
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					SCREAMING_SNAKE_CASE							:str							 =				'platform'

					import jax
					import jax.numpy as jnp

					from transformers import BlenderbotTokenizer
					from transformers.models.blenderbot.modeling_flax_blenderbot import (
					    FlaxBlenderbotForConditionalGeneration,
					    FlaxBlenderbotModel,
					    shift_tokens_right,
					)


def 						UpperCAmelCase			(	a_		,   a_		,   a_=None		,   a_=None		,   a_=None		,   a_=None		,   a_=None		,   a_=None		,   )		->	Any:







				"""simple docstring"""



				if attention_mask is None:
								__A   			= np.where(input_ids != config.pad_token_id		,   1		,   0						)
				if decoder_attention_mask is None:
								__A   			= np.where(decoder_input_ids != config.pad_token_id		,   1		,   0						)
				if head_mask is None:
								__A   			= np.ones((config.encoder_layers, config.encoder_attention_heads)						)
				if decoder_head_mask is None:
								__A   			= np.ones((config.decoder_layers, config.decoder_attention_heads)						)
				if cross_attn_head_mask is None:
								__A   			= np.ones((config.decoder_layers, config.decoder_attention_heads)						)
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": attention_mask,
				}





class 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: List[str]							,A					: Tuple							,A					: Tuple=13							,A					: List[Any]=7							,A					: Optional[int]=True							,A					: List[Any]=False							,A					: Optional[int]=99							,A					: Union[str, Any]=16							,A					: Union[str, Any]=2							,A					: Optional[int]=4							,A					: Union[str, Any]=4							,A					: str="gelu"							,A					: Union[str, Any]=0.1							,A					: Any=0.1							,A					: Tuple=32							,A					: Any=2							,A					: Optional[int]=1							,A					: Union[str, Any]=0							,A					: Optional[int]=0.02							,):
					__A   			= parent
					__A   			= batch_size
					__A   			= seq_length
					__A   			= is_training
					__A   			= use_labels
					__A   			= vocab_size
					__A   			= hidden_size
					__A   			= num_hidden_layers
					__A   			= num_attention_heads
					__A   			= intermediate_size
					__A   			= hidden_act
					__A   			= hidden_dropout_prob
					__A   			= attention_probs_dropout_prob
					__A   			= max_position_embeddings
					__A   			= eos_token_id
					__A   			= pad_token_id
					__A   			= bos_token_id
					__A   			= initializer_range






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,self.vocab_size )							,3							,self.vocab_size )
					__A   			= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,dtype=np.intaa ))							,-1 )

					__A   			= shift_tokens_right(A							,1							,2 )

					__A   			= BlenderbotConfig(
					    vocab_size=self.vocab_size							,d_model=self.hidden_size							,encoder_layers=self.num_hidden_layers							,decoder_layers=self.num_hidden_layers							,encoder_attention_heads=self.num_attention_heads							,decoder_attention_heads=self.num_attention_heads							,encoder_ffn_dim=self.intermediate_size							,decoder_ffn_dim=self.intermediate_size							,dropout=self.hidden_dropout_prob							,attention_dropout=self.attention_probs_dropout_prob							,max_position_embeddings=self.max_position_embeddings							,eos_token_id=self.eos_token_id							,bos_token_id=self.bos_token_id							,pad_token_id=self.pad_token_id							,initializer_range=self.initializer_range							,use_cache=A							,)
					__A   			= prepare_blenderbot_inputs_dict(A							,A							,A )
					return config, inputs_dict






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A			,							__A   			= self.prepare_config_and_inputs()
					return config, inputs_dict






	def 				UpperCamelCase_  (       self					: List[str]							,A					: str							,A					: Tuple							,A					: Any ):
					__A   			= 20
					__A   			= model_class_name(A )

					__A   			= model.encode(inputs_dict["input_ids"] )

					__A			,							__A   			= (
					    inputs_dict["decoder_input_ids"],
					    inputs_dict["decoder_attention_mask"],
					)

					__A   			= model.init_cache(decoder_input_ids.shape[0]							,A							,A )
					__A   			= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,dtype="i4" )

					__A   			= jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]							,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,)
					__A   			= model.decode(
					    decoder_input_ids[:, :-1]							,A							,decoder_attention_mask=A							,past_key_values=A							,decoder_position_ids=A							,)

					__A   			= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,dtype="i4" )
					__A   			= model.decode(
					    decoder_input_ids[:, -1:]							,A							,decoder_attention_mask=A							,past_key_values=outputs_cache.past_key_values							,decoder_position_ids=A							,)

					__A   			= model.decode(A							,A )

					__A   			= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
					self.parent.assertTrue(diff < 1E-3							,msg=f'''Max diff is {diff}''' )







	def 				UpperCamelCase_  (       self					: Dict							,A					: Any							,A					: int							,A					: Optional[int] ):
					__A   			= 20
					__A   			= model_class_name(A )

					__A   			= model.encode(inputs_dict["input_ids"] )

					__A			,							__A   			= (
					    inputs_dict["decoder_input_ids"],
					    inputs_dict["decoder_attention_mask"],
					)

					__A   			= jnp.concatenate(
					    [
					        decoder_attention_mask,
					        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
					    ]							,axis=-1							,)

					__A   			= model.init_cache(decoder_input_ids.shape[0]							,A							,A )
					__A   			= jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]							,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,)

					__A   			= model.decode(
					    decoder_input_ids[:, :-1]							,A							,decoder_attention_mask=A							,past_key_values=A							,decoder_position_ids=A							,)
					__A   			= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,dtype="i4" )
					__A   			= model.decode(
					    decoder_input_ids[:, -1:]							,A							,past_key_values=outputs_cache.past_key_values							,decoder_attention_mask=A							,decoder_position_ids=A							,)

					__A   			= model.decode(A							,A							,decoder_attention_mask=A )

					__A   			= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
					self.parent.assertTrue(diff < 1E-3							,msg=f'''Max diff is {diff}''' )





@require_flax
class 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     99






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= np.array(
					    [
					        [71, 82, 18, 33, 46, 91, 2],
					        [68, 34, 26, 58, 30, 82, 2],
					        [5, 97, 17, 39, 94, 40, 2],
					        [76, 83, 94, 25, 70, 78, 2],
					        [87, 59, 41, 35, 48, 66, 2],
					        [55, 13, 16, 58, 5, 2, 1],  # note padding
					        [64, 27, 31, 51, 12, 75, 2],
					        [52, 64, 86, 17, 83, 39, 2],
					        [48, 61, 9, 24, 71, 82, 2],
					        [26, 1, 60, 48, 22, 13, 2],
					        [21, 5, 62, 28, 14, 76, 2],
					        [45, 98, 37, 86, 59, 48, 2],
					        [70, 70, 50, 9, 28, 0, 2],
					    ]							,dtype=np.intaa							,)

					__A   			= input_ids.shape[0]
					__A   			= BlenderbotConfig(
					    vocab_size=self.vocab_size							,d_model=24							,encoder_layers=2							,decoder_layers=2							,encoder_attention_heads=2							,decoder_attention_heads=2							,encoder_ffn_dim=32							,decoder_ffn_dim=32							,max_position_embeddings=48							,eos_token_id=2							,pad_token_id=1							,bos_token_id=0							,)
					return config, input_ids, batch_size






	def 				UpperCamelCase_  (       self					: Dict ):
					__A			,							__A			,							__A   			= self._get_config_and_data()
					__A   			= FlaxBlenderbotForConditionalGeneration(A )
					__A   			= lm_model(input_ids=A )
					__A   			= (batch_size, input_ids.shape[1], config.vocab_size)
					self.assertEqual(outputs["logits"].shape							,A )






	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= BlenderbotConfig(
					    vocab_size=self.vocab_size							,d_model=14							,encoder_layers=2							,decoder_layers=2							,encoder_attention_heads=2							,decoder_attention_heads=2							,encoder_ffn_dim=8							,decoder_ffn_dim=8							,max_position_embeddings=48							,)
					__A   			= FlaxBlenderbotForConditionalGeneration(A )
					__A   			= np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]							,dtype=np.intaa )
					__A   			= np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]							,dtype=np.intaa )
					__A   			= lm_model(input_ids=A							,decoder_input_ids=A )
					__A   			= (*summary.shape, config.vocab_size)
					self.assertEqual(outputs["logits"].shape							,A )







	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]							,dtype=np.intaa )
					__A   			= shift_tokens_right(A							,1							,2 )
					__A   			= np.equal(A							,1 ).astype(np.floataa ).sum()
					__A   			= np.equal(A							,1 ).astype(np.floataa ).sum()
					self.assertEqual(shifted.shape							,input_ids.shape )
					self.assertEqual(A							,n_pad_before - 1 )
					self.assertTrue(np.equal(shifted[:, 0]							,2 ).all() )





@require_flax
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			unittest.TestCase			,			__SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     True
	snake_case_					    =     (
	    (
	        FlaxBlenderbotModel,
	        FlaxBlenderbotForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	snake_case_					    =     (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= FlaxBlenderbotModelTester(self )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A			,							__A   			= self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(A							,A							,A )






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A			,							__A   			= self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(A							,A							,A )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A			,							__A   			= self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__ ):
													__A   			= self._prepare_for_class(A							,A )
													__A   			= model_class(A )

													@jax.jit
													def encode_jitted(A					: Any							,A					: Optional[Any]=None							,**A					: str ):
																	return model.encode(input_ids=A							,attention_mask=A )

													with self.subTest("JIT Enabled" ):
																	__A   			= encode_jitted(**A ).to_tuple()

													with self.subTest("JIT Disabled" ):
																	with jax.disable_jit():
																					__A   			= encode_jitted(**A ).to_tuple()

													self.assertEqual(len(A )							,len(A ) )
													for jitted_output, output in zip(A							,A ):
																	self.assertEqual(jitted_output.shape							,output.shape )






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A			,							__A   			= self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__ ):
													__A   			= model_class(A )
													__A   			= model.encode(inputs_dict["input_ids"]							,inputs_dict["attention_mask"] )

													__A   			= {
													    "decoder_input_ids": inputs_dict["decoder_input_ids"],
													    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
													    "encoder_outputs": encoder_outputs,
													}

													@jax.jit
													def decode_jitted(A					: List[Any]							,A					: Optional[int]							,A					: Tuple ):
																	return model.decode(
																	    decoder_input_ids=A							,decoder_attention_mask=A							,encoder_outputs=A							,)

													with self.subTest("JIT Enabled" ):
																	__A   			= decode_jitted(**A ).to_tuple()

													with self.subTest("JIT Disabled" ):
																	with jax.disable_jit():
																					__A   			= decode_jitted(**A ).to_tuple()

													self.assertEqual(len(A )							,len(A ) )
													for jitted_output, output in zip(A							,A ):
																	self.assertEqual(jitted_output.shape							,output.shape )






	@slow
	def 				UpperCamelCase_  (       self					: Tuple ):
					for model_class_name in self.all_model_classes:
									__A   			= model_class_name.from_pretrained("facebook/blenderbot-400M-distill" )
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									__A   			= np.ones((1, 1) ) * model.config.eos_token_id
									__A   			= model(A )
									self.assertIsNotNone(A )







	@unittest.skipUnless(jax_device != "cpu"							,"3B test too slow on CPU." )
	@slow
	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
					__A   			= {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

					__A   			= FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"							,from_pt=A )
					__A   			= BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B" )

					__A   			= ["Sam"]
					__A   			= tokenizer(A							,return_tensors="jax" )

					__A   			= model.generate(**A							,**A )
					__A   			= "Sam is a great name. It means \"sun\" in Gaelic."

					__A   			= tokenizer.batch_decode(A							,**A )
					assert generated_txt[0].strip() == tgt_text


import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
					import torch


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE							:List[str]							 =				logging.get_logger(__name__)
SCREAMING_SNAKE_CASE							:List[str]							 =				{'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE							:Dict							 =				{
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

SCREAMING_SNAKE_CASE							:Optional[Any]							 =				{
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     ["input_ids", "attention_mask"]






	def __init__(       self					: Optional[int]							,A					: Optional[Any]							,A					: Optional[int]=False							,A					: int=False							,A					: Union[str, Any]=False							,A					: int=None							,A					: Optional[Any]=None							,A					: Union[str, Any]=None							,A					: Optional[Any]=None							,A					: Optional[Dict[str, Any]] = None							,**A					: Tuple							,):
					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

					__A   			= kwargs.get("name_or_path" )
					if name_or_path is None:
									logger.warning(
									    "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
									    " you are testing the model, this can safely be ignored" )
									__A   			= "None"

					# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
					__A   			= "<|endoftext|>" if eos_token is None else eos_token
					__A   			= "<unk>" if unk_token is None else unk_token
					if "gpt-sw3-7b" in name_or_path:
									__A   			= unk_token if pad_token is None else pad_token
									__A   			= eos_token if bos_token is None else bos_token
					else:
									__A   			= "<pad>" if pad_token is None else pad_token
									__A   			= "<s>" if bos_token is None else bos_token

					super().__init__(
					    do_lower_case=A							,remove_space=A							,keep_accents=A							,bos_token=A							,eos_token=A							,unk_token=A							,pad_token=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)

					__A   			= do_lower_case
					__A   			= remove_space
					__A   			= keep_accents
					__A   			= vocab_file

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(A )

					# Used for whitespace normalization in input texts
					# fmt : off
					__A   			= {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
					# fmt : on

					# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
					__A   			= re.compile(
					    f'''[{''.join(map(A							,list(range(0							,9 ) ) + list(range(11							,32 ) ) + list(range(1_27							,1_60 ) ) + [1_60, 1_73, 82_03] ) )}]''' )






	def __getstate__(       self					: Optional[int] ):
					__A   			= self.__dict__.copy()
					__A   			= None
					return state






	def __setstate__(       self					: Optional[Any]							,A					: Union[str, Any] ):
					__A   			= d

					# for backward compatibility
					if not hasattr(self							,"sp_model_kwargs" ):
									__A   			= {}

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(self.vocab_file )






	@property
	# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
	def 				UpperCamelCase_  (       self					: List[str] ):
					return len(self.sp_model )






	def 				UpperCamelCase_  (       self					: int							,A					: str ):
					__A   			= self.non_printing_characters_re.sub(""							,A )

					# Normalize whitespaces
					__A   			= "".join([char if char not in self.whitespaces else " " for char in text] )

					# NFC Unicode normalization
					__A   			= unicodedata.normalize("NFC"							,A )
					return text






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: str							,**A					: Optional[int] ):
					__A   			= self.preprocess_text(A )
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: Any							,A					: str ):
					return self.sp_model.PieceToId(A )






	def 				UpperCamelCase_  (       self					: Dict							,A					: int ):
					return self.sp_model.IdToPiece(A )






	@staticmethod
	def 				UpperCamelCase_  (       A					: str ):
					return out_string






	def 				UpperCamelCase_  (       self					: str							,A					: List[str] ):
					__A   			= []
					__A   			= ""
					__A   			= False
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
													if not prev_is_special:
																	out_string += " "

													out_string += self.sp_model.decode(A ) + token
													__A   			= True
													__A   			= []
									else:
													current_sub_tokens.append(A )
													__A   			= False
					out_string += self.sp_model.decode(A )

					return out_string






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def 				UpperCamelCase_  (       self					: List[str]							,A					: str							,A					: Optional[str] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
									copyfile(self.vocab_file							,A )
					elif not os.path.isfile(self.vocab_file ):
									with open(A							,"wb" ) as fi:
													__A   			= self.sp_model.serialized_model_proto()
													fi.write(A )

					return (out_vocab_file,)






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: Union[str, List[str]]							,A					: Union[str, bool] = False ):

					if isinstance(A							,A ):
									__A   			= self.preprocess_text(A )
									__A   			= self.sp_model.encode(A )
					else:
									__A   			= [self.preprocess_text(A ) for t in text]
									__A   			= self.sp_model.encode(A )

					if return_tensors is True or return_tensors == "pt":
									__A   			= torch.tensor(A )

					return token_ids






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Union[int, List[int]] ):
					return self.sp_model.decode(A )







	def 				UpperCamelCase_  (       self					: List[str]							,A					: "Conversation" ):
					__A   			= [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
					__A   			= (
					    f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(A ) + f'''{self.bos_token}Bot:'''
					)
					return self.encode(text=A )
