


"""simple docstring"""







import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD


class 				SCREAMING_SNAKE_CASE__    (		nn.Module    ):
       """simple docstring"""


       a  :    int
       a  :    int
       a  :    float       =0.0
       a  :    int       =1
       a  :    int       =1
       a  :    bool       =True
       a  :    bool       =False
       a  :    bool       =False
       a  :    bool       =False
       a  :    jnp.dtype       =jnp.floataa

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							[]
         lowerCAmelCase					:					Union[str, Any]								=							[]

         for i in range(self.num_layers						):
           lowerCAmelCase					:					Tuple								=							self.in_channels if i == 0 else self.out_channels

           lowerCAmelCase					:					Union[str, Any]								=							FlaxResnetBlockaD(
               in_channels=snake_case__		,					out_channels=self.out_channels		,					dropout_prob=self.dropout		,					dtype=self.dtype		,					)
           resnets.append(snake_case__						)

           lowerCAmelCase					:					Optional[Any]								=							FlaxTransformeraDModel(
               in_channels=self.out_channels		,					n_heads=self.num_attention_heads		,					d_head=self.out_channels // self.num_attention_heads		,					depth=1		,					use_linear_projection=self.use_linear_projection		,					only_cross_attention=self.only_cross_attention		,					use_memory_efficient_attention=self.use_memory_efficient_attention		,					dtype=self.dtype		,					)
           attentions.append(snake_case__						)

         lowerCAmelCase					:					str								=							resnets
         lowerCAmelCase					:					Tuple								=							attentions

         if self.add_downsample:
           lowerCAmelCase					:					List[str]								=							FlaxDownsampleaD(self.out_channels		,					dtype=self.dtype						)







       def __call__(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__=True						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							()

         for resnet, attn in zip(self.resnets		,					self.attentions						):
           lowerCAmelCase					:					str								=							resnet(snake_case__		,					snake_case__		,					deterministic=snake_case__						)
           lowerCAmelCase					:					str								=							attn(snake_case__		,					snake_case__		,					deterministic=snake_case__						)
           output_states += (hidden_states,)

         if self.add_downsample:
           lowerCAmelCase					:					List[str]								=							self.downsamplers_a(snake_case__						)
           output_states += (hidden_states,)

         return hidden_states, output_states


class 				SCREAMING_SNAKE_CASE__    (		nn.Module    ):
       """simple docstring"""


       a  :    int
       a  :    int
       a  :    float       =0.0
       a  :    int       =1
       a  :    bool       =True
       a  :    jnp.dtype       =jnp.floataa

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							[]

         for i in range(self.num_layers						):
           lowerCAmelCase					:					List[str]								=							self.in_channels if i == 0 else self.out_channels

           lowerCAmelCase					:					Tuple								=							FlaxResnetBlockaD(
               in_channels=snake_case__		,					out_channels=self.out_channels		,					dropout_prob=self.dropout		,					dtype=self.dtype		,					)
           resnets.append(snake_case__						)
         lowerCAmelCase					:					Union[str, Any]								=							resnets

         if self.add_downsample:
           lowerCAmelCase					:					Dict								=							FlaxDownsampleaD(self.out_channels		,					dtype=self.dtype						)







       def __call__(						self		,					snake_case__		,					snake_case__		,					snake_case__=True						):






         """simple docstring"""





         lowerCAmelCase					:					Union[str, Any]								=							()

         for resnet in self.resnets:
           lowerCAmelCase					:					List[str]								=							resnet(snake_case__		,					snake_case__		,					deterministic=snake_case__						)
           output_states += (hidden_states,)

         if self.add_downsample:
           lowerCAmelCase					:					Union[str, Any]								=							self.downsamplers_a(snake_case__						)
           output_states += (hidden_states,)

         return hidden_states, output_states


class 				SCREAMING_SNAKE_CASE__    (		nn.Module    ):
       """simple docstring"""


       a  :    int
       a  :    int
       a  :    int
       a  :    float       =0.0
       a  :    int       =1
       a  :    int       =1
       a  :    bool       =True
       a  :    bool       =False
       a  :    bool       =False
       a  :    bool       =False
       a  :    jnp.dtype       =jnp.floataa

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							[]
         lowerCAmelCase					:					str								=							[]

         for i in range(self.num_layers						):
           lowerCAmelCase					:					int								=							self.in_channels if (i == self.num_layers - 1) else self.out_channels
           lowerCAmelCase					:					List[str]								=							self.prev_output_channel if i == 0 else self.out_channels

           lowerCAmelCase					:					List[str]								=							FlaxResnetBlockaD(
               in_channels=resnet_in_channels + res_skip_channels		,					out_channels=self.out_channels		,					dropout_prob=self.dropout		,					dtype=self.dtype		,					)
           resnets.append(snake_case__						)

           lowerCAmelCase					:					Optional[Any]								=							FlaxTransformeraDModel(
               in_channels=self.out_channels		,					n_heads=self.num_attention_heads		,					d_head=self.out_channels // self.num_attention_heads		,					depth=1		,					use_linear_projection=self.use_linear_projection		,					only_cross_attention=self.only_cross_attention		,					use_memory_efficient_attention=self.use_memory_efficient_attention		,					dtype=self.dtype		,					)
           attentions.append(snake_case__						)

         lowerCAmelCase					:					str								=							resnets
         lowerCAmelCase					:					Tuple								=							attentions

         if self.add_upsample:
           lowerCAmelCase					:					Optional[Any]								=							FlaxUpsampleaD(self.out_channels		,					dtype=self.dtype						)







       def __call__(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__=True						):






         """simple docstring"""





         for resnet, attn in zip(self.resnets		,					self.attentions						):
           # pop res hidden states
           lowerCAmelCase					:					int								=							res_hidden_states_tuple[-1]
           lowerCAmelCase					:					Any								=							res_hidden_states_tuple[:-1]
           lowerCAmelCase					:					int								=							jnp.concatenate((hidden_states, res_hidden_states)		,					axis=-1						)

           lowerCAmelCase					:					Tuple								=							resnet(snake_case__		,					snake_case__		,					deterministic=snake_case__						)
           lowerCAmelCase					:					Optional[Any]								=							attn(snake_case__		,					snake_case__		,					deterministic=snake_case__						)

         if self.add_upsample:
           lowerCAmelCase					:					int								=							self.upsamplers_a(snake_case__						)

         return hidden_states


class 				SCREAMING_SNAKE_CASE__    (		nn.Module    ):
       """simple docstring"""


       a  :    int
       a  :    int
       a  :    int
       a  :    float       =0.0
       a  :    int       =1
       a  :    bool       =True
       a  :    jnp.dtype       =jnp.floataa

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							[]

         for i in range(self.num_layers						):
           lowerCAmelCase					:					Dict								=							self.in_channels if (i == self.num_layers - 1) else self.out_channels
           lowerCAmelCase					:					str								=							self.prev_output_channel if i == 0 else self.out_channels

           lowerCAmelCase					:					List[str]								=							FlaxResnetBlockaD(
               in_channels=resnet_in_channels + res_skip_channels		,					out_channels=self.out_channels		,					dropout_prob=self.dropout		,					dtype=self.dtype		,					)
           resnets.append(snake_case__						)

         lowerCAmelCase					:					Dict								=							resnets

         if self.add_upsample:
           lowerCAmelCase					:					List[Any]								=							FlaxUpsampleaD(self.out_channels		,					dtype=self.dtype						)







       def __call__(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__=True						):






         """simple docstring"""





         for resnet in self.resnets:
           # pop res hidden states
           lowerCAmelCase					:					Any								=							res_hidden_states_tuple[-1]
           lowerCAmelCase					:					Union[str, Any]								=							res_hidden_states_tuple[:-1]
           lowerCAmelCase					:					Tuple								=							jnp.concatenate((hidden_states, res_hidden_states)		,					axis=-1						)

           lowerCAmelCase					:					List[str]								=							resnet(snake_case__		,					snake_case__		,					deterministic=snake_case__						)

         if self.add_upsample:
           lowerCAmelCase					:					str								=							self.upsamplers_a(snake_case__						)

         return hidden_states


class 				SCREAMING_SNAKE_CASE__    (		nn.Module    ):
       """simple docstring"""


       a  :    int
       a  :    float       =0.0
       a  :    int       =1
       a  :    int       =1
       a  :    bool       =False
       a  :    bool       =False
       a  :    jnp.dtype       =jnp.floataa

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							[
             FlaxResnetBlockaD(
                 in_channels=self.in_channels		,					out_channels=self.in_channels		,					dropout_prob=self.dropout		,					dtype=self.dtype		,					)
         ]

         lowerCAmelCase					:					List[Any]								=							[]

         for _ in range(self.num_layers						):
           lowerCAmelCase					:					str								=							FlaxTransformeraDModel(
               in_channels=self.in_channels		,					n_heads=self.num_attention_heads		,					d_head=self.in_channels // self.num_attention_heads		,					depth=1		,					use_linear_projection=self.use_linear_projection		,					use_memory_efficient_attention=self.use_memory_efficient_attention		,					dtype=self.dtype		,					)
           attentions.append(snake_case__						)

           lowerCAmelCase					:					Union[str, Any]								=							FlaxResnetBlockaD(
               in_channels=self.in_channels		,					out_channels=self.in_channels		,					dropout_prob=self.dropout		,					dtype=self.dtype		,					)
           resnets.append(snake_case__						)

         lowerCAmelCase					:					List[str]								=							resnets
         lowerCAmelCase					:					Tuple								=							attentions







       def __call__(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__=True						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							self.resnets[0](snake_case__		,					snake_case__						)
         for attn, resnet in zip(self.attentions		,					self.resnets[1:]						):
           lowerCAmelCase					:					Optional[Any]								=							attn(snake_case__		,					snake_case__		,					deterministic=snake_case__						)
           lowerCAmelCase					:					Tuple								=							resnet(snake_case__		,					snake_case__		,					deterministic=snake_case__						)

         return hidden_states









"""simple docstring"""







import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				SCREAMING_SNAKE_CASE__    (		lowercase       ,   unittest.TestCase    ):
       """simple docstring"""


       a  :    List[str]       =None
       a  :    List[Any]       =BloomTokenizerFast
       a  :    Optional[int]       =BloomTokenizerFast
       a  :    Optional[Any]       =True
       a  :    Dict       =False
       a  :    Optional[Any]       ="tokenizer_file"
       a  :    Optional[int]       ={"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}

       def 	lowercase__							(						self						):






         """simple docstring"""





         super().setUp()
         lowerCAmelCase					:					Tuple								=							BloomTokenizerFast.from_pretrained("bigscience/tokenizer"						)
         tokenizer.save_pretrained(self.tmpdirname						)

       def 	lowercase__							(						self		,					**snake_case__						):






         """simple docstring"""





         kwargs.update(self.special_tokens_map						)
         return BloomTokenizerFast.from_pretrained(self.tmpdirname		,					**snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							self.get_rust_tokenizer()

         lowerCAmelCase					:					List[Any]								=							["The quick brown fox</s>", "jumps over the lazy dog</s>"]
         lowerCAmelCase					:					str								=							[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

         lowerCAmelCase					:					Optional[int]								=							tokenizer.batch_encode_plus(snake_case__						)["input_ids"]
         self.assertListEqual(snake_case__		,					snake_case__						)

         lowerCAmelCase					:					Optional[int]								=							tokenizer.batch_decode(snake_case__						)
         self.assertListEqual(snake_case__		,					snake_case__						)

       def 	lowercase__							(						self		,					snake_case__=6						):






         """simple docstring"""





         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
             lowerCAmelCase					:					Tuple								=							self.rust_tokenizer_class.from_pretrained(snake_case__		,					**snake_case__						)
             # tokenizer_r.pad_token = None # Hotfixing padding = None
             # Simple input
             lowerCAmelCase					:					str								=							"This is a simple input"
             lowerCAmelCase					:					Tuple								=							["This is a simple input 1", "This is a simple input 2"]
             lowerCAmelCase					:					Any								=							("This is a simple input", "This is a pair")
             lowerCAmelCase					:					Tuple								=							[
                 ("This is a simple input 1", "This is a simple input 2"),
                 ("This is a simple pair 1", "This is a simple pair 2"),
             ]

             # Simple input tests
             try:
               tokenizer_r.encode(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.encode_plus(snake_case__		,					max_length=snake_case__						)

               tokenizer_r.batch_encode_plus(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.encode(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.batch_encode_plus(snake_case__		,					max_length=snake_case__						)
             except ValueError:
               self.fail("Bloom Tokenizer should be able to deal with padding"						)

             lowerCAmelCase					:					Tuple								=							None  # Hotfixing padding = None
             self.assertRaises(snake_case__		,					tokenizer_r.encode		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Simple input
             self.assertRaises(snake_case__		,					tokenizer_r.encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Simple input
             self.assertRaises(
                 snake_case__		,					tokenizer_r.batch_encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"		,					)

             # Pair input
             self.assertRaises(snake_case__		,					tokenizer_r.encode		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Pair input
             self.assertRaises(snake_case__		,					tokenizer_r.encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Pair input
             self.assertRaises(
                 snake_case__		,					tokenizer_r.batch_encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"		,					)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							self.get_rust_tokenizer()
         lowerCAmelCase					:					int								=							load_dataset("xnli"		,					"all_languages"		,					split="test"		,					streaming=snake_case__						)

         lowerCAmelCase					:					Tuple								=							next(iter(snake_case__						)						)["premise"]  # pick up one data
         lowerCAmelCase					:					Optional[Any]								=							list(sample_data.values()						)

         lowerCAmelCase					:					int								=							list(map(tokenizer.encode		,					snake_case__						)						)
         lowerCAmelCase					:					List[Any]								=							[tokenizer.decode(snake_case__		,					clean_up_tokenization_spaces=snake_case__						) for x in output_tokens]
         self.assertListEqual(snake_case__		,					snake_case__						)







       def 	lowercase__							(						self						):






         """simple docstring"""





         self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map						)		,					1						)
         self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()						)[0]						)		,					1						)





