
from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel





@require_tf
class 	_lowerCAmelCase					:
				_lowercase					=BlenderbotSmallConfig
				_lowercase					={}
				_lowercase					='''gelu'''




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase=13				,				_UpperCamelCase=7				,				_UpperCamelCase=True				,				_UpperCamelCase=False				,				_UpperCamelCase=99				,				_UpperCamelCase=32				,				_UpperCamelCase=2				,				_UpperCamelCase=4				,				_UpperCamelCase=37				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=20				,				_UpperCamelCase=2				,				_UpperCamelCase=1				,				_UpperCamelCase=0				,				)				->      Optional[Any]:
							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_dropout_prob
							lowerCAmelCase_						=	attention_probs_dropout_prob
							lowerCAmelCase_						=	max_position_embeddings
							lowerCAmelCase_						=	eos_token_id
							lowerCAmelCase_						=	pad_token_id
							lowerCAmelCase_						=	bos_token_id




				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	ids_tensor([self.batch_size, self.seq_length - 1]				,				self.vocab_size     )
							lowerCAmelCase_						=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )				,				1     )
							lowerCAmelCase_						=	tf.concat([input_ids, eos_tensor]				,				axis=1     )

							lowerCAmelCase_						=	ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size     )

							lowerCAmelCase_						=	self.config_cls(
							    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_ids=[2]				,				bos_token_id=self.bos_token_id				,				pad_token_id=self.pad_token_id				,				decoder_start_token_id=self.pad_token_id				,				**self.config_updates				,				)
							lowerCAmelCase_						=	prepare_blenderbot_small_inputs_dict(_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )
							return config, inputs_dict




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Dict:
							lowerCAmelCase_						=	TFBlenderbotSmallModel(config=_UpperCamelCase     ).get_decoder()
							lowerCAmelCase_						=	inputs_dict["input_ids"]

							lowerCAmelCase_						=	input_ids[:1, :]
							lowerCAmelCase_						=	inputs_dict["attention_mask"][:1, :]
							lowerCAmelCase_						=	inputs_dict["head_mask"]
							lowerCAmelCase_						=	1

							# first forward pass
							lowerCAmelCase_						=	model(_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				head_mask=_UpperCamelCase				,				use_cache=_UpperCamelCase     )

							lowerCAmelCase_	,  lowerCAmelCase_						=	outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							lowerCAmelCase_						=	ids_tensor((self.batch_size, 3)				,				config.vocab_size     )
							lowerCAmelCase_						=	tf.cast(ids_tensor((self.batch_size, 3)				,				2     )				,				tf.inta     )

							# append to next input_ids and
							lowerCAmelCase_						=	tf.concat([input_ids, next_tokens]				,				axis=-1     )
							lowerCAmelCase_						=	tf.concat([attention_mask, next_attn_mask]				,				axis=-1     )

							lowerCAmelCase_						=	model(_UpperCamelCase				,				attention_mask=_UpperCamelCase     )[0]
							lowerCAmelCase_						=	model(_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				past_key_values=_UpperCamelCase     )[0]

							self.parent.assertEqual(next_tokens.shape[1]				,				output_from_past.shape[1]     )

							# select random slice
							lowerCAmelCase_						=	int(ids_tensor((1,)				,				output_from_past.shape[-1]     )     )
							lowerCAmelCase_						=	output_from_no_past[:, -3:, random_slice_idx]
							lowerCAmelCase_						=	output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(_UpperCamelCase				,				_UpperCamelCase				,				rtol=1e-3     )






def    lowerCamelCase__				(				__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  Optional[int]=None    ,							__lowerCAmelCase  :  str=None    ,							__lowerCAmelCase  :  Any=None    ,							__lowerCAmelCase  :  List[Any]=None    ,							__lowerCAmelCase  :  Any=None    ,							):




			"""simple docstring"""


			if attention_mask is None:
						lowerCAmelCase_						=	tf.cast(tf.math.not_equal(__lowerCAmelCase    ,							config.pad_token_id				)    ,							tf.inta				)
			if decoder_attention_mask is None:
						lowerCAmelCase_						=	tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape    ,							dtype=tf.inta				),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]    ,							config.pad_token_id				)    ,							tf.inta				),
						    ]    ,							axis=-1    ,							)
			if head_mask is None:
						lowerCAmelCase_						=	tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
			if decoder_head_mask is None:
						lowerCAmelCase_						=	tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
			if cross_attn_head_mask is None:
						lowerCAmelCase_						=	tf.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": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}





@require_tf
class 	_lowerCAmelCase					(	__a					,      __a					,      unittest.TestCase       ):
				_lowercase					=(
				    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
				)
				_lowercase					=(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
				_lowercase					=(
				    {
				        '''conversational''': TFBlenderbotSmallForConditionalGeneration,
				        '''feature-extraction''': TFBlenderbotSmallModel,
				        '''summarization''': TFBlenderbotSmallForConditionalGeneration,
				        '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
				        '''translation''': TFBlenderbotSmallForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				_lowercase					=True
				_lowercase					=False
				_lowercase					=False




				def      __a						(					self     )				->      int:
							lowerCAmelCase_						=	TFBlenderbotSmallModelTester(self     )
							lowerCAmelCase_						=	ConfigTester(self				,				config_class=_UpperCamelCase     )




				def      __a						(					self     )				->      int:
							self.config_tester.run_common_tests()




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase     )





@require_tokenizers
@require_tf
class 	_lowerCAmelCase					(	unittest.TestCase       ):
				_lowercase					=[
				    '''Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '''
				    '''  i\'m going to throw up.\nand why is that?'''
				]
				_lowercase					='''facebook/blenderbot_small-90M'''




				@cached_property
				def      __a						(					self     )				->      Optional[int]:
							# use "old" tokenizer here because of bug when downloading new tokenizer
							return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"     )




				@cached_property
				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
							return model




				@slow
				def      __a						(					self     )				->      Tuple:
							lowerCAmelCase_						=	self.tokenizer(self.src_text				,				return_tensors="tf"     )
							lowerCAmelCase_						=	self.model.generate(
							    model_inputs.input_ids				,				attention_mask=model_inputs.attention_mask				,				num_beams=2				,				use_cache=_UpperCamelCase				,				)
							lowerCAmelCase_						=	self.tokenizer.batch_decode(generated_ids.numpy()				,				skip_special_tokens=_UpperCamelCase     )[0]
							assert generated_words in (
							    "i don't know. i just feel like i'm going to throw up. it's not fun.",
							    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
							    "i'm not sure. i just feel like i've been in a bad situation.",
							)





from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


_A   =     logging.get_logger(__name__)


_A   =     TypeVar("DatasetType", Dataset, IterableDataset)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[DatasetType]    ,							__lowerCAmelCase  :  Optional[List[float]] = None    ,							__lowerCAmelCase  :  Optional[int] = None    ,							__lowerCAmelCase  :  Optional[DatasetInfo] = None    ,							__lowerCAmelCase  :  Optional[NamedSplit] = None    ,							__lowerCAmelCase  :  Literal["first_exhausted", "all_exhausted"] = "first_exhausted"    ,							):




			"""simple docstring"""


			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("Unable to interleave an empty list of datasets."				)
			for i, dataset in enumerate(__lowerCAmelCase				):
						if not isinstance(__lowerCAmelCase    ,							(Dataset, IterableDataset)				):
									if isinstance(__lowerCAmelCase    ,							(DatasetDict, IterableDatasetDict)				):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."				)
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(__lowerCAmelCase				)}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__lowerCAmelCase				)				)}']"""				)
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase				).__name__}."""				)
						if i == 0:
									lowerCAmelCase_	,  lowerCAmelCase_						=	(
									    (Dataset, IterableDataset) if isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""				)
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""				)
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							stopping_strategy=__lowerCAmelCase				)
			else:
						return _interleave_iterable_datasets(
						    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							stopping_strategy=__lowerCAmelCase				)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[DatasetType]    ,							__lowerCAmelCase  :  Optional[DatasetInfo] = None    ,							__lowerCAmelCase  :  Optional[NamedSplit] = None    ,							__lowerCAmelCase  :  int = 0    ,							):




			"""simple docstring"""


			if not dsets:
						raise ValueError("Unable to concatenate an empty list of datasets."				)
			for i, dataset in enumerate(__lowerCAmelCase				):
						if not isinstance(__lowerCAmelCase    ,							(Dataset, IterableDataset)				):
									if isinstance(__lowerCAmelCase    ,							(DatasetDict, IterableDatasetDict)				):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."				)
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(__lowerCAmelCase				)}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__lowerCAmelCase				)				)}']"""				)
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase				).__name__}."""				)
						if i == 0:
									lowerCAmelCase_	,  lowerCAmelCase_						=	(
									    (Dataset, IterableDataset) if isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""				)
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							axis=__lowerCAmelCase				)
			else:
						return _concatenate_iterable_datasets(__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							axis=__lowerCAmelCase				)



