

"""simple docstring"""




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						:
			lowerCamelCase__   :		Tuple							=							BlenderbotSmallConfig
			lowerCamelCase__   :		Dict							=							{}
			lowerCamelCase__   :		str							=							'gelu'

			def __init__(					self				:						Dict    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Dict=1_3    ,				__UpperCAmelCase				:						Tuple=7    ,				__UpperCAmelCase				:						str=True    ,				__UpperCAmelCase				:						Any=False    ,				__UpperCAmelCase				:						int=9_9    ,				__UpperCAmelCase				:						List[Any]=3_2    ,				__UpperCAmelCase				:						Tuple=2    ,				__UpperCAmelCase				:						Tuple=4    ,				__UpperCAmelCase				:						Optional[Any]=3_7    ,				__UpperCAmelCase				:						Any=0.1    ,				__UpperCAmelCase				:						Tuple=0.1    ,				__UpperCAmelCase				:						List[str]=2_0    ,				__UpperCAmelCase				:						str=2    ,				__UpperCAmelCase				:						Tuple=1    ,				__UpperCAmelCase				:						int=0    ,				)   ->    Any:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      seq_length
						SCREAMING_SNAKE_CASE__								=      is_training
						SCREAMING_SNAKE_CASE__								=      use_labels
						SCREAMING_SNAKE_CASE__								=      vocab_size
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      num_hidden_layers
						SCREAMING_SNAKE_CASE__								=      num_attention_heads
						SCREAMING_SNAKE_CASE__								=      intermediate_size

						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      max_position_embeddings
						SCREAMING_SNAKE_CASE__								=      eos_token_id
						SCREAMING_SNAKE_CASE__								=      pad_token_id
						SCREAMING_SNAKE_CASE__								=      bos_token_id

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

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

						SCREAMING_SNAKE_CASE__								=      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    ,				)
						SCREAMING_SNAKE_CASE__								=      prepare_blenderbot_small_inputs_dict(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )
						return config, inputs_dict





			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[int]       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      TFBlenderbotSmallModel(config=__UpperCAmelCase       ).get_decoder()
						SCREAMING_SNAKE_CASE__								=      inputs_dict["""input_ids"""]

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

						# first forward pass
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase    ,				head_mask=__UpperCAmelCase    ,				use_cache=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      outputs.to_tuple()

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

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

						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase       )[0]
						SCREAMING_SNAKE_CASE__								=      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
						SCREAMING_SNAKE_CASE__								=      int(ids_tensor((1,)    ,				output_from_past.shape[-1]       )       )
						SCREAMING_SNAKE_CASE__								=      output_from_no_past[:, -3:, random_slice_idx]
						SCREAMING_SNAKE_CASE__								=      output_from_past[:, :, random_slice_idx]

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

def 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=None		,				snake_case__=None		,				snake_case__=None		,				):




			'''simple docstring'''


			if attention_mask is None:
						SCREAMING_SNAKE_CASE__								=      tf.cast(tf.math.not_equal(snake_case__		,				config.pad_token_id    )		,				tf.inta    )
			if decoder_attention_mask is None:
						SCREAMING_SNAKE_CASE__								=      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:
						SCREAMING_SNAKE_CASE__								=      tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
			if decoder_head_mask is None:
						SCREAMING_SNAKE_CASE__								=      tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
			if cross_attn_head_mask is None:
						SCREAMING_SNAKE_CASE__								=      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			):
			lowerCamelCase__   :		Tuple							=							(
			    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
			)
			lowerCamelCase__   :		List[str]							=							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
			lowerCamelCase__   :		str							=							(
			    {
			        'conversational': TFBlenderbotSmallForConditionalGeneration,
			        'feature-extraction': TFBlenderbotSmallModel,
			        'summarization': TFBlenderbotSmallForConditionalGeneration,
			        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
			        'translation': TFBlenderbotSmallForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			lowerCamelCase__   :		List[str]							=							True
			lowerCamelCase__   :		str							=							False
			lowerCamelCase__   :		Any							=							False

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      TFBlenderbotSmallModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      ConfigTester(self    ,				config_class=__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    List[str]:
						self.config_tester.run_common_tests()





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      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			):
			lowerCamelCase__   :		str							=							[
			    '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__   :		Any							=							'facebook/blenderbot_small-90M'

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

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
						return model





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.tokenizer(self.src_text    ,				return_tensors="""tf"""       )
						SCREAMING_SNAKE_CASE__								=      self.model.generate(
						    model_inputs.input_ids    ,				attention_mask=model_inputs.attention_mask    ,				num_beams=2    ,				use_cache=__UpperCAmelCase    ,				)
						SCREAMING_SNAKE_CASE__								=      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.",
						)





"""simple docstring"""




from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments


