



"""simple docstring"""






import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer






def      snake_case__   (		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    Tuple=True							,		__lowerCamelCase				:    Dict="pt"	):
			"""simple docstring"""
			lowerCamelCase__		:		str			={'''add_prefix_space''': True} if isinstance(__lowerCamelCase							,		__lowerCamelCase	) and not line.startswith(''' '''	) else {}
			lowerCamelCase__		:		int			=padding_side
			return tokenizer(
			    [line]							,		max_length=__lowerCamelCase							,		padding='''max_length''' if pad_to_max_length else None							,		truncation=__lowerCamelCase							,		return_tensors=__lowerCamelCase							,		add_special_tokens=__lowerCamelCase							,		**__lowerCamelCase							,		)






def      snake_case__   (		__lowerCamelCase				:    str							,		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    Optional[int]=None							,		):
			"""simple docstring"""
			lowerCamelCase__		:		Any			=input_ids.ne(__lowerCamelCase	).any(dim=0	)
			if attention_mask is None:
						return input_ids[:, keep_column_mask]
			else:
						return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  str,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  str="train",  lowerCamelCase							:  List[Any]=None,  lowerCamelCase							:  Tuple=None,  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  int="",  )->					List[Any]:
								super().__init__()
								lowerCamelCase__		:		Tuple			=Path(lowerCamelCase     ).joinpath(type_path + '''.source'''     )
								lowerCamelCase__		:		str			=Path(lowerCamelCase     ).joinpath(type_path + '''.target'''     )
								lowerCamelCase__		:		Dict			=self.get_char_lens(self.src_file     )
								lowerCamelCase__		:		Tuple			=max_source_length
								lowerCamelCase__		:		Optional[int]			=max_target_length
								assert min(self.src_lens     ) > 0, F'''found empty line in {self.src_file}'''
								lowerCamelCase__		:		Dict			=tokenizer
								lowerCamelCase__		:		List[str]			=prefix
								if n_obs is not None:
											lowerCamelCase__		:		int			=self.src_lens[:n_obs]
								lowerCamelCase__		:		Dict			=src_lang
								lowerCamelCase__		:		Tuple			=tgt_lang



					def __len__(       self							:  Dict     )->					Optional[int]:
								return len(self.src_lens     )



					def __getitem__(       self							:  List[str],  lowerCamelCase							:  Optional[int]     )->					Dict[str, torch.Tensor]:
								lowerCamelCase__		:		List[Any]			=index + 1  # linecache starts at 1
								lowerCamelCase__		:		Optional[int]			=self.prefix + linecache.getline(str(self.src_file     ),  lowerCamelCase     ).rstrip('''\n'''     )
								lowerCamelCase__		:		Optional[Any]			=linecache.getline(str(self.tgt_file     ),  lowerCamelCase     ).rstrip('''\n'''     )
								assert source_line, F'''empty source line for index {index}'''
								assert tgt_line, F'''empty tgt line for index {index}'''

								# Need to add eos token manually for T5
								if isinstance(self.tokenizer,  lowerCamelCase     ):
											source_line += self.tokenizer.eos_token
											tgt_line += self.tokenizer.eos_token

								# Pad source and target to the right
								lowerCamelCase__		:		Optional[int]			=(
								    self.tokenizer.question_encoder if isinstance(self.tokenizer,  lowerCamelCase     ) else self.tokenizer
								)
								lowerCamelCase__		:		Tuple			=self.tokenizer.generator if isinstance(self.tokenizer,  lowerCamelCase     ) else self.tokenizer

								lowerCamelCase__		:		Optional[int]			=encode_line(lowerCamelCase,  lowerCamelCase,  self.max_source_length,  '''right'''     )
								lowerCamelCase__		:		str			=encode_line(lowerCamelCase,  lowerCamelCase,  self.max_target_length,  '''right'''     )

								lowerCamelCase__		:		str			=source_inputs['''input_ids'''].squeeze()
								lowerCamelCase__		:		str			=target_inputs['''input_ids'''].squeeze()
								lowerCamelCase__		:		Union[str, Any]			=source_inputs['''attention_mask'''].squeeze()
								return {
								    "input_ids": source_ids,
								    "attention_mask": src_mask,
								    "decoder_input_ids": target_ids,
								}



					@staticmethod
					def 		snake_case       (       lowerCamelCase							:  Union[str, Any]     )->					Optional[int]:
								return [len(lowerCamelCase     ) for x in Path(lowerCamelCase     ).open().readlines()]




					def 		snake_case       (       self							:  str,  lowerCamelCase							:  str     )->					Dict[str, torch.Tensor]:
								lowerCamelCase__		:		List[Any]			=torch.stack([x['''input_ids'''] for x in batch]     )
								lowerCamelCase__		:		int			=torch.stack([x['''attention_mask'''] for x in batch]     )
								lowerCamelCase__		:		Union[str, Any]			=torch.stack([x['''decoder_input_ids'''] for x in batch]     )
								lowerCamelCase__		:		str			=(
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer,  lowerCamelCase     )
								    else self.tokenizer.pad_token_id
								)
								lowerCamelCase__		:		List[str]			=(
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer,  lowerCamelCase     )
								    else self.tokenizer.pad_token_id
								)
								lowerCamelCase__		:		Optional[int]			=trim_batch(lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__	,   lowerCamelCase__		:		Any			=trim_batch(lowerCamelCase,  lowerCamelCase,  attention_mask=lowerCamelCase     )
								lowerCamelCase__		:		List[str]			={
								    '''input_ids''': source_ids,
								    '''attention_mask''': source_mask,
								    '''decoder_input_ids''': y,
								}
								return batch





_lowercase : Any      						=						getLogger(__name__)






def      snake_case__   (		__lowerCamelCase				:    List[List]	):
			"""simple docstring"""
			return list(itertools.chain.from_iterable(__lowerCamelCase	)	)






def      snake_case__   (		__lowerCamelCase				:    str	):
			"""simple docstring"""
			lowerCamelCase__		:		Dict			=get_git_info()
			save_json(__lowerCamelCase							,		os.path.join(__lowerCamelCase							,		'''git_log.json'''	)	)






def      snake_case__   (		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    Optional[int]							,		__lowerCamelCase				:    Dict=4							,		**__lowerCamelCase				:    int	):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''w'''	) as f:
						json.dump(__lowerCamelCase							,		__lowerCamelCase							,		indent=__lowerCamelCase							,		**__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			with open(__lowerCamelCase	) as f:
						return json.load(__lowerCamelCase	)






def      snake_case__   (		):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]			=git.Repo(search_parent_directories=__lowerCamelCase	)
			lowerCamelCase__		:		Union[str, Any]			={
			    '''repo_id''': str(__lowerCamelCase	),
			    '''repo_sha''': str(repo.head.object.hexsha	),
			    '''repo_branch''': str(repo.active_branch	),
			    '''hostname''': str(socket.gethostname()	),
			}
			return repo_infos






def      snake_case__   (		__lowerCamelCase				:    Callable							,		__lowerCamelCase				:    Iterable	):
			"""simple docstring"""
			return list(map(__lowerCamelCase							,		__lowerCamelCase	)	)






def      snake_case__   (		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''wb'''	) as f:
						return pickle.dump(__lowerCamelCase							,		__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    str	):
			"""simple docstring"""

			def remove_articles(__lowerCamelCase				:    List[Any]	):
						return re.sub(R'''\b(a|an|the)\b'''							,		''' '''							,		__lowerCamelCase	)

			def white_space_fix(__lowerCamelCase				:    Any	):
						return " ".join(text.split()	)

			def remove_punc(__lowerCamelCase				:    Optional[Any]	):
						lowerCamelCase__		:		Tuple			=set(string.punctuation	)
						return "".join(ch for ch in text if ch not in exclude	)

			def lower(__lowerCamelCase				:    Any	):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase	)	)	)	)






def      snake_case__   (		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]			=normalize_answer(__lowerCamelCase	).split()
			lowerCamelCase__		:		List[str]			=normalize_answer(__lowerCamelCase	).split()
			lowerCamelCase__		:		Optional[int]			=Counter(__lowerCamelCase	) & Counter(__lowerCamelCase	)
			lowerCamelCase__		:		Union[str, Any]			=sum(common.values()	)
			if num_same == 0:
						return 0
			lowerCamelCase__		:		Dict			=1.0 * num_same / len(__lowerCamelCase	)
			lowerCamelCase__		:		List[str]			=1.0 * num_same / len(__lowerCamelCase	)
			lowerCamelCase__		:		Union[str, Any]			=(2 * precision * recall) / (precision + recall)
			return fa






def      snake_case__   (		__lowerCamelCase				:    Dict							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			return normalize_answer(__lowerCamelCase	) == normalize_answer(__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			assert len(__lowerCamelCase	) == len(__lowerCamelCase	)
			lowerCamelCase__		:		Any			=0
			for hypo, pred in zip(__lowerCamelCase							,		__lowerCamelCase	):
						em += exact_match_score(__lowerCamelCase							,		__lowerCamelCase	)
			if len(__lowerCamelCase	) > 0:
						em /= len(__lowerCamelCase	)
			return {"em": em}






def      snake_case__   (		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			return model_prefix.startswith('''rag'''	)






def      snake_case__   (		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    str	):
			"""simple docstring"""
			lowerCamelCase__		:		Any			={p: p for p in extra_params}
			# T5 models don't have `dropout` param, they have `dropout_rate` instead
			lowerCamelCase__		:		Optional[int]			='''dropout_rate'''
			for p in extra_params:
						if getattr(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	):
									if not hasattr(__lowerCamelCase							,		__lowerCamelCase	) and not hasattr(__lowerCamelCase							,		equivalent_param[p]	):
												logger.info('''config doesn\'t have a `{}` attribute'''.format(__lowerCamelCase	)	)
												delattr(__lowerCamelCase							,		__lowerCamelCase	)
												continue
									lowerCamelCase__		:		List[Any]			=p if hasattr(__lowerCamelCase							,		__lowerCamelCase	) else equivalent_param[p]
									setattr(__lowerCamelCase							,		__lowerCamelCase							,		getattr(__lowerCamelCase							,		__lowerCamelCase	)	)
									delattr(__lowerCamelCase							,		__lowerCamelCase	)
			return hparams, config









"""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 : List[Any]      						=						"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      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    str							,		__lowerCamelCase				:    Optional[Any]=None							,		__lowerCamelCase				:    str=None							,		__lowerCamelCase				:    Tuple=None							,		__lowerCamelCase				:    List[Any]=None							,		__lowerCamelCase				:    Tuple=None							,		__lowerCamelCase				:    str=None							,		):
			"""simple docstring"""
			if attention_mask is None:
						lowerCamelCase__		:		Any			=np.where(input_ids != config.pad_token_id							,		1							,		0	)
			if decoder_attention_mask is None:
						lowerCamelCase__		:		str			=np.where(decoder_input_ids != config.pad_token_id							,		1							,		0	)
			if head_mask is None:
						lowerCamelCase__		:		Dict			=np.ones((config.encoder_layers, config.encoder_attention_heads)	)
			if decoder_head_mask is None:
						lowerCamelCase__		:		str			=np.ones((config.decoder_layers, config.decoder_attention_heads)	)
			if cross_attn_head_mask is None:
						lowerCamelCase__		:		Dict			=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        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





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



					def 		snake_case       (       self							:  Optional[Any]     )->					str:
								lowerCamelCase__		:		Dict			=np.clip(ids_tensor([self.batch_size, self.seq_length - 1],  self.vocab_size     ),  3,  self.vocab_size     )
								lowerCamelCase__		:		Union[str, Any]			=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),  dtype=np.intaa     )),  -1     )

								lowerCamelCase__		:		Dict			=shift_tokens_right(lowerCamelCase,  1,  2     )

								lowerCamelCase__		:		Optional[Any]			=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,  )
								lowerCamelCase__		:		List[str]			=prepare_blenderbot_inputs_dict(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     )
								return config, inputs_dict



					def 		snake_case       (       self							:  str     )->					Optional[Any]:
								lowerCamelCase__	,   lowerCamelCase__		:		Any			=self.prepare_config_and_inputs()
								return config, inputs_dict



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Dict,  lowerCamelCase							:  Tuple     )->					Optional[Any]:
								lowerCamelCase__		:		Union[str, Any]			=20
								lowerCamelCase__		:		Optional[int]			=model_class_name(lowerCamelCase     )

								lowerCamelCase__		:		Optional[Any]			=model.encode(inputs_dict['''input_ids''']     )

								lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

								lowerCamelCase__		:		Any			=model.init_cache(decoder_input_ids.shape[0],  lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		Dict			=jnp.ones((decoder_input_ids.shape[0], max_decoder_length),  dtype='''i4'''     )

								lowerCamelCase__		:		List[Any]			=jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :],  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),  )
								lowerCamelCase__		:		int			=model.decode(
								    decoder_input_ids[:, :-1],  lowerCamelCase,  decoder_attention_mask=lowerCamelCase,  past_key_values=lowerCamelCase,  decoder_position_ids=lowerCamelCase,  )

								lowerCamelCase__		:		Optional[int]			=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],  dtype='''i4'''     )
								lowerCamelCase__		:		Union[str, Any]			=model.decode(
								    decoder_input_ids[:, -1:],  lowerCamelCase,  decoder_attention_mask=lowerCamelCase,  past_key_values=outputs_cache.past_key_values,  decoder_position_ids=lowerCamelCase,  )

								lowerCamelCase__		:		int			=model.decode(lowerCamelCase,  lowerCamelCase     )

								lowerCamelCase__		:		Dict			=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							:  str,  lowerCamelCase							:  str,  lowerCamelCase							:  str,  lowerCamelCase							:  str     )->					List[str]:
								lowerCamelCase__		:		List[Any]			=20
								lowerCamelCase__		:		List[Any]			=model_class_name(lowerCamelCase     )

								lowerCamelCase__		:		Union[str, Any]			=model.encode(inputs_dict['''input_ids''']     )

								lowerCamelCase__	,   lowerCamelCase__		:		str			=(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

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

								lowerCamelCase__		:		List[str]			=model.init_cache(decoder_input_ids.shape[0],  lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		Union[str, Any]			=jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :],  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),  )

								lowerCamelCase__		:		List[Any]			=model.decode(
								    decoder_input_ids[:, :-1],  lowerCamelCase,  decoder_attention_mask=lowerCamelCase,  past_key_values=lowerCamelCase,  decoder_position_ids=lowerCamelCase,  )
								lowerCamelCase__		:		List[str]			=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],  dtype='''i4'''     )
								lowerCamelCase__		:		Optional[Any]			=model.decode(
								    decoder_input_ids[:, -1:],  lowerCamelCase,  past_key_values=outputs_cache.past_key_values,  decoder_attention_mask=lowerCamelCase,  decoder_position_ids=lowerCamelCase,  )

								lowerCamelCase__		:		Optional[Any]			=model.decode(lowerCamelCase,  lowerCamelCase,  decoder_attention_mask=lowerCamelCase     )

								lowerCamelCase__		:		List[Any]			=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        __SCREAMING_SNAKE_CASE	(      unittest.TestCase   ):


					'''simple docstring'''





					_a   			=							9_9



					def 		snake_case       (       self							:  Optional[int]     )->					Optional[Any]:
								lowerCamelCase__		:		Union[str, Any]			=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,  )

								lowerCamelCase__		:		Any			=input_ids.shape[0]
								lowerCamelCase__		:		Any			=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 		snake_case       (       self							:  Any     )->					Union[str, Any]:
								lowerCamelCase__	,   lowerCamelCase__	,   lowerCamelCase__		:		List[Any]			=self._get_config_and_data()
								lowerCamelCase__		:		int			=FlaxBlenderbotForConditionalGeneration(lowerCamelCase     )
								lowerCamelCase__		:		str			=lm_model(input_ids=lowerCamelCase     )
								lowerCamelCase__		:		List[Any]			=(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape,  lowerCamelCase     )



					def 		snake_case       (       self							:  Tuple     )->					Optional[Any]:
								lowerCamelCase__		:		Union[str, Any]			=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,  )
								lowerCamelCase__		:		Union[str, Any]			=FlaxBlenderbotForConditionalGeneration(lowerCamelCase     )
								lowerCamelCase__		:		List[Any]			=np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],  dtype=np.intaa     )
								lowerCamelCase__		:		Optional[Any]			=np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],  dtype=np.intaa     )
								lowerCamelCase__		:		Optional[int]			=lm_model(input_ids=lowerCamelCase,  decoder_input_ids=lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=(*summary.shape, config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape,  lowerCamelCase     )




					def 		snake_case       (       self							:  Union[str, Any]     )->					Union[str, Any]:
								lowerCamelCase__		:		Optional[int]			=np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],  dtype=np.intaa     )
								lowerCamelCase__		:		Optional[Any]			=shift_tokens_right(lowerCamelCase,  1,  2     )
								lowerCamelCase__		:		str			=np.equal(lowerCamelCase,  1     ).astype(np.floataa     ).sum()
								lowerCamelCase__		:		List[str]			=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        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_		,   unittest.TestCase		,   lowerCAmelCase_   ):


					'''simple docstring'''





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



					def 		snake_case       (       self							:  Union[str, Any]     )->					List[str]:
								lowerCamelCase__		:		str			=FlaxBlenderbotModelTester(self     )



					def 		snake_case       (       self							:  Optional[int]     )->					int:
								lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=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							:  List[str]     )->					Union[str, Any]:
								lowerCamelCase__	,   lowerCamelCase__		:		Union[str, Any]			=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							:  List[Any]     )->					Tuple:
								lowerCamelCase__	,   lowerCamelCase__		:		int			=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__     ):
														lowerCamelCase__		:		List[Any]			=self._prepare_for_class(lowerCamelCase,  lowerCamelCase     )
														lowerCamelCase__		:		int			=model_class(lowerCamelCase     )

														@jax.jit
														def encode_jitted(lowerCamelCase							:  int,  lowerCamelCase							:  Union[str, Any]=None,  **lowerCamelCase							:  List[str]     ):
																	return model.encode(input_ids=lowerCamelCase,  attention_mask=lowerCamelCase     )

														with self.subTest('''JIT Enabled'''     ):
																	lowerCamelCase__		:		Any			=encode_jitted(**lowerCamelCase     ).to_tuple()

														with self.subTest('''JIT Disabled'''     ):
																	with jax.disable_jit():
																				lowerCamelCase__		:		Dict			=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							:  List[str]     )->					Dict:
								lowerCamelCase__	,   lowerCamelCase__		:		Any			=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__     ):
														lowerCamelCase__		:		Optional[Any]			=model_class(lowerCamelCase     )
														lowerCamelCase__		:		List[Any]			=model.encode(inputs_dict['''input_ids'''],  inputs_dict['''attention_mask''']     )

														lowerCamelCase__		:		Optional[int]			={
														    '''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							:  Union[str, Any],  lowerCamelCase							:  Any,  lowerCamelCase							:  Tuple     ):
																	return model.decode(
																	    decoder_input_ids=lowerCamelCase,  decoder_attention_mask=lowerCamelCase,  encoder_outputs=lowerCamelCase,  )

														with self.subTest('''JIT Enabled'''     ):
																	lowerCamelCase__		:		Union[str, Any]			=decode_jitted(**lowerCamelCase     ).to_tuple()

														with self.subTest('''JIT Disabled'''     ):
																	with jax.disable_jit():
																				lowerCamelCase__		:		Optional[Any]			=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							:  Tuple     )->					Tuple:
								for model_class_name in self.all_model_classes:
											lowerCamelCase__		:		int			=model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''     )
											# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
											lowerCamelCase__		:		Union[str, Any]			=np.ones((1, 1)     ) * model.config.eos_token_id
											lowerCamelCase__		:		Optional[Any]			=model(lowerCamelCase     )
											self.assertIsNotNone(lowerCamelCase     )




					@unittest.skipUnless(jax_device != '''cpu''',  '''3B test too slow on CPU.'''     )
					@slow
					def 		snake_case       (       self							:  Optional[int]     )->					Tuple:
								lowerCamelCase__		:		List[Any]			={'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
								lowerCamelCase__		:		Optional[int]			={'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

								lowerCamelCase__		:		Tuple			=FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B''',  from_pt=lowerCamelCase     )
								lowerCamelCase__		:		int			=BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''     )

								lowerCamelCase__		:		str			=['''Sam''']
								lowerCamelCase__		:		Union[str, Any]			=tokenizer(lowerCamelCase,  return_tensors='''jax'''     )

								lowerCamelCase__		:		Tuple			=model.generate(**lowerCamelCase,  **lowerCamelCase     )
								lowerCamelCase__		:		Tuple			='''Sam is a great name. It means "sun" in Gaelic.'''

								lowerCamelCase__		:		Union[str, Any]			=tokenizer.batch_decode(lowerCamelCase,  **lowerCamelCase     )
								assert generated_txt[0].strip() == tgt_text




