
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowercase   =	logging.get_logger(__name__)


lowercase   =	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
lowercase   =	{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

lowercase   =	{
    '''allenai/led-base-16384''': 1_6_3_8_4,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def   __lowerCAmelCase    (				)	->     List[str]:
							lowerCamelCase_					=       (
							    list(range(ord("""!"""							)      ,					ord("""~"""							) + 1							)							) + list(range(ord("""¡"""							)      ,					ord("""¬"""							) + 1							)							) + list(range(ord("""®"""							)      ,					ord("""ÿ"""							) + 1							)							)
							)
							lowerCamelCase_					=       bs[:]
							lowerCamelCase_					=       0
							for b in range(2**8							):
														if b not in bs:
																					bs.append(UpperCAmelCase__							)
																					cs.append(2**8 + n							)
																					n += 1
							lowerCamelCase_					=       [chr(UpperCAmelCase__							) for n in cs]
							return dict(zip(UpperCAmelCase__      ,					UpperCAmelCase__							)							)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Tuple							)	->     List[str]:
							lowerCamelCase_					=       set()
							lowerCamelCase_					=       word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)							)
														lowerCamelCase_					=       char
							return pairs





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE           =     PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE           =     ['''input_ids''', '''attention_mask''']

					def __init__(						self       :  List[Any] ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Union[str, Any]="replace" ,       __UpperCamelCase       :  Dict="<s>" ,       __UpperCamelCase       :  Optional[Any]="</s>" ,       __UpperCamelCase       :  Union[str, Any]="</s>" ,       __UpperCamelCase       :  Optional[Any]="<s>" ,       __UpperCamelCase       :  Optional[int]="<unk>" ,       __UpperCamelCase       :  List[Any]="<pad>" ,       __UpperCamelCase       :  Union[str, Any]="<mask>" ,       __UpperCamelCase       :  Optional[int]=False ,       **__UpperCamelCase       :  Dict ,       ):
												lowerCamelCase_					=       AddedToken(__UpperCamelCase ,       lstrip=__UpperCamelCase ,       rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase ,       __UpperCamelCase ) else bos_token
												lowerCamelCase_					=       AddedToken(__UpperCamelCase ,       lstrip=__UpperCamelCase ,       rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase ,       __UpperCamelCase ) else eos_token
												lowerCamelCase_					=       AddedToken(__UpperCamelCase ,       lstrip=__UpperCamelCase ,       rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase ,       __UpperCamelCase ) else sep_token
												lowerCamelCase_					=       AddedToken(__UpperCamelCase ,       lstrip=__UpperCamelCase ,       rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase ,       __UpperCamelCase ) else cls_token
												lowerCamelCase_					=       AddedToken(__UpperCamelCase ,       lstrip=__UpperCamelCase ,       rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase ,       __UpperCamelCase ) else unk_token
												lowerCamelCase_					=       AddedToken(__UpperCamelCase ,       lstrip=__UpperCamelCase ,       rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase ,       __UpperCamelCase ) else pad_token

												# Mask token behave like a normal word, i.e. include the space before it
												lowerCamelCase_					=       AddedToken(__UpperCamelCase ,       lstrip=__UpperCamelCase ,       rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase ,       __UpperCamelCase ) else mask_token

												super().__init__(
												    errors=__UpperCamelCase ,       bos_token=__UpperCamelCase ,       eos_token=__UpperCamelCase ,       unk_token=__UpperCamelCase ,       sep_token=__UpperCamelCase ,       cls_token=__UpperCamelCase ,       pad_token=__UpperCamelCase ,       mask_token=__UpperCamelCase ,       add_prefix_space=__UpperCamelCase ,       **__UpperCamelCase ,       )

												with open(__UpperCamelCase ,       encoding="""utf-8""" ) as vocab_handle:
																			lowerCamelCase_					=       json.load(__UpperCamelCase )
												lowerCamelCase_					=       {v: k for k, v in self.encoder.items()}
												lowerCamelCase_					=       errors  # how to handle errors in decoding
												lowerCamelCase_					=       bytes_to_unicode()
												lowerCamelCase_					=       {v: k for k, v in self.byte_encoder.items()}
												with open(__UpperCamelCase ,       encoding="""utf-8""" ) as merges_handle:
																			lowerCamelCase_					=       merges_handle.read().split("""\n""" )[1:-1]
												lowerCamelCase_					=       [tuple(merge.split() ) for merge in bpe_merges]
												lowerCamelCase_					=       dict(zip(__UpperCamelCase ,       range(len(__UpperCamelCase ) ) ) )
												lowerCamelCase_					=       {}
												lowerCamelCase_					=       add_prefix_space

												# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
												lowerCamelCase_					=       re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )

					@property
					# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
					def  lowercase__   (						self       :  List[str] ):
												return len(self.encoder )

					def  lowercase__   (						self       :  str ):
												return dict(self.encoder ,       **self.added_tokens_encoder )

					def  lowercase__   (						self       :  str ,       __UpperCamelCase       :  List[str] ):
												if token in self.cache:
																			return self.cache[token]
												lowerCamelCase_					=       tuple(__UpperCamelCase )
												lowerCamelCase_					=       get_pairs(__UpperCamelCase )

												if not pairs:
																			return token

												while True:
																			lowerCamelCase_					=       min(__UpperCamelCase ,       key=lambda __UpperCamelCase : self.bpe_ranks.get(__UpperCamelCase ,       float("""inf""" ) ) )
																			if bigram not in self.bpe_ranks:
																										break
																			lowerCamelCase_	, lowerCamelCase_					=       bigram
																			lowerCamelCase_					=       []
																			lowerCamelCase_					=       0
																			while i < len(__UpperCamelCase ):
																										try:
																																	lowerCamelCase_					=       word.index(__UpperCamelCase ,       __UpperCamelCase )
																										except ValueError:
																																	new_word.extend(word[i:] )
																																	break
																										else:
																																	new_word.extend(word[i:j] )
																																	lowerCamelCase_					=       j

																										if word[i] == first and i < len(__UpperCamelCase ) - 1 and word[i + 1] == second:
																																	new_word.append(first + second )
																																	i += 2
																										else:
																																	new_word.append(word[i] )
																																	i += 1
																			lowerCamelCase_					=       tuple(__UpperCamelCase )
																			lowerCamelCase_					=       new_word
																			if len(__UpperCamelCase ) == 1:
																										break
																			else:
																										lowerCamelCase_					=       get_pairs(__UpperCamelCase )
												lowerCamelCase_					=       """ """.join(__UpperCamelCase )
												lowerCamelCase_					=       word
												return word

					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  str ):
												lowerCamelCase_					=       []
												for token in re.findall(self.pat ,       __UpperCamelCase ):
																			lowerCamelCase_					=       """""".join(
																			    self.byte_encoder[b] for b in token.encode("""utf-8""" ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__UpperCamelCase ).split(""" """ ) )
												return bpe_tokens

					def  lowercase__   (						self       :  Any ,       __UpperCamelCase       :  Union[str, Any] ):
												return self.encoder.get(__UpperCamelCase ,       self.encoder.get(self.unk_token ) )

					def  lowercase__   (						self       :  Union[str, Any] ,       __UpperCamelCase       :  List[Any] ):
												return self.decoder.get(__UpperCamelCase )

					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  Optional[Any] ):
												lowerCamelCase_					=       """""".join(__UpperCamelCase )
												lowerCamelCase_					=       bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" ,       errors=self.errors )
												return text

					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Optional[str] = None ):
												if not os.path.isdir(__UpperCamelCase ):
																			logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
																			return
												lowerCamelCase_					=       os.path.join(
												    __UpperCamelCase ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
												lowerCamelCase_					=       os.path.join(
												    __UpperCamelCase ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )

												with open(__UpperCamelCase ,       """w""" ,       encoding="""utf-8""" ) as f:
																			f.write(json.dumps(self.encoder ,       indent=2 ,       sort_keys=__UpperCamelCase ,       ensure_ascii=__UpperCamelCase ) + """\n""" )

												lowerCamelCase_					=       0
												with open(__UpperCamelCase ,       """w""" ,       encoding="""utf-8""" ) as writer:
																			writer.write("""#version: 0.2\n""" )
																			for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,       key=lambda __UpperCamelCase : kv[1] ):
																										if index != token_index:
																																	logger.warning(
																																	    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																																	    """ Please check that the tokenizer is not corrupted!""" )
																																	lowerCamelCase_					=       token_index
																										writer.write(""" """.join(__UpperCamelCase ) + """\n""" )
																										index += 1

												return vocab_file, merge_file

					def  lowercase__   (						self       :  Any ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):
												if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowerCamelCase_					=       [self.cls_token_id]
												lowerCamelCase_					=       [self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep

					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ,       __UpperCamelCase       :  bool = False ):
												if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=__UpperCamelCase ,       token_ids_a=__UpperCamelCase ,       already_has_special_tokens=__UpperCamelCase )

												if token_ids_a is None:
																			return [1] + ([0] * len(__UpperCamelCase )) + [1]
												return [1] + ([0] * len(__UpperCamelCase )) + [1, 1] + ([0] * len(__UpperCamelCase )) + [1]

					def  lowercase__   (						self       :  Union[str, Any] ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):
												lowerCamelCase_					=       [self.sep_token_id]
												lowerCamelCase_					=       [self.cls_token_id]

												if token_ids_a is None:
																			return len(cls + token_ids_a + sep ) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]

					def  lowercase__   (						self       :  Union[str, Any] ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  List[str]=False ,       **__UpperCamelCase       :  str ):
												lowerCamelCase_					=       kwargs.pop("""add_prefix_space""" ,       self.add_prefix_space )
												if (is_split_into_words or add_prefix_space) and (len(__UpperCamelCase ) > 0 and not text[0].isspace()):
																			lowerCamelCase_					=       """ """ + text
												return (text, kwargs)



					def  lowercase__   (						self       :  str ,       __UpperCamelCase       :  Union[Dict[str, EncodedInput], BatchEncoding] ,       __UpperCamelCase       :  Optional[int] = None ,       __UpperCamelCase       :  PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,       __UpperCamelCase       :  Optional[int] = None ,       __UpperCamelCase       :  Optional[bool] = None ,       ):
												lowerCamelCase_					=       super()._pad(
												    encoded_inputs=__UpperCamelCase ,       max_length=__UpperCamelCase ,       padding_strategy=__UpperCamelCase ,       pad_to_multiple_of=__UpperCamelCase ,       return_attention_mask=__UpperCamelCase ,       )

												# Load from model defaults
												if return_attention_mask is None:
																			lowerCamelCase_					=       """attention_mask""" in self.model_input_names

												if return_attention_mask and "global_attention_mask" in encoded_inputs:
																			lowerCamelCase_					=       encoded_inputs[self.model_input_names[0]]
																			# `global_attention_mask` need to have the same length as other (sequential) inputs.
																			lowerCamelCase_					=       len(encoded_inputs["""global_attention_mask"""] ) != len(__UpperCamelCase )

																			if needs_to_be_padded:
																										lowerCamelCase_					=       len(__UpperCamelCase ) - len(encoded_inputs["""global_attention_mask"""] )

																										if self.padding_side == "right":
																																	# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																																	lowerCamelCase_					=       (
																																	    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																																	)
																										elif self.padding_side == "left":
																																	lowerCamelCase_					=       [-1] * difference + encoded_inputs[
																																	    """global_attention_mask"""
																																	]
																										else:
																																	raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )

												return encoded_inputs







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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
		lowercase   =	'''platform'''

		import jax
		import jax.numpy as jnp

		from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
		    FlaxBlenderbotSmallForConditionalGeneration,
		    FlaxBlenderbotSmallModel,
		    shift_tokens_right,
		)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   Any=None      ,					UpperCAmelCase__					:   Optional[Any]=None      ,					UpperCAmelCase__					:   List[str]=None      ,					UpperCAmelCase__					:   Optional[int]=None      ,					UpperCAmelCase__					:   List[str]=None      ,					UpperCAmelCase__					:   Dict=None      ,					)	->     List[Any]:
							if attention_mask is None:
														lowerCamelCase_					=       np.where(input_ids != config.pad_token_id      ,					1      ,					0							)
							if decoder_attention_mask is None:
														lowerCamelCase_					=       np.where(decoder_input_ids != config.pad_token_id      ,					1      ,					0							)
							if head_mask is None:
														lowerCamelCase_					=       np.ones((config.encoder_layers, config.encoder_attention_heads)							)
							if decoder_head_mask is None:
														lowerCamelCase_					=       np.ones((config.decoder_layers, config.decoder_attention_heads)							)
							if cross_attn_head_mask is None:
														lowerCamelCase_					=       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    __A:

					def __init__(						self       :  str ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  str=1_3 ,       __UpperCamelCase       :  List[str]=7 ,       __UpperCamelCase       :  Tuple=True ,       __UpperCamelCase       :  List[Any]=False ,       __UpperCamelCase       :  Dict=9_9 ,       __UpperCamelCase       :  Tuple=1_6 ,       __UpperCamelCase       :  Any=2 ,       __UpperCamelCase       :  Dict=4 ,       __UpperCamelCase       :  List[str]=4 ,       __UpperCamelCase       :  Optional[Any]="gelu" ,       __UpperCamelCase       :  List[str]=0.1 ,       __UpperCamelCase       :  str=0.1 ,       __UpperCamelCase       :  Union[str, Any]=3_2 ,       __UpperCamelCase       :  str=2 ,       __UpperCamelCase       :  Tuple=1 ,       __UpperCamelCase       :  Optional[Any]=0 ,       __UpperCamelCase       :  str=0.02 ,       ):
												lowerCamelCase_					=       parent
												lowerCamelCase_					=       batch_size
												lowerCamelCase_					=       seq_length
												lowerCamelCase_					=       is_training
												lowerCamelCase_					=       use_labels
												lowerCamelCase_					=       vocab_size
												lowerCamelCase_					=       hidden_size
												lowerCamelCase_					=       num_hidden_layers
												lowerCamelCase_					=       num_attention_heads
												lowerCamelCase_					=       intermediate_size
												lowerCamelCase_					=       hidden_act
												lowerCamelCase_					=       hidden_dropout_prob
												lowerCamelCase_					=       attention_probs_dropout_prob
												lowerCamelCase_					=       max_position_embeddings
												lowerCamelCase_					=       eos_token_id
												lowerCamelCase_					=       pad_token_id
												lowerCamelCase_					=       bos_token_id
												lowerCamelCase_					=       initializer_range

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

												lowerCamelCase_					=       shift_tokens_right(__UpperCamelCase ,       1 ,       2 )

												lowerCamelCase_					=       BlenderbotSmallConfig(
												    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=__UpperCamelCase ,       )
												lowerCamelCase_					=       prepare_blenderbot_inputs_dict(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
												return config, inputs_dict

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_	, lowerCamelCase_					=       self.prepare_config_and_inputs()
												return config, inputs_dict

					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  str ):
												lowerCamelCase_					=       2_0
												lowerCamelCase_					=       model_class_name(__UpperCamelCase )

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

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

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

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

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

												lowerCamelCase_					=       model.decode(__UpperCamelCase ,       __UpperCamelCase )

												lowerCamelCase_					=       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  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  List[str] ):
												lowerCamelCase_					=       2_0
												lowerCamelCase_					=       model_class_name(__UpperCamelCase )

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

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

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

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

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

												lowerCamelCase_					=       model.decode(__UpperCamelCase ,       __UpperCamelCase ,       decoder_attention_mask=__UpperCamelCase )

												lowerCamelCase_					=       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    __A(					unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     9_9

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       np.array(
												    [
												        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
												        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
												        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
												        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
												        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
												        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
												        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
												        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
												        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
												        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
												        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
												        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
												        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
												    ] ,       dtype=np.intaa ,       )

												lowerCamelCase_					=       input_ids.shape[0]
												lowerCamelCase_					=       BlenderbotSmallConfig(
												    vocab_size=self.vocab_size ,       d_model=2_4 ,       encoder_layers=2 ,       decoder_layers=2 ,       encoder_attention_heads=2 ,       decoder_attention_heads=2 ,       encoder_ffn_dim=3_2 ,       decoder_ffn_dim=3_2 ,       max_position_embeddings=4_8 ,       eos_token_id=2 ,       pad_token_id=1 ,       bos_token_id=0 ,       )
												return config, input_ids, batch_size

					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       self._get_config_and_data()
												lowerCamelCase_					=       FlaxBlenderbotSmallForConditionalGeneration(__UpperCamelCase )
												lowerCamelCase_					=       lm_model(input_ids=__UpperCamelCase )
												lowerCamelCase_					=       (batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape ,       __UpperCamelCase )

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       BlenderbotSmallConfig(
												    vocab_size=self.vocab_size ,       d_model=1_4 ,       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=4_8 ,       )
												lowerCamelCase_					=       FlaxBlenderbotSmallForConditionalGeneration(__UpperCamelCase )
												lowerCamelCase_					=       np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]] ,       dtype=np.intaa )
												lowerCamelCase_					=       np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] ,       dtype=np.intaa )
												lowerCamelCase_					=       lm_model(input_ids=__UpperCamelCase ,       decoder_input_ids=__UpperCamelCase )
												lowerCamelCase_					=       (*summary.shape, config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape ,       __UpperCamelCase )



					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]] ,       dtype=np.intaa )
												lowerCamelCase_					=       shift_tokens_right(__UpperCamelCase ,       1 ,       2 )
												lowerCamelCase_					=       np.equal(__UpperCamelCase ,       1 ).astype(np.floataa ).sum()
												lowerCamelCase_					=       np.equal(__UpperCamelCase ,       1 ).astype(np.floataa ).sum()
												self.assertEqual(shifted.shape ,       input_ids.shape )
												self.assertEqual(__UpperCamelCase ,       n_pad_before - 1 )
												self.assertTrue(np.equal(shifted[:, 0] ,       2 ).all() )





@require_flax
class    __A(					UpperCAmelCase  ,		unittest.TestCase  ,		UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     True
					SCREAMING_SNAKE_CASE           =     (
					    (
					        FlaxBlenderbotSmallModel,
					        FlaxBlenderbotSmallForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE           =     (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       FlaxBlenderbotSmallModelTester(self )

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_	, lowerCamelCase_					=       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(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__ ):
																										lowerCamelCase_					=       self._prepare_for_class(__UpperCamelCase ,       __UpperCamelCase )
																										lowerCamelCase_					=       model_class(__UpperCamelCase )

																										@jax.jit
																										def encode_jitted(__UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict=None ,       **__UpperCamelCase       :  Tuple ):
																																	return model.encode(input_ids=__UpperCamelCase ,       attention_mask=__UpperCamelCase )

																										with self.subTest("""JIT Enabled""" ):
																																	lowerCamelCase_					=       encode_jitted(**__UpperCamelCase ).to_tuple()

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

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

					def  lowercase__   (						self       :  Any ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()

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

																										lowerCamelCase_					=       {
																										    """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(__UpperCamelCase       :  int ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  Union[str, Any] ):
																																	return model.decode(
																																	    decoder_input_ids=__UpperCamelCase ,       decoder_attention_mask=__UpperCamelCase ,       encoder_outputs=__UpperCamelCase ,       )

																										with self.subTest("""JIT Enabled""" ):
																																	lowerCamelCase_					=       decode_jitted(**__UpperCamelCase ).to_tuple()

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

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



					@slow
					def  lowercase__   (						self       :  str ):
												for model_class_name in self.all_model_classes:
																			lowerCamelCase_					=       model_class_name.from_pretrained("""facebook/blenderbot_small-90M""" )
																			# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																			lowerCamelCase_					=       np.ones((1, 1) ) * model.config.eos_token_id
																			lowerCamelCase_					=       model(__UpperCamelCase )
																			self.assertIsNotNone(__UpperCamelCase )





