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 UpperCAmelCase						:


				'''simple docstring'''






				lowerCamelCase_    =					BlenderbotSmallConfig
				lowerCamelCase_    =					{}
				lowerCamelCase_    =					'''gelu'''
				def __init__(						self		,	lowercase		,	lowercase=1_3		,	lowercase=7		,	lowercase=True		,	lowercase=False		,	lowercase=9_9		,	lowercase=3_2		,	lowercase=2		,	lowercase=4		,	lowercase=3_7		,	lowercase=0.1		,	lowercase=0.1		,	lowercase=2_0		,	lowercase=2		,	lowercase=1		,	lowercase=0		,	):



						"""simple docstring"""





						A_		:				Any     =      parent
						A_		:				Dict     =      batch_size
						A_		:				Tuple     =      seq_length
						A_		:				str     =      is_training
						A_		:				str     =      use_labels
						A_		:				int     =      vocab_size
						A_		:				Optional[Any]     =      hidden_size
						A_		:				Dict     =      num_hidden_layers
						A_		:				Optional[Any]     =      num_attention_heads
						A_		:				str     =      intermediate_size

						A_		:				Optional[int]     =      hidden_dropout_prob
						A_		:				str     =      attention_probs_dropout_prob
						A_		:				List[str]     =      max_position_embeddings
						A_		:				Optional[int]     =      eos_token_id
						A_		:				Optional[Any]     =      pad_token_id
						A_		:				List[str]     =      bos_token_id
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				Dict     =      ids_tensor([self.batch_size, self.seq_length - 1]		,	self.vocab_size					)
						A_		:				List[Any]     =      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)		,	1					)
						A_		:				List[Any]     =      tf.concat([input_ids, eos_tensor]		,	axis=1					)

						A_		:				Any     =      ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size					)

						A_		:				Tuple     =      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		,	)
						A_		:				Union[str, Any]     =      prepare_blenderbot_small_inputs_dict(_UpperCamelCase		,	_UpperCamelCase		,	_UpperCamelCase					)
						return config, inputs_dict







				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase					):



						"""simple docstring"""





						A_		:				Tuple     =      TFBlenderbotSmallModel(config=_UpperCamelCase					).get_decoder()
						A_		:				str     =      inputs_dict['input_ids']

						A_		:				int     =      input_ids[:1, :]
						A_		:				Any     =      inputs_dict['attention_mask'][:1, :]
						A_		:				int     =      inputs_dict['head_mask']
						A_		:				int     =      1

						# first forward pass
						A_		:				Optional[int]     =      model(_UpperCamelCase		,	attention_mask=_UpperCamelCase		,	head_mask=_UpperCamelCase		,	use_cache=_UpperCamelCase					)

						A_   ,			A_		:				Optional[int]     =      outputs.to_tuple()

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

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

						A_		:				str     =      model(_UpperCamelCase		,	attention_mask=_UpperCamelCase					)[0]
						A_		:				List[Any]     =      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
						A_		:				int     =      int(ids_tensor((1,)		,	output_from_past.shape[-1]					)					)
						A_		:				Any     =      output_from_no_past[:, -3:, random_slice_idx]
						A_		:				List[Any]     =      output_from_past[:, :, random_slice_idx]

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




def 				UpperCamelCase						(       __lowercase						: Dict						,__lowercase						: Tuple						,__lowercase						: List[Any]						,__lowercase						: Optional[int]=None						,__lowercase						: str=None						,__lowercase						: Any=None						,__lowercase						: List[Any]=None						,__lowercase						: Any=None						,):
		'''simple docstring'''




		if attention_mask is None:
				A_		:				Optional[int]     =      tf.cast(tf.math.not_equal(__lowerCAmelCase						,config.pad_token_id	)						,tf.inta	)
		if decoder_attention_mask is None:
				A_		:				int     =      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:
				A_		:				int     =      tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
		if decoder_head_mask is None:
				A_		:				Union[str, Any]     =      tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
		if cross_attn_head_mask is None:
				A_		:				Tuple     =      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 UpperCAmelCase						(					__a					,       __a					,       unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    =					(
				    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
				)
				lowerCamelCase_    =					(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
				lowerCamelCase_    =					(
				    {
				        '''conversational''': TFBlenderbotSmallForConditionalGeneration,
				        '''feature-extraction''': TFBlenderbotSmallModel,
				        '''summarization''': TFBlenderbotSmallForConditionalGeneration,
				        '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
				        '''translation''': TFBlenderbotSmallForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				lowerCamelCase_    =					True
				lowerCamelCase_    =					False
				lowerCamelCase_    =					False
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				Any     =      TFBlenderbotSmallModelTester(self					)
						A_		:				int     =      ConfigTester(self		,	config_class=_UpperCamelCase					)
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						self.config_tester.run_common_tests()







				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				List[Any]     =      self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase					)





@require_tokenizers
@require_tf
class UpperCAmelCase						(					unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    =					[
				    '''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?'''
				]
				lowerCamelCase_    =					'''facebook/blenderbot_small-90M'''
				@cached_property
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'					)
				@cached_property
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				str     =      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
						return model







				@slow
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				Tuple     =      self.tokenizer(self.src_text		,	return_tensors='tf'					)
						A_		:				Optional[Any]     =      self.model.generate(
						    model_inputs.input_ids		,	attention_mask=model_inputs.attention_mask		,	num_beams=2		,	use_cache=_UpperCamelCase		,	)
						A_		:				Dict     =      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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A   =     {}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["NllbTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["NllbTokenizerFast"]


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb import NllbTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb_fast import NllbTokenizerFast

else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



