






"""simple docstring"""

import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
			import bitsandbytes as bnb

from copy import deepcopy


lowercase    			=       logging.getLogger(__name__)






def _lowerCAmelCase	(  __lowerCamelCase:torch.nn.Module ,						__lowerCamelCase:BnbQuantizationConfig ,						__lowerCamelCase:Union[str, os.PathLike] = None ,						__lowerCamelCase:Optional[Dict[str, Union[int, str, torch.device]]] = None ,						__lowerCamelCase:Optional[List[str]] = None ,						__lowerCamelCase:Optional[Dict[Union[int, str], Union[int, str]]] = None ,						__lowerCamelCase:Optional[Union[str, os.PathLike]] = None ,						__lowerCamelCase:bool = False ,						):

						'''simple docstring'''






						__magic_name__            =     bnb_quantization_config.load_in_abit
						__magic_name__            =     bnb_quantization_config.load_in_abit

						if load_in_abit and not is_abit_bnb_available():
												raise ImportError(
												    "You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"
												    " make sure you have the latest version of `bitsandbytes` installed."   )
						if load_in_abit and not is_abit_bnb_available():
												raise ValueError(
												    "You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"
												    "make sure you have the latest version of `bitsandbytes` installed."   )

						__magic_name__            =     []
						# custom device map
						if isinstance(__lowerCamelCase ,						__lowerCamelCase   ) and len(device_map.keys()   ) > 1:
												__magic_name__            =     [key for key, value in device_map.items() if value in ["disk", "cpu"]]

						# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
						if bnb_quantization_config.skip_modules is None:
												__magic_name__            =     get_keys_to_not_convert(__lowerCamelCase   )

						# add cpu modules to skip modules only for 4-bit modules
						if load_in_abit:
												bnb_quantization_config.skip_modules.extend(__lowerCamelCase   )
						__magic_name__            =     bnb_quantization_config.skip_modules

						# We add the modules we want to keep in full precision
						if bnb_quantization_config.keep_in_fpaa_modules is None:
												__magic_name__            =     []
						__magic_name__            =     bnb_quantization_config.keep_in_fpaa_modules
						modules_to_not_convert.extend(__lowerCamelCase   )

						# compatibility with peft
						__magic_name__            =     load_in_abit
						__magic_name__            =     load_in_abit

						__magic_name__            =     get_parameter_device(__lowerCamelCase   )
						if model_device.type != "meta":
												# quantization of an already loaded model
												logger.warning(
												    "It is not recommended to quantize a loaded model. "
												    "The model should be instantiated under the `init_empty_weights` context manager."   )
												__magic_name__            =     replace_with_bnb_layers(__lowerCamelCase ,						__lowerCamelCase ,						modules_to_not_convert=__lowerCamelCase   )
												# convert param to the right dtype
												__magic_name__            =     bnb_quantization_config.torch_dtype
												for name, param in model.state_dict().items():
																		if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules   ):
																								param.to(torch.floataa   )
																								if param.dtype != torch.floataa:
																														__magic_name__            =     name.replace(".weight" ,						""   ).replace(".bias" ,						""   )
																														__magic_name__            =     getattr(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )
																														if param is not None:
																																				param.to(torch.floataa   )
																		elif torch.is_floating_point(__lowerCamelCase   ):
																								param.to(__lowerCamelCase   )
												if model_device.type == "cuda":
																		# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
																		model.cuda(torch.cuda.current_device()   )
																		torch.cuda.empty_cache()
												elif torch.cuda.is_available():
																		model.to(torch.cuda.current_device()   )
												else:
																		raise RuntimeError("No GPU found. A GPU is needed for quantization."   )
												logger.info(
												    f'''The model device type is {model_device.type}. However, cuda is needed for quantization.'''
												    "We move the model to cuda."   )
												return model

						elif weights_location is None:
												raise RuntimeError(
												    f'''`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} '''   )

						else:
												with init_empty_weights():
																		__magic_name__            =     replace_with_bnb_layers(
																		    __lowerCamelCase ,						__lowerCamelCase ,						modules_to_not_convert=__lowerCamelCase   )

												__magic_name__            =     get_quantized_model_device_map(
												    __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						max_memory=__lowerCamelCase ,						no_split_module_classes=__lowerCamelCase ,						)
												if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
																		__magic_name__            =     True

												__magic_name__            =     any(x in list(device_map.values()   ) for x in ["cpu", "disk"]   )

												load_checkpoint_in_model(
												    __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						dtype=bnb_quantization_config.torch_dtype ,						offload_folder=__lowerCamelCase ,						offload_state_dict=__lowerCamelCase ,						keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules ,						offload_abit_bnb=load_in_abit and offload ,						)
												return dispatch_model(__lowerCamelCase ,						device_map=__lowerCamelCase ,						offload_dir=__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int] ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:int=None ,						__lowerCamelCase:Dict=None ,						__lowerCamelCase:Optional[int]=None   ):

						'''simple docstring'''






						if device_map is None:
												if torch.cuda.is_available():
																		__magic_name__            =     {"": torch.cuda.current_device()}
												else:
																		raise RuntimeError("No GPU found. A GPU is needed for quantization."   )
												logger.info("The device_map was not initialized." "Setting device_map to `{'':torch.cuda.current_device()}`."   )

						if isinstance(__lowerCamelCase ,						__lowerCamelCase   ):
												if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
																		raise ValueError(
																		    "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or "
																		    "'sequential'."   )

												__magic_name__            =     {}
												special_dtypes.update(
												    {
												        name: bnb_quantization_config.torch_dtype
												        for name, _ in model.named_parameters()
												        if any(m in name for m in bnb_quantization_config.skip_modules   )
												    }   )
												special_dtypes.update(
												    {
												        name: torch.floataa
												        for name, _ in model.named_parameters()
												        if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules   )
												    }   )

												__magic_name__            =     {}
												__magic_name__            =     special_dtypes
												__magic_name__            =     no_split_module_classes
												__magic_name__            =     bnb_quantization_config.target_dtype

												# get max_memory for each device.
												if device_map != "sequential":
																		__magic_name__            =     get_balanced_memory(
																		    __lowerCamelCase ,						low_zero=(device_map == "balanced_low_0") ,						max_memory=__lowerCamelCase ,						**__lowerCamelCase ,						)

												__magic_name__            =     max_memory
												__magic_name__            =     infer_auto_device_map(__lowerCamelCase ,						**__lowerCamelCase   )

						if isinstance(__lowerCamelCase ,						__lowerCamelCase   ):
												# check if don't have any quantized module on the cpu
												__magic_name__            =     bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

												__magic_name__            =     {
												    key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
												}
												for device in ["cpu", "disk"]:
																		if device in device_map_without_some_modules.values():
																								if bnb_quantization_config.load_in_abit:
																														raise ValueError(
																														    "\n                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n                        these modules in `torch_dtype`, you need to pass a custom `device_map` to\n                        `load_and_quantize_model`. Check\n                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n                        for more details.\n                        "   )
																								else:
																														logger.info(
																														    "Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit"   )
												del device_map_without_some_modules
						return device_map






def _lowerCAmelCase	(  __lowerCamelCase:int ,						__lowerCamelCase:Tuple ,						__lowerCamelCase:Dict=None ,						__lowerCamelCase:List[str]=None   ):

						'''simple docstring'''






						if modules_to_not_convert is None:
												__magic_name__            =     []

						__magic_name__      ,    __magic_name__            =     _replace_with_bnb_layers(
						    __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )
						if not has_been_replaced:
												logger.warning(
												    "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
												    " this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."
												    " Please double check your model architecture, or submit an issue on github if you think this is"
												    " a bug."   )
						return model






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int] ,						__lowerCamelCase:Optional[int] ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:Optional[int]=None ,						):

						'''simple docstring'''






						__magic_name__            =     False
						for name, module in model.named_children():
												if current_key_name is None:
																		__magic_name__            =     []
												current_key_name.append(__lowerCamelCase   )
												if isinstance(__lowerCamelCase ,						nn.Linear   ) and name not in modules_to_not_convert:
																		# Check if the current key is not in the `modules_to_not_convert`
																		__magic_name__            =     ".".join(__lowerCamelCase   )
																		__magic_name__            =     True
																		for key in modules_to_not_convert:
																								if (
																								    (key in current_key_name_str) and (key + "." in current_key_name_str)
																								) or key == current_key_name_str:
																														__magic_name__            =     False
																														break
																		if proceed:
																								# Load bnb module with empty weight and replace ``nn.Linear` module
																								if bnb_quantization_config.load_in_abit:
																														__magic_name__            =     bnb.nn.LinearabitLt(
																														    module.in_features ,						module.out_features ,						module.bias is not None ,						has_fpaa_weights=__lowerCamelCase ,						threshold=bnb_quantization_config.llm_inta_threshold ,						)
																								elif bnb_quantization_config.load_in_abit:
																														__magic_name__            =     bnb.nn.Linearabit(
																														    module.in_features ,						module.out_features ,						module.bias is not None ,						bnb_quantization_config.bnb_abit_compute_dtype ,						compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant ,						quant_type=bnb_quantization_config.bnb_abit_quant_type ,						)
																								else:
																														raise ValueError("load_in_8bit and load_in_4bit can't be both False"   )
																								__magic_name__            =     module.weight.data
																								if module.bias is not None:
																														__magic_name__            =     module.bias.data
																								bnb_module.requires_grad_(__lowerCamelCase   )
																								setattr(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )
																								__magic_name__            =     True
												if len(list(module.children()   )   ) > 0:
																		__magic_name__      ,    __magic_name__            =     _replace_with_bnb_layers(
																		    __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )
																		__magic_name__            =     has_been_replaced | _has_been_replaced
												# Remove the last key for recursion
												current_key_name.pop(-1   )
						return model, has_been_replaced






def _lowerCAmelCase	(  __lowerCamelCase:Optional[Any]   ):

						'''simple docstring'''






						with init_empty_weights():
												__magic_name__            =     deepcopy(__lowerCamelCase   )  # this has 0 cost since it is done inside `init_empty_weights` context manager`

						__magic_name__            =     find_tied_parameters(__lowerCamelCase   )
						# For compatibility with Accelerate < 0.18
						if isinstance(__lowerCamelCase ,						__lowerCamelCase   ):
												__magic_name__            =     sum(list(tied_params.values()   ) ,						[]   ) + list(tied_params.keys()   )
						else:
												__magic_name__            =     sum(__lowerCamelCase ,						[]   )
						__magic_name__            =     len(__lowerCamelCase   ) > 0

						# Check if it is a base model
						__magic_name__            =     False
						if hasattr(__lowerCamelCase ,						"base_model_prefix"   ):
												__magic_name__            =     not hasattr(__lowerCamelCase ,						model.base_model_prefix   )

						# Ignore this for base models (BertModel, GPT2Model, etc.)
						if (not has_tied_params) and is_base_model:
												return []

						# otherwise they have an attached head
						__magic_name__            =     list(model.named_children()   )
						__magic_name__            =     [list_modules[-1][0]]

						# add last module together with tied weights
						__magic_name__            =     set(__lowerCamelCase   ) - set(__lowerCamelCase   )
						__magic_name__            =     list(set(__lowerCamelCase   )   ) + list(__lowerCamelCase   )

						# remove ".weight" from the keys
						__magic_name__            =     [".weight", ".bias"]
						__magic_name__            =     []
						for name in list_untouched:
												for name_to_remove in names_to_remove:
																		if name_to_remove in name:
																								__magic_name__            =     name.replace(__lowerCamelCase ,						""   )
												filtered_module_names.append(__lowerCamelCase   )

						return filtered_module_names






def _lowerCAmelCase	(  __lowerCamelCase:Dict   ):

						'''simple docstring'''






						for m in model.modules():
												if isinstance(__lowerCamelCase ,						bnb.nn.Linearabit   ):
																		return True
						return False






def _lowerCAmelCase	(  __lowerCamelCase:nn.Module   ):

						'''simple docstring'''






						return next(parameter.parameters()   ).device






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int] ,						__lowerCamelCase:Any ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:List[str] ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						if fpaa_statistics is None:
												set_module_tensor_to_device(__lowerCamelCase ,						__lowerCamelCase ,						0 ,						dtype=__lowerCamelCase ,						value=__lowerCamelCase   )
												__magic_name__            =     param_name
												__magic_name__            =     model
												if "." in tensor_name:
																		__magic_name__            =     tensor_name.split("."   )
																		for split in splits[:-1]:
																								__magic_name__            =     getattr(__lowerCamelCase ,						__lowerCamelCase   )
																								if new_module is None:
																														raise ValueError(f'''{module} has no attribute {split}.'''   )
																								__magic_name__            =     new_module
																		__magic_name__            =     splits[-1]
												# offload weights
												__magic_name__            =     False
												offload_weight(module._parameters[tensor_name] ,						__lowerCamelCase ,						__lowerCamelCase ,						index=__lowerCamelCase   )
												if hasattr(module._parameters[tensor_name] ,						"SCB"   ):
																		offload_weight(
																		    module._parameters[tensor_name].SCB ,						param_name.replace("weight" ,						"SCB"   ) ,						__lowerCamelCase ,						index=__lowerCamelCase ,						)
						else:
												offload_weight(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						index=__lowerCamelCase   )
												offload_weight(__lowerCamelCase ,						param_name.replace("weight" ,						"SCB"   ) ,						__lowerCamelCase ,						index=__lowerCamelCase   )

						set_module_tensor_to_device(__lowerCamelCase ,						__lowerCamelCase ,						"meta" ,						dtype=__lowerCamelCase ,						value=torch.empty(*param.size()   )   )











"""simple docstring"""

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
			lowercase    			=       '''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 _lowerCAmelCase	(  __lowerCamelCase:List[Any] ,						__lowerCamelCase:int ,						__lowerCamelCase:List[Any]=None ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:List[str]=None ,						__lowerCamelCase:Optional[int]=None ,						__lowerCamelCase:Optional[int]=None ,						):

						'''simple docstring'''






						if attention_mask is None:
												__magic_name__            =     np.where(input_ids != config.pad_token_id ,						1 ,						0   )
						if decoder_attention_mask is None:
												__magic_name__            =     np.where(decoder_input_ids != config.pad_token_id ,						1 ,						0   )
						if head_mask is None:
												__magic_name__            =     np.ones((config.encoder_layers, config.encoder_attention_heads)   )
						if decoder_head_mask is None:
												__magic_name__            =     np.ones((config.decoder_layers, config.decoder_attention_heads)   )
						if cross_attn_head_mask is None:
												__magic_name__            =     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							:						Tuple						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Optional[Any]=1_3						,    __lowerCamelCase							:						str=7						,    __lowerCamelCase							:						Optional[Any]=True						,    __lowerCamelCase							:						Dict=False						,    __lowerCamelCase							:						Optional[int]=9_9						,    __lowerCamelCase							:						Tuple=1_6						,    __lowerCamelCase							:						Any=2						,    __lowerCamelCase							:						int=4						,    __lowerCamelCase							:						List[Any]=4						,    __lowerCamelCase							:						List[str]="gelu"						,    __lowerCamelCase							:						Union[str, Any]=0.1						,    __lowerCamelCase							:						Optional[int]=0.1						,    __lowerCamelCase							:						List[str]=3_2						,    __lowerCamelCase							:						int=2						,    __lowerCamelCase							:						Any=1						,    __lowerCamelCase							:						int=0						,    __lowerCamelCase							:						Tuple=0.02						,    )				->					List[Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     seq_length
										__magic_name__            =     is_training
										__magic_name__            =     use_labels
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size
										__magic_name__            =     hidden_act
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     eos_token_id
										__magic_name__            =     pad_token_id
										__magic_name__            =     bos_token_id
										__magic_name__            =     initializer_range







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

										__magic_name__            =     shift_tokens_right(__lowerCamelCase						,    1						,    2	)

										__magic_name__            =     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=__lowerCamelCase						,    )
										__magic_name__            =     prepare_blenderbot_inputs_dict(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)
										return config, inputs_dict







				def     _snake_case					(							self							:						Optional[Any]	)				->					Any:
										__magic_name__      ,    __magic_name__            =     self.prepare_config_and_inputs()
										return config, inputs_dict







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						List[Any]	)				->					Union[str, Any]:
										__magic_name__            =     2_0
										__magic_name__            =     model_class_name(__lowerCamelCase	)

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

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

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

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

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

										__magic_name__            =     model.decode(__lowerCamelCase						,    __lowerCamelCase	)

										__magic_name__            =     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     _snake_case					(							self							:						List[Any]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Tuple	)				->					int:
										__magic_name__            =     2_0
										__magic_name__            =     model_class_name(__lowerCamelCase	)

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

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

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

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

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

										__magic_name__            =     model.decode(__lowerCamelCase						,    __lowerCamelCase						,    decoder_attention_mask=__lowerCamelCase	)

										__magic_name__            =     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  ):
				UpperCAmelCase__        =				9_9







				def     _snake_case					(							self							:						Dict	)				->					Dict:
										__magic_name__            =     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						,    )

										__magic_name__            =     input_ids.shape[0]
										__magic_name__            =     BlenderbotConfig(
										    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     _snake_case					(							self							:						Optional[Any]	)				->					Optional[int]:
										__magic_name__      ,    __magic_name__      ,    __magic_name__            =     self._get_config_and_data()
										__magic_name__            =     FlaxBlenderbotForConditionalGeneration(__lowerCamelCase	)
										__magic_name__            =     lm_model(input_ids=__lowerCamelCase	)
										__magic_name__            =     (batch_size, input_ids.shape[1], config.vocab_size)
										self.assertEqual(outputs["logits"].shape						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[Any]:
										__magic_name__            =     BlenderbotConfig(
										    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						,    )
										__magic_name__            =     FlaxBlenderbotForConditionalGeneration(__lowerCamelCase	)
										__magic_name__            =     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	)
										__magic_name__            =     np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]						,    dtype=np.intaa	)
										__magic_name__            =     lm_model(input_ids=__lowerCamelCase						,    decoder_input_ids=__lowerCamelCase	)
										__magic_name__            =     (*summary.shape, config.vocab_size)
										self.assertEqual(outputs["logits"].shape						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[Any]:
										__magic_name__            =     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	)
										__magic_name__            =     shift_tokens_right(__lowerCamelCase						,    1						,    2	)
										__magic_name__            =     np.equal(__lowerCamelCase						,    1	).astype(np.floataa	).sum()
										__magic_name__            =     np.equal(__lowerCamelCase						,    1	).astype(np.floataa	).sum()
										self.assertEqual(shifted.shape						,    input_ids.shape	)
										self.assertEqual(__lowerCamelCase						,    n_pad_before - 1	)
										self.assertTrue(np.equal(shifted[:, 0]						,    2	).all()	)





@require_flax
class 						A_							(      snake_case_					,							unittest.TestCase					,							snake_case_  ):
				UpperCAmelCase__        =				True
				UpperCAmelCase__        =				(
				    (
				        FlaxBlenderbotModel,
				        FlaxBlenderbotForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				UpperCAmelCase__        =				(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







				def     _snake_case					(							self							:						List[Any]	)				->					Any:
										__magic_name__            =     FlaxBlenderbotModelTester(self	)







				def     _snake_case					(							self							:						int	)				->					Optional[Any]:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[int]	)				->					Union[str, Any]:
										__magic_name__      ,    __magic_name__            =     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(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Tuple	)				->					Dict:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__	):
																						__magic_name__            =     self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)
																						__magic_name__            =     model_class(__lowerCamelCase	)

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

																						with self.subTest("JIT Enabled"	):
																												__magic_name__            =     encode_jitted(**__lowerCamelCase	).to_tuple()

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

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







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Union[str, Any]:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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

																						__magic_name__            =     {
																						    "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(__lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Dict	):
																												return model.decode(
																												    decoder_input_ids=__lowerCamelCase						,    decoder_attention_mask=__lowerCamelCase						,    encoder_outputs=__lowerCamelCase						,    )

																						with self.subTest("JIT Enabled"	):
																												__magic_name__            =     decode_jitted(**__lowerCamelCase	).to_tuple()

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

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







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







				@unittest.skipUnless(jax_device != "cpu"						,    "3B test too slow on CPU."	)
				@slow
				def     _snake_case					(							self							:						int	)				->					List[Any]:
										__magic_name__            =     {"num_beams": 1, "early_stopping": True, "min_length": 1_5, "max_length": 2_5}
										__magic_name__            =     {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

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

										__magic_name__            =     ["Sam"]
										__magic_name__            =     tokenizer(__lowerCamelCase						,    return_tensors="jax"	)

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

										__magic_name__            =     tokenizer.batch_decode(__lowerCamelCase						,    **__lowerCamelCase	)
										assert generated_txt[0].strip() == tgt_text



