


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      lowercase			:

		"""simple docstring"""

		_a									=       BlenderbotConfig
		_a									=       {}
		_a									=       '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					,     ):

							'''simple docstring'''


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

							UpperCamelCase__     :Union[str, Any]							      =				hidden_dropout_prob
							UpperCamelCase__     :Optional[Any]							      =				attention_probs_dropout_prob
							UpperCamelCase__     :Dict							      =				max_position_embeddings
							UpperCamelCase__     :Union[str, Any]							      =				eos_token_id
							UpperCamelCase__     :int							      =				pad_token_id
							UpperCamelCase__     :str							      =				bos_token_id




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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

							UpperCamelCase__     :int							      =				ids_tensor([self.batch_size, self.seq_length]					,     self.vocab_size							)

							UpperCamelCase__     :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					,     )
							UpperCamelCase__     :str							      =				prepare_blenderbot_inputs_dict(__a					,     __a					,     __a							)
							return config, inputs_dict




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				TFBlenderbotModel(config=__a							).get_decoder()
							UpperCamelCase__     :Union[str, Any]							      =				inputs_dict['''input_ids''']

							UpperCamelCase__     :str							      =				input_ids[:1, :]
							UpperCamelCase__     :List[str]							      =				inputs_dict['''attention_mask'''][:1, :]
							UpperCamelCase__     :Dict							      =				inputs_dict['''head_mask''']
							UpperCamelCase__     :Optional[Any]							      =				1

							# first forward pass
							UpperCamelCase__     :int							      =				model(__a					,     attention_mask=__a					,     head_mask=__a					,     use_cache=__a							)

							UpperCamelCase__      ,       UpperCamelCase__     :str							      =				outputs.to_tuple()

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

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

							UpperCamelCase__     :List[Any]							      =				model(__a					,     attention_mask=__a							)[0]
							UpperCamelCase__     :str							      =				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
							UpperCamelCase__     :Tuple							      =				int(ids_tensor((1,)					,     output_from_past.shape[-1]							)							)
							UpperCamelCase__     :Union[str, Any]							      =				output_from_no_past[:, -3:, random_slice_idx]
							UpperCamelCase__     :Tuple							      =				output_from_past[:, :, random_slice_idx]

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



def     a   (    __a				,      __a				,      __a				,      __a=None				,      __a=None				,      __a=None				,      __a=None				,      __a=None				,      )						->			Union[str, Any]:



					'''simple docstring'''






					if attention_mask is None:
										UpperCamelCase__     :str							      =				tf.cast(tf.math.not_equal(__snake_case				,      config.pad_token_id     )				,      tf.inta     )
					if decoder_attention_mask is None:
										UpperCamelCase__     :List[str]							      =				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:
										UpperCamelCase__     :int							      =				tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
					if decoder_head_mask is None:
										UpperCamelCase__     :str							      =				tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
					if cross_attn_head_mask is None:
										UpperCamelCase__     :Union[str, Any]							      =				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      lowercase			(							A__			,	A__			,	unittest.TestCase      ):

		"""simple docstring"""

		_a									=       (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
		_a									=       (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
		_a									=       (
		    {
		        'conversational': TFBlenderbotForConditionalGeneration,
		        'feature-extraction': TFBlenderbotModel,
		        'summarization': TFBlenderbotForConditionalGeneration,
		        'text2text-generation': TFBlenderbotForConditionalGeneration,
		        'translation': TFBlenderbotForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		_a									=       True
		_a									=       False
		_a									=       False




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				TFBlenderbotModelTester(self							)
							UpperCamelCase__     :List[Any]							      =				ConfigTester(self					,     config_class=__a							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.config_tester.run_common_tests()




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*__a							)



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

		"""simple docstring"""

		_a									=       ['My friends are cool but they eat too many carbs.']
		_a									=       'facebook/blenderbot-400M-distill'




		@cached_property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return BlenderbotTokenizer.from_pretrained(self.model_name							)




		@cached_property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
							return model




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.tokenizer(self.src_text					,     return_tensors='''tf'''							)
							UpperCamelCase__     :str							      =				self.model.generate(
							    model_inputs.input_ids					,     )
							UpperCamelCase__     :int							      =				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 tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch
@require_torch
@require_optimum
@slow
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :int							      =				AutoTokenizer.from_pretrained(UpperCamelCase_							)
							UpperCamelCase__     :int							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :int							      =				tokenizer('''This is me'''					,     return_tensors='''pt'''							)

							UpperCamelCase__     :Optional[int]							      =				model.to_bettertransformer()

							self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							UpperCamelCase__     :int							      =				model.generate(**UpperCamelCase_							)

							UpperCamelCase__     :Optional[Any]							      =				model.reverse_bettertransformer()

							self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

												self.assertFalse(
												    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()							)							)

												UpperCamelCase__     :Tuple							      =				model_reloaded.generate(**UpperCamelCase_							)
												self.assertTrue(torch.allclose(UpperCamelCase_					,     UpperCamelCase_							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :Dict							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
												with self.assertRaises(UpperCamelCase_							):
																	model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				model.reverse_bettertransformer()
												model.save_pretrained(UpperCamelCase_							)