




'''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      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   jnp.ndarray





@flax_register_to_config
class      lowerCAmelCase (			nn.Module					, __lowerCAmelCase					, __lowerCAmelCase):
       __lowercase	:   int										=      32
       __lowercase	:   int										=      4
       __lowercase	:   int										=      4
       __lowercase	:   Tuple[str]										=      (
           "CrossAttnDownBlock2D",
           "CrossAttnDownBlock2D",
           "CrossAttnDownBlock2D",
           "DownBlock2D",
       )
       __lowercase	:   Tuple[str]										=      ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
       __lowercase	:   Union[bool, Tuple[bool]]										=      False
       __lowercase	:   Tuple[int]										=      (320, 640, 1280, 1280)
       __lowercase	:   int										=      2
       __lowercase	:   Union[int, Tuple[int]]										=      8
       __lowercase	:   Optional[Union[int, Tuple[int]]]										=      None
       __lowercase	:   int										=      1280
       __lowercase	:   float										=      0.0
       __lowercase	:   bool										=      False
       __lowercase	:   jnp.dtype										=      jnp.floataa
       __lowercase	:   bool										=      True
       __lowercase	:   int										=      0
       __lowercase	:   bool										=      False


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	FrozenDict:



            '''simple docstring'''




            __snake_case            =	(1, self.in_channels, self.sample_size, self.sample_size)
            __snake_case            =	jnp.zeros(__SCREAMING_SNAKE_CASE     ,	dtype=jnp.floataa	)
            __snake_case            =	jnp.ones((1,)     ,	dtype=jnp.intaa	)
            __snake_case            =	jnp.zeros((1, 1, self.cross_attention_dim)     ,	dtype=jnp.floataa	)

            __snake_case       ,     __snake_case            =	jax.random.split(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	{'''params''': params_rng, '''dropout''': dropout_rng}

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


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	self.block_out_channels
            __snake_case            =	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.
            __snake_case            =	self.num_attention_heads or self.attention_head_dim

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

            # time
            __snake_case            =	FlaxTimesteps(
                block_out_channels[0]     ,	flip_sin_to_cos=self.flip_sin_to_cos     ,	freq_shift=self.config.freq_shift	)
            __snake_case            =	FlaxTimestepEmbedding(__SCREAMING_SNAKE_CASE     ,	dtype=self.dtype	)

            __snake_case            =	self.only_cross_attention
            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	(only_cross_attention,) * len(self.down_block_types	)

            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	(num_attention_heads,) * len(self.down_block_types	)

            # down
            __snake_case            =	[]
            __snake_case            =	block_out_channels[0]
            for i, down_block_type in enumerate(self.down_block_types	):
                 __snake_case            =	output_channel
                 __snake_case            =	block_out_channels[i]
                 __snake_case            =	i == len(__SCREAMING_SNAKE_CASE	) - 1

                 if down_block_type == "CrossAttnDownBlock2D":
                      __snake_case            =	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:
                      __snake_case            =	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	)
            __snake_case            =	down_blocks

            # mid
            __snake_case            =	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
            __snake_case            =	[]
            __snake_case            =	list(reversed(__SCREAMING_SNAKE_CASE	)	)
            __snake_case            =	list(reversed(__SCREAMING_SNAKE_CASE	)	)
            __snake_case            =	list(reversed(__SCREAMING_SNAKE_CASE	)	)
            __snake_case            =	reversed_block_out_channels[0]
            for i, up_block_type in enumerate(self.up_block_types	):
                 __snake_case            =	output_channel
                 __snake_case            =	reversed_block_out_channels[i]
                 __snake_case            =	reversed_block_out_channels[min(i + 1     ,	len(__SCREAMING_SNAKE_CASE	) - 1	)]

                 __snake_case            =	i == len(__SCREAMING_SNAKE_CASE	) - 1

                 if up_block_type == "CrossAttnUpBlock2D":
                      __snake_case            =	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:
                      __snake_case            =	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	)
                 __snake_case            =	output_channel
            __snake_case            =	up_blocks

            # out
            __snake_case            =	nn.GroupNorm(num_groups=32     ,	epsilon=1E-5	)
            __snake_case            =	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	):
                 __snake_case            =	jnp.array([timesteps]     ,	dtype=jnp.intaa	)
            elif isinstance(__SCREAMING_SNAKE_CASE     ,	jnp.ndarray	) and len(timesteps.shape	) == 0:
                 __snake_case            =	timesteps.astype(dtype=jnp.floataa	)
                 __snake_case            =	jnp.expand_dims(__SCREAMING_SNAKE_CASE     ,	0	)

            __snake_case            =	self.time_proj(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.time_embedding(__SCREAMING_SNAKE_CASE	)

            # 2. pre-process
            __snake_case            =	jnp.transpose(__SCREAMING_SNAKE_CASE     ,	(0, 2, 3, 1)	)
            __snake_case            =	self.conv_in(__SCREAMING_SNAKE_CASE	)

            # 3. down
            __snake_case            =	(sample,)
            for down_block in self.down_blocks:
                 if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                      __snake_case       ,     __snake_case            =	down_block(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	deterministic=not train	)
                 else:
                      __snake_case       ,     __snake_case            =	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:
                 __snake_case            =	()

                 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,)

                 __snake_case            =	new_down_block_res_samples

            # 4. mid
            __snake_case            =	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:
                 __snake_case            =	down_block_res_samples[-(self.layers_per_block + 1) :]
                 __snake_case            =	down_block_res_samples[: -(self.layers_per_block + 1)]
                 if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                      __snake_case            =	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:
                      __snake_case            =	up_block(__SCREAMING_SNAKE_CASE     ,	temb=__SCREAMING_SNAKE_CASE     ,	res_hidden_states_tuple=__SCREAMING_SNAKE_CASE     ,	deterministic=not train	)

        # 6. post-process
            __snake_case            =	self.conv_norm_out(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	nn.silu(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.conv_out(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	jnp.transpose(__SCREAMING_SNAKE_CASE     ,	(0, 3, 1, 2)	)

            if not return_dict:
                 return (sample,)

            return FlaxUNetaDConditionOutput(sample=__SCREAMING_SNAKE_CASE	)












'''simple docstring'''



import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   int										=      BarthezTokenizer
       __lowercase	:   Any										=      BarthezTokenizerFast
       __lowercase	:   Dict										=      True
       __lowercase	:   Optional[int]										=      True


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            super().setUp()

            __snake_case            =	BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''	)
            tokenizer.save_pretrained(self.tmpdirname	)
            tokenizer.save_pretrained(self.tmpdirname     ,	legacy_format=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	tokenizer


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	'''<pad>'''
            __snake_case            =	1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE	)     ,	__SCREAMING_SNAKE_CASE	)
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE	)     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	list(self.get_tokenizer().get_vocab().keys()	)

            self.assertEqual(vocab_keys[0]     ,	'''<s>'''	)
            self.assertEqual(vocab_keys[1]     ,	'''<pad>'''	)
            self.assertEqual(vocab_keys[-1]     ,	'''<mask>'''	)
            self.assertEqual(len(__SCREAMING_SNAKE_CASE	)     ,	10_1122	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size     ,	10_1122	)


       @require_torch
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
            __snake_case            =	[0, 57, 3018, 7_0307, 91, 2]

            __snake_case            =	self.tokenizer(
                __SCREAMING_SNAKE_CASE     ,	max_length=len(__SCREAMING_SNAKE_CASE	)     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	)
            self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            self.assertEqual((2, 6)     ,	batch.input_ids.shape	)
            self.assertEqual((2, 6)     ,	batch.attention_mask.shape	)
            __snake_case            =	batch.input_ids.tolist()[0]
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_rust_tokenizer()

            __snake_case            =	'''I was born in 92000, and this is falsé.'''

            __snake_case            =	tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	tokenizer.encode(__SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_rust_tokenizer()
            __snake_case            =	tokenizer.encode(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
            # fmt: on

            # moussaKam/mbarthez is a french model. So we also use french texts.
            __snake_case            =	[
                '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
                '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
                '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
                '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
                '''telles que la traduction et la synthèse de texte.''',
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=__SCREAMING_SNAKE_CASE     ,	model_name='''moussaKam/mbarthez'''     ,	revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''     ,	sequences=__SCREAMING_SNAKE_CASE     ,	)






