnDownBlock2D",
		    "CrossAttnDownBlock2D",
		    "CrossAttnDownBlock2D",
		    "DownBlock2D",
		)
		A    =							("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
		A    =							False
		A    =							(3_20, 6_40, 12_80, 12_80)
		A    =							2
		A    =							8
		A    =							None
		A    =							12_80
		A    =							0.0
		A    =							False
		A    =							jnp.floataa
		A    =							True
		A    =							0
		A    =							False
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	FrozenDict:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	(1, self.in_channels, self.sample_size, self.sample_size)
					_UpperCamelCase					:List[Any]					     =	jnp.zeros(SCREAMING_SNAKE_CASE__					,				dtype=jnp.floataa   )
					_UpperCamelCase					:Dict					     =	jnp.ones((1,)					,				dtype=jnp.intaa   )
					_UpperCamelCase					:Any					     =	jnp.zeros((1, 1, self.cross_attention_dim)					,				dtype=jnp.floataa   )

					_UpperCamelCase     ,      _UpperCamelCase					:List[Any]					     =	jax.random.split(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[str]					     =	{'''params''': params_rng, '''dropout''': dropout_rng}

					return self.init(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )["params"]
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	self.block_out_channels
					_UpperCamelCase					:str					     =	block_out_channels[0] * 4

					if self.num_attention_heads is not None:
								raise ValueError(
								    '''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.'''   )

					# If `num_attention_heads` is not defined (which is the case for most models)
					# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
					# The reason for this behavior is to correct for incorrectly named variables that were introduced
					# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
					# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
					# which is why we correct for the naming here.
					_UpperCamelCase					:Optional[Any]					     =	self.num_attention_heads or self.attention_head_dim

					# input
					_UpperCamelCase					:Optional[int]					     =	nn.Conv(
					    block_out_channels[0]					,				kernel_size=(3, 3)					,				strides=(1, 1)					,				padding=((1, 1), (1, 1))					,				dtype=self.dtype					,				)

					# time
					_UpperCamelCase					:List[Any]					     =	FlaxTimesteps(
					    block_out_channels[0]					,				flip_sin_to_cos=self.flip_sin_to_cos					,				freq_shift=self.config.freq_shift   )
					_UpperCamelCase					:Any					     =	FlaxTimestepEmbedding(SCREAMING_SNAKE_CASE__					,				dtype=self.dtype   )

					_UpperCamelCase					:Tuple					     =	self.only_cross_attention
					if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Optional[int]					     =	(only_cross_attention,) * len(self.down_block_types   )

					if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Dict					     =	(num_attention_heads,) * len(self.down_block_types   )

					# down
					_UpperCamelCase					:Any					     =	[]
					_UpperCamelCase					:str					     =	block_out_channels[0]
					for i, down_block_type in enumerate(self.down_block_types   ):
								_UpperCamelCase					:str					     =	output_channel
								_UpperCamelCase					:Dict					     =	block_out_channels[i]
								_UpperCamelCase					:Dict					     =	i == len(SCREAMING_SNAKE_CASE__   ) - 1

								if down_block_type == "CrossAttnDownBlock2D":
											_UpperCamelCase					:List[str]					     =	FlaxCrossAttnDownBlockaD(
											    in_channels=SCREAMING_SNAKE_CASE__					,				out_channels=SCREAMING_SNAKE_CASE__					,				dropout=self.dropout					,				num_layers=self.layers_per_block					,				num_attention_heads=num_attention_heads[i]					,				add_downsample=not is_final_block					,				use_linear_projection=self.use_linear_projection					,				only_cross_attention=only_cross_attention[i]					,				use_memory_efficient_attention=self.use_memory_efficient_attention					,				dtype=self.dtype					,				)
								else:
											_UpperCamelCase					:List[Any]					     =	FlaxDownBlockaD(
											    in_channels=SCREAMING_SNAKE_CASE__					,				out_channels=SCREAMING_SNAKE_CASE__					,				dropout=self.dropout					,				num_layers=self.layers_per_block					,				add_downsample=not is_final_block					,				dtype=self.dtype					,				)

								down_blocks.append(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Tuple					     =	down_blocks

					# mid
					_UpperCamelCase					:Dict					     =	FlaxUNetMidBlockaDCrossAttn(
					    in_channels=block_out_channels[-1]					,				dropout=self.dropout					,				num_attention_heads=num_attention_heads[-1]					,				use_linear_projection=self.use_linear_projection					,				use_memory_efficient_attention=self.use_memory_efficient_attention					,				dtype=self.dtype					,				)

					# up
					_UpperCamelCase					:Tuple					     =	[]
					_UpperCamelCase					:List[Any]					     =	list(reversed(SCREAMING_SNAKE_CASE__   )   )
					_UpperCamelCase					:List[str]					     =	list(reversed(SCREAMING_SNAKE_CASE__   )   )
					_UpperCamelCase					:Union[str, Any]					     =	list(reversed(SCREAMING_SNAKE_CASE__   )   )
					_UpperCamelCase					:Dict					     =	reversed_block_out_channels[0]
					for i, up_block_type in enumerate(self.up_block_types   ):
								_UpperCamelCase					:Union[str, Any]					     =	output_channel
								_UpperCamelCase					:Any					     =	reversed_block_out_channels[i]
								_UpperCamelCase					:Tuple					     =	reversed_block_out_channels[min(i + 1					,				len(SCREAMING_SNAKE_CASE__   ) - 1   )]

								_UpperCamelCase					:Any					     =	i == len(SCREAMING_SNAKE_CASE__   ) - 1

								if up_block_type == "CrossAttnUpBlock2D":
											_UpperCamelCase					:Optional[int]					     =	FlaxCrossAttnUpBlockaD(
											    in_channels=SCREAMING_SNAKE_CASE__					,				out_channels=SCREAMING_SNAKE_CASE__					,				prev_output_channel=SCREAMING_SNAKE_CASE__					,				num_layers=self.layers_per_block + 1					,				num_attention_heads=reversed_num_attention_heads[i]					,				add_upsample=not is_final_block					,				dropout=self.dropout					,				use_linear_projection=self.use_linear_projection					,				only_cross_attention=only_cross_attention[i]					,				use_memory_efficient_attention=self.use_memory_efficient_attention					,				dtype=self.dtype					,				)
								else:
											_UpperCamelCase					:Optional[Any]					     =	FlaxUpBlockaD(
											    in_channels=SCREAMING_SNAKE_CASE__					,				out_channels=SCREAMING_SNAKE_CASE__					,				prev_output_channel=SCREAMING_SNAKE_CASE__					,				num_layers=self.layers_per_block + 1					,				add_upsample=not is_final_block					,				dropout=self.dropout					,				dtype=self.dtype					,				)

								up_blocks.append(SCREAMING_SNAKE_CASE__   )
								_UpperCamelCase					:int					     =	output_channel
					_UpperCamelCase					:str					     =	up_blocks

					# out
					_UpperCamelCase					:Tuple					     =	nn.GroupNorm(num_groups=32					,				epsilon=1E-5   )
					_UpperCamelCase					:str					     =	nn.Conv(
					    self.out_channels					,				kernel_size=(3, 3)					,				strides=(1, 1)					,				padding=((1, 1), (1, 1))					,				dtype=self.dtype					,				)





		def __call__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__ = True					,				SCREAMING_SNAKE_CASE__ = False					,				)      ->	Union[FlaxUNetaDConditionOutput, Tuple]:

					"""simple docstring"""



					if not isinstance(SCREAMING_SNAKE_CASE__					,				jnp.ndarray   ):
								_UpperCamelCase					:List[str]					     =	jnp.array([timesteps]					,				dtype=jnp.intaa   )
					elif isinstance(SCREAMING_SNAKE_CASE__					,				jnp.ndarray   ) and len(timesteps.shape   ) == 0:
								_UpperCamelCase					:List[str]					     =	timesteps.astype(dtype=jnp.floataa   )
								_UpperCamelCase					:Union[str, Any]					     =	jnp.expand_dims(SCREAMING_SNAKE_CASE__					,				0   )

					_UpperCamelCase					:int					     =	self.time_proj(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:str					     =	self.time_embedding(SCREAMING_SNAKE_CASE__   )

					# 2. pre-process
					_UpperCamelCase					:List[Any]					     =	jnp.transpose(SCREAMING_SNAKE_CASE__					,				(0, 2, 3, 1)   )
					_UpperCamelCase					:str					     =	self.conv_in(SCREAMING_SNAKE_CASE__   )

					# 3. down
					_UpperCamelCase					:List[Any]					     =	(sample,)
					for down_block in self.down_blocks:
								if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ):
											_UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	down_block(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				deterministic=not train   )
								else:
											_UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	down_block(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				deterministic=not train   )
								down_block_res_samples += res_samples

					if down_block_additional_residuals is not None:
								_UpperCamelCase					:Tuple					     =	()

								for down_block_res_sample, down_block_additional_residual in zip(
								    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ):
											down_block_res_sample += down_block_additional_residual
											new_down_block_res_samples += (down_block_res_sample,)

								_UpperCamelCase					:Any					     =	new_down_block_res_samples

					# 4. mid
					_UpperCamelCase					:List[str]					     =	self.mid_block(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				deterministic=not train   )

					if mid_block_additional_residual is not None:
								sample += mid_block_additional_residual

					# 5. up
					for up_block in self.up_blocks:
								_UpperCamelCase					:Optional[int]					     =	down_block_res_samples[-(self.layers_per_block + 1) :]
								_UpperCamelCase					:Optional[Any]					     =	down_block_res_samples[: -(self.layers_per_block + 1)]
								if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ):
											_UpperCamelCase					:Tuple					     =	up_block(
											    SCREAMING_SNAKE_CASE__					,				temb=SCREAMING_SNAKE_CASE__					,				encoder_hidden_states=SCREAMING_SNAKE_CASE__					,				res_hidden_states_tuple=SCREAMING_SNAKE_CASE__					,				deterministic=not train					,				)
								else:
											_UpperCamelCase					:Optional[int]					     =	up_block(SCREAMING_SNAKE_CASE__					,				temb=SCREAMING_SNAKE_CASE__					,				res_hidden_states_tuple=SCREAMING_SNAKE_CASE__					,				deterministic=not train   )

        # 6. post-process
					_UpperCamelCase					:Optional[Any]					     =	self.conv_norm_out(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:str					     =	nn.silu(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	self.conv_out(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:int					     =	jnp.transpose(SCREAMING_SNAKE_CASE__					,				(0, 3, 1, 2)   )

					if not return_dict:
								return (sample,)

					return FlaxUNetaDConditionOutput(sample=SCREAMING_SNAKE_CASE__   )





"""simple docstring"""






import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
UpperCamelCase__				:str			   =	logging.getLogger()





def 				A_       ( snake_case__       ,					snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:Optional[int]					     =	'''\n'''.join(snake_case__		)
			Path(snake_case__		).open('''w'''		).writelines(snake_case__		)


UpperCamelCase__				:Dict			   =	"""patrickvonplaten/t5-tiny-random"""
UpperCamelCase__				:List[Any]			   =	"""sshleifer/bart-tiny-random"""
UpperCamelCase__				:str			   =	"""sshleifer/tiny-mbart"""

UpperCamelCase__				:Any			   =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	Path(self.get_auto_remove_tmp_dir()   ) / '''utest_input.source'''
					_UpperCamelCase					:Union[str, Any]					     =	input_file_name.parent / '''utest_output.txt'''
					assert not output_file_name.exists()
					_UpperCamelCase					:int					     =	[''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
					_dump_articles(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:str					     =	str(Path(self.get_auto_remove_tmp_dir()   ) / '''scores.json'''   )
					_UpperCamelCase					:Any					     =	'''translation_en_to_de''' if model == T5_TINY else '''summarization'''
					_UpperCamelCase					:Union[str, Any]					     =	f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

					with patch.object(SCREAMING_SNAKE_CASE__					,				'''argv'''					,				SCREAMING_SNAKE_CASE__   ):
								run_generate()
								assert Path(SCREAMING_SNAKE_CASE__   ).exists()
								# os.remove(Path(output_file_name))
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					self.run_eval_tester(SCREAMING_SNAKE_CASE__   )
		@parameterized.expand([BART_TINY, MBART_TINY]   )
		@slow
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					self.run_eval_tester(SCREAMING_SNAKE_CASE__   )





		@parameterized.expand([T5_TINY, MBART_TINY]   )
		@slow
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	Path(self.get_auto_remove_tmp_dir()   ) / '''utest_input.source'''
					_UpperCamelCase					:Any					     =	input_file_name.parent / '''utest_output.txt'''
					assert not output_file_name.exists()

					_UpperCamelCase					:Union[str, Any]					     =	{
					    '''en''': ['''Machine learning is great, isn\'t it?''', '''I like to eat bananas''', '''Tomorrow is another great day!'''],
					    '''de''': [
					        '''Maschinelles Lernen ist großartig, oder?''',
					        '''Ich esse gerne Bananen''',
					        '''Morgen ist wieder ein toller Tag!''',
					    ],
					}

					_UpperCamelCase					:str					     =	Path(self.get_auto_remove_tmp_dir()   )
					_UpperCamelCase					:Optional[int]					     =	str(tmp_dir / '''scores.json'''   )
					_UpperCamelCase					:int					     =	str(tmp_dir / '''val.target'''   )
					_dump_articles(SCREAMING_SNAKE_CASE__					,				text['''en''']   )
					_dump_articles(SCREAMING_SNAKE_CASE__					,				text['''de''']   )
					_UpperCamelCase					:List[str]					     =	'''translation_en_to_de''' if model == T5_TINY else '''summarization'''
					_UpperCamelCase					:str					     =	f"\n            run_eval_search.py\n            {model}\n            {str(SCREAMING_SNAKE_CASE__   )}\n            {str(SCREAMING_SNAKE_CASE__   )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
					testargs.extend(['''--search''', '''num_beams=1:2 length_penalty=0.9:1.0''']   )

					with patch.object(SCREAMING_SNAKE_CASE__					,				'''argv'''					,				SCREAMING_SNAKE_CASE__   ):
								with CaptureStdout() as cs:
											run_search()
								_UpperCamelCase					:Union[str, Any]					     =	[''' num_beams | length_penalty''', model, '''Best score args''']
								_UpperCamelCase					:List[Any]					     =	['''Info''']
								if "translation" in task:
											expected_strings.append('''bleu'''   )
								else:
											expected_strings.extend(SCREAMING_SNAKE_CASE__   )
								for w in expected_strings:
											assert w in cs.out
								for w in un_expected_strings:
											assert w not in cs.out
								assert Path(SCREAMING_SNAKE_CASE__   ).exists()
								os.remove(Path(SCREAMING_SNAKE_CASE__   )   )

