






"""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 		snake_case						(						UpperCAmelCase ):
    __magic_name__					  =  42






@flax_register_to_config
class 		snake_case						(						nn.Module     ,				UpperCAmelCase     ,				UpperCAmelCase ):
    __magic_name__					  =  32
    __magic_name__					  =  4
    __magic_name__					  =  4
    __magic_name__					  =  (
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "DownBlock2D",
    )
    __magic_name__					  =  ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
    __magic_name__					  =  False
    __magic_name__					  =  (320, 640, 1280, 1280)
    __magic_name__					  =  2
    __magic_name__					  =  8
    __magic_name__					  =  None
    __magic_name__					  =  1280
    __magic_name__					  =  0.0
    __magic_name__					  =  False
    __magic_name__					  =  jnp.floataa
    __magic_name__					  =  True
    __magic_name__					  =  0
    __magic_name__					  =  False





    def        lowerCamelCase__   (							self			:      Optional[Any]       ,	A			:      jax.random.KeyArray    ):


      '''simple docstring'''




      a  :    str       =     (1, self.in_channels, self.sample_size, self.sample_size)
      a  :    Union[str, Any]       =     jnp.zeros(A       ,	dtype=jnp.floataa    )
      a  :    List[str]       =     jnp.ones((1,)       ,	dtype=jnp.intaa    )
      a  :    Optional[Any]       =     jnp.zeros((1, 1, self.cross_attention_dim)       ,	dtype=jnp.floataa    )

      a,  a  :    int       =     jax.random.split(A    )
      a  :    Tuple       =     {'params': params_rng, 'dropout': dropout_rng}

      return self.init(A       ,	A       ,	A       ,	A    )["params"]





    def        lowerCamelCase__   (							self			:      Union[str, Any]    ):


      '''simple docstring'''




      a  :    Tuple       =     self.block_out_channels
      a  :    Optional[Any]       =     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.
      a  :    Dict       =     self.num_attention_heads or self.attention_head_dim

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

      # time
      a  :    Union[str, Any]       =     FlaxTimesteps(
          block_out_channels[0]       ,	flip_sin_to_cos=self.flip_sin_to_cos       ,	freq_shift=self.config.freq_shift    )
      a  :    Tuple       =     FlaxTimestepEmbedding(A       ,	dtype=self.dtype    )

      a  :    Optional[Any]       =     self.only_cross_attention
      if isinstance(A       ,	A    ):
        a  :    List[Any]       =     (only_cross_attention,) * len(self.down_block_types    )

      if isinstance(A       ,	A    ):
        a  :    List[Any]       =     (num_attention_heads,) * len(self.down_block_types    )

      # down
      a  :    Optional[Any]       =     []
      a  :    Optional[Any]       =     block_out_channels[0]
      for i, down_block_type in enumerate(self.down_block_types    ):
        a  :    List[str]       =     output_channel
        a  :    Optional[Any]       =     block_out_channels[i]
        a  :    Union[str, Any]       =     i == len(A    ) - 1

        if down_block_type == "CrossAttnDownBlock2D":
          a  :    Optional[Any]       =     FlaxCrossAttnDownBlockaD(
              in_channels=A       ,	out_channels=A       ,	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:
          a  :    int       =     FlaxDownBlockaD(
              in_channels=A       ,	out_channels=A       ,	dropout=self.dropout       ,	num_layers=self.layers_per_block       ,	add_downsample=not is_final_block       ,	dtype=self.dtype       ,	)

        down_blocks.append(A    )
      a  :    List[Any]       =     down_blocks

      # mid
      a  :    int       =     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
      a  :    str       =     []
      a  :    Any       =     list(reversed(A    )    )
      a  :    int       =     list(reversed(A    )    )
      a  :    Union[str, Any]       =     list(reversed(A    )    )
      a  :    Optional[Any]       =     reversed_block_out_channels[0]
      for i, up_block_type in enumerate(self.up_block_types    ):
        a  :    List[str]       =     output_channel
        a  :    List[str]       =     reversed_block_out_channels[i]
        a  :    Tuple       =     reversed_block_out_channels[min(i + 1       ,	len(A    ) - 1    )]

        a  :    Tuple       =     i == len(A    ) - 1

        if up_block_type == "CrossAttnUpBlock2D":
          a  :    List[Any]       =     FlaxCrossAttnUpBlockaD(
              in_channels=A       ,	out_channels=A       ,	prev_output_channel=A       ,	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:
          a  :    Union[str, Any]       =     FlaxUpBlockaD(
              in_channels=A       ,	out_channels=A       ,	prev_output_channel=A       ,	num_layers=self.layers_per_block + 1       ,	add_upsample=not is_final_block       ,	dropout=self.dropout       ,	dtype=self.dtype       ,	)

        up_blocks.append(A    )
        a  :    List[str]       =     output_channel
      a  :    Tuple       =     up_blocks

      # out
      a  :    Union[str, Any]       =     nn.GroupNorm(num_groups=3_2       ,	epsilon=1E-5    )
      a  :    List[Any]       =     nn.Conv(
          self.out_channels       ,	kernel_size=(3, 3)       ,	strides=(1, 1)       ,	padding=((1, 1), (1, 1))       ,	dtype=self.dtype       ,	)





    def __call__(							self			:      Dict       ,	A			:      Dict       ,	A			:      str       ,	A			:      Dict       ,	A			:      Any=None       ,	A			:      Tuple=None       ,	A			:      bool = True       ,	A			:      bool = False       ,	):


      '''simple docstring'''




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

      a  :    Dict       =     self.time_proj(A    )
      a  :    int       =     self.time_embedding(A    )

      # 2. pre-process
      a  :    Union[str, Any]       =     jnp.transpose(A       ,	(0, 2, 3, 1)    )
      a  :    str       =     self.conv_in(A    )

      # 3. down
      a  :    Union[str, Any]       =     (sample,)
      for down_block in self.down_blocks:
        if isinstance(A       ,	A    ):
          a,  a  :    Optional[int]       =     down_block(A       ,	A       ,	A       ,	deterministic=not train    )
        else:
          a,  a  :    Optional[Any]       =     down_block(A       ,	A       ,	deterministic=not train    )
        down_block_res_samples += res_samples

      if down_block_additional_residuals is not None:
        a  :    str       =     ()

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

        a  :    int       =     new_down_block_res_samples

      # 4. mid
      a  :    Tuple       =     self.mid_block(A       ,	A       ,	A       ,	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:
        a  :    str       =     down_block_res_samples[-(self.layers_per_block + 1) :]
        a  :    str       =     down_block_res_samples[: -(self.layers_per_block + 1)]
        if isinstance(A       ,	A    ):
          a  :    int       =     up_block(
              A       ,	temb=A       ,	encoder_hidden_states=A       ,	res_hidden_states_tuple=A       ,	deterministic=not train       ,	)
        else:
          a  :    List[Any]       =     up_block(A       ,	temb=A       ,	res_hidden_states_tuple=A       ,	deterministic=not train    )

        # 6. post-process
      a  :    Union[str, Any]       =     self.conv_norm_out(A    )
      a  :    Union[str, Any]       =     nn.silu(A    )
      a  :    List[Any]       =     self.conv_out(A    )
      a  :    List[Any]       =     jnp.transpose(A       ,	(0, 3, 1, 2)    )

      if not return_dict:
        return (sample,)

      return FlaxUNetaDConditionOutput(sample=A    )












"""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       :					List[str]									=		logging.getLogger()




def     snake_case						(A_   :Path			,    A_   :list   ):







  '''simple docstring'''

  a  :    Optional[int]       =     '\n'.join(A_   )
  Path(A_   ).open('w'   ).writelines(A_   )


_UpperCamelCase       :					Optional[Any]									=		'patrickvonplaten/t5-tiny-random'
_UpperCamelCase       :					str									=		'sshleifer/bart-tiny-random'
_UpperCamelCase       :					Any									=		'sshleifer/tiny-mbart'

_UpperCamelCase       :					List[str]									=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class 		snake_case						(						UpperCAmelCase ):





    def        lowerCamelCase__   (							self			:      List[Any]       ,	A			:      Optional[Any]    ):


      '''simple docstring'''




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

      a  :    Tuple       =     str(Path(self.get_auto_remove_tmp_dir()    ) / 'scores.json'    )
      a  :    List[Any]       =     'translation_en_to_de' if model == T5_TINY else 'summarization'
      a  :    List[Any]       =     F'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

      with patch.object(A       ,	'argv'       ,	A    ):
        run_generate()
        assert Path(A    ).exists()
        # os.remove(Path(output_file_name))





    def        lowerCamelCase__   (							self			:      Optional[Any]    ):


      '''simple docstring'''




      self.run_eval_tester(A    )





    @parameterized.expand([BART_TINY, MBART_TINY]    )
    @slow
    def        lowerCamelCase__   (							self			:      Union[str, Any]       ,	A			:      List[Any]    ):


      '''simple docstring'''




      self.run_eval_tester(A    )





    @parameterized.expand([T5_TINY, MBART_TINY]    )
    @slow
    def        lowerCamelCase__   (							self			:      Optional[Any]       ,	A			:      List[Any]    ):


      '''simple docstring'''




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

      a  :    Optional[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!',
          ],
      }

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

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




