


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    _UpperCamelCase     :

				"""simple docstring"""







				lowerCAmelCase					=			BlenderbotConfig
				lowerCAmelCase					=			{}
				lowerCAmelCase					=			'gelu'

				def __init__(    self					, a__					, a__=13					, a__=7					, a__=True					, a__=False					, a__=99					, a__=32					, a__=2					, a__=4					, a__=37					, a__=0.1					, a__=0.1					, a__=20					, a__=2					, a__=1					, a__=0					, )						->					int:
					A             =					parent
					A             =					batch_size
					A             =					seq_length
					A             =					is_training
					A             =					use_labels
					A             =					vocab_size
					A             =					hidden_size
					A             =					num_hidden_layers
					A             =					num_attention_heads
					A             =					intermediate_size

					A             =					hidden_dropout_prob
					A             =					attention_probs_dropout_prob
					A             =					max_position_embeddings
					A             =					eos_token_id
					A             =					pad_token_id
					A             =					bos_token_id

				def   _UpperCAmelCase       (    self )						->					int:
					A             =					ids_tensor([self.batch_size, self.seq_length - 1]					, self.vocab_size )
					A             =					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )					, 1 )
					A             =					tf.concat([input_ids, eos_tensor]					, axis=1 )

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

					A             =					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             =					prepare_blenderbot_inputs_dict(a__					, a__					, a__ )
					return config, inputs_dict

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					Any:
					A             =					TFBlenderbotModel(config=a__ ).get_decoder()
					A             =					inputs_dict["""input_ids"""]

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

					# first forward pass
					A             =					model(a__					, attention_mask=a__					, head_mask=a__					, use_cache=a__ )

					A       ,      A             =					outputs.to_tuple()

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

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

					A             =					model(a__					, attention_mask=a__ )[0]
					A             =					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
					A             =					int(ids_tensor((1,)					, output_from_past.shape[-1] ) )
					A             =					output_from_no_past[:, -3:, random_slice_idx]
					A             =					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__:  Optional[int]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  Dict=None				,       UpperCamelCase__:  Optional[Any]=None				,       UpperCamelCase__:  Union[str, Any]=None				,       UpperCamelCase__:  Optional[Any]=None				,       UpperCamelCase__:  Optional[Any]=None				,       )			-> Optional[int]:





	"""simple docstring"""







	if attention_mask is None:
		A             =					tf.cast(tf.math.not_equal(UpperCamelCase__				,       config.pad_token_id    )				,       tf.inta    )
	if decoder_attention_mask is None:
		A             =					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             =					tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
	if decoder_head_mask is None:
		A             =					tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
	if cross_attn_head_mask is None:
		A             =					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     (       __snake_case     ,		__snake_case     ,		unittest.TestCase					):

				"""simple docstring"""







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

				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					TFBlenderbotModelTester(self )
					A             =					ConfigTester(self					, config_class=a__ )

				def   _UpperCAmelCase       (    self )						->					Dict:
					self.config_tester.run_common_tests()

				def   _UpperCAmelCase       (    self )						->					str:
					A             =					self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*a__ )






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

				"""simple docstring"""







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

				@cached_property
				def   _UpperCAmelCase       (    self )						->					List[Any]:
					return BlenderbotTokenizer.from_pretrained(self.model_name )

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

				@slow
				def   _UpperCAmelCase       (    self )						->					List[Any]:
					A             =					self.tokenizer(self.src_text					, return_tensors="""tf""" )
					A             =					self.model.generate(
					    model_inputs.input_ids					, )
					A             =					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?"
					)







import numpy as np

from transformers import Pipeline





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]    )			-> Optional[int]:





	"""simple docstring"""







	A             =					np.max(UpperCamelCase__				,       axis=-1				,       keepdims=UpperCamelCase__    )
	A             =					np.exp(outputs - maxes    )
	return shifted_exp / shifted_exp.sum(axis=-1				,       keepdims=UpperCamelCase__    )






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self					, **a__ )						->					Union[str, Any]:
					A             =					{}
					if "second_text" in kwargs:
						A             =					kwargs["""second_text"""]
					return preprocess_kwargs, {}, {}

				def   _UpperCAmelCase       (    self					, a__					, a__=None )						->					str:
					return self.tokenizer(a__					, text_pair=a__					, return_tensors=self.framework )

				def   _UpperCAmelCase       (    self					, a__ )						->					Tuple:
					return self.model(**a__ )

				def   _UpperCAmelCase       (    self					, a__ )						->					Optional[int]:
					A             =					model_outputs.logits[0].numpy()
					A             =					softmax(a__ )

					A             =					np.argmax(a__ )
					A             =					self.model.config.idalabel[best_class]
					A             =					probabilities[best_class].item()
					A             =					logits.tolist()
					return {"label": label, "score": score, "logits": logits}



