






"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel


@require_tf
class 		__lowerCamelCase				:





		'''simple docstring'''






		a_	:				Optional[Any]            =							BlenderbotConfig
		a_	:				Tuple            =							{}
		a_	:				List[Any]            =							"""gelu"""





		def __init__(   self							:	int						,			a_							:	List[str]						,			a_							:	Tuple=13						,			a_							:	int=7						,			a_							:	List[str]=True						,			a_							:	Tuple=False						,			a_							:	str=99						,			a_							:	Optional[int]=32						,			a_							:	Optional[int]=2						,			a_							:	Union[str, Any]=4						,			a_							:	Tuple=37						,			a_							:	Optional[int]=0.1						,			a_							:	Any=0.1						,			a_							:	Tuple=20						,			a_							:	Tuple=2						,			a_							:	Tuple=1						,			a_							:	Union[str, Any]=0						,			):
				lowerCAmelCase_   :							List[str]									=			parent
				lowerCAmelCase_   :							int									=			batch_size
				lowerCAmelCase_   :							Optional[int]									=			seq_length
				lowerCAmelCase_   :							Optional[Any]									=			is_training
				lowerCAmelCase_   :							Optional[Any]									=			use_labels
				lowerCAmelCase_   :							List[Any]									=			vocab_size
				lowerCAmelCase_   :							Dict									=			hidden_size
				lowerCAmelCase_   :							str									=			num_hidden_layers
				lowerCAmelCase_   :							Union[str, Any]									=			num_attention_heads
				lowerCAmelCase_   :							Optional[int]									=			intermediate_size

				lowerCAmelCase_   :							str									=			hidden_dropout_prob
				lowerCAmelCase_   :							str									=			attention_probs_dropout_prob
				lowerCAmelCase_   :							Any									=			max_position_embeddings
				lowerCAmelCase_   :							Dict									=			eos_token_id
				lowerCAmelCase_   :							List[str]									=			pad_token_id
				lowerCAmelCase_   :							Tuple									=			bos_token_id





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

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

				lowerCAmelCase_   :							Optional[int]									=			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_   :							Dict									=			prepare_blenderbot_inputs_dict(a_						,			a_						,			a_	)
				return config, inputs_dict





		def    lowerCamelCase       (   self							:	Dict						,			a_							:	Optional[Any]						,			a_							:	List[str]	):
				lowerCAmelCase_   :							Tuple									=			TFBlenderbotModel(config=a_	).get_decoder()
				lowerCAmelCase_   :							Tuple									=			inputs_dict["input_ids"]

				lowerCAmelCase_   :							int									=			input_ids[:1, :]
				lowerCAmelCase_   :							Dict									=			inputs_dict["attention_mask"][:1, :]
				lowerCAmelCase_   :							List[Any]									=			inputs_dict["head_mask"]
				lowerCAmelCase_   :							Union[str, Any]									=			1

				# first forward pass
				lowerCAmelCase_   :							Tuple									=			model(a_						,			attention_mask=a_						,			head_mask=a_						,			use_cache=a_	)

				lowerCAmelCase_				,	lowerCAmelCase_   :							Optional[Any]									=			outputs.to_tuple()

				# create hypothetical next token and extent to next_input_ids
				lowerCAmelCase_   :							Union[str, Any]									=			ids_tensor((self.batch_size, 3)						,			config.vocab_size	)
				lowerCAmelCase_   :							List[str]									=			tf.cast(ids_tensor((self.batch_size, 3)						,			2	)						,			tf.inta	)

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

				lowerCAmelCase_   :							Dict									=			model(a_						,			attention_mask=a_	)[0]
				lowerCAmelCase_   :							Tuple									=			model(a_						,			attention_mask=a_						,			past_key_values=a_	)[0]

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

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

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







def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase=None      ,       __UpperCamelCase=None      ,       __UpperCamelCase=None      ,       __UpperCamelCase=None      ,       __UpperCamelCase=None      ,       )						->    List[str]:

		"""simple docstring"""







		if attention_mask is None:
				lowerCAmelCase_   :							Optional[int]									=			tf.cast(tf.math.not_equal(__UpperCamelCase      ,       config.pad_token_id       )      ,       tf.inta       )
		if decoder_attention_mask is None:
				lowerCAmelCase_   :							Dict									=			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_   :							Union[str, Any]									=			tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
		if decoder_head_mask is None:
				lowerCAmelCase_   :							List[Any]									=			tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
		if cross_attn_head_mask is None:
				lowerCAmelCase_   :							List[str]									=			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			):





		'''simple docstring'''






		a_	:				Optional[int]            =							(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
		a_	:				List[Any]            =							(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
		a_	:				Tuple            =							(
		    {
		        """conversational""": TFBlenderbotForConditionalGeneration,
		        """feature-extraction""": TFBlenderbotModel,
		        """summarization""": TFBlenderbotForConditionalGeneration,
		        """text2text-generation""": TFBlenderbotForConditionalGeneration,
		        """translation""": TFBlenderbotForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		a_	:				List[Any]            =							True
		a_	:				List[Any]            =							False
		a_	:				str            =							False





		def    lowerCamelCase       (   self							:	List[Any]	):
				lowerCAmelCase_   :							List[str]									=			TFBlenderbotModelTester(self	)
				lowerCAmelCase_   :							Tuple									=			ConfigTester(self						,			config_class=a_	)





		def    lowerCamelCase       (   self							:	Tuple	):
				self.config_tester.run_common_tests()





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				lowerCAmelCase_   :							Dict									=			self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*a_	)







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





		'''simple docstring'''






		a_	:				Any            =							["""My friends are cool but they eat too many carbs."""]
		a_	:				Optional[int]            =							"""facebook/blenderbot-400M-distill"""





		@cached_property
		def    lowerCamelCase       (   self							:	Dict	):
				return BlenderbotTokenizer.from_pretrained(self.model_name	)





		@cached_property
		def    lowerCamelCase       (   self							:	Optional[Any]	):
				lowerCAmelCase_   :							str									=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
				return model





		@slow
		def    lowerCamelCase       (   self							:	List[str]	):
				lowerCAmelCase_   :							int									=			self.tokenizer(self.src_text						,			return_tensors="tf"	)
				lowerCAmelCase_   :							Dict									=			self.model.generate(
				    model_inputs.input_ids						,			)
				lowerCAmelCase_   :							Optional[Any]									=			self.tokenizer.batch_decode(generated_ids.numpy()						,			skip_special_tokens=a_	)[0]
				assert (
				    generated_words
				    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
				)








"""simple docstring"""

import inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu


class 		__lowerCamelCase				( unittest.TestCase			):





		'''simple docstring'''






		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							int									=			inspect.getfile(accelerate.test_utils	)
				lowerCAmelCase_   :							List[str]									=			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ["scripts", "test_script.py"]	)
				lowerCAmelCase_   :							Dict									=			os.path.sep.join(inspect.getfile(self.__class__	).split(os.path.sep	)[:-1]	)





		@require_tpu
		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							List[Any]									=			f'''
            {self.test_dir}/xla_spawn.py
            --num_cores 8
            {self.test_file_path}
        '''.split()
				lowerCAmelCase_   :							Dict									=			[sys.executable] + distributed_args
				execute_subprocess_async(a_						,			env=os.environ.copy()	)
