

"""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__ :		str       		=				BlenderbotSmallConfig
			lowerCamelCase__ :		Dict       		=				{}
			lowerCamelCase__ :		int       		=				'gelu'

			def __init__(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    List[Any]=1_3     ,					__UpperCAmelCase					:    Union[str, Any]=7     ,					__UpperCAmelCase					:    Optional[int]=True     ,					__UpperCAmelCase					:    int=False     ,					__UpperCAmelCase					:    int=9_9     ,					__UpperCAmelCase					:    Optional[Any]=3_2     ,					__UpperCAmelCase					:    Tuple=2     ,					__UpperCAmelCase					:    List[Any]=4     ,					__UpperCAmelCase					:    str=3_7     ,					__UpperCAmelCase					:    Tuple=0.1     ,					__UpperCAmelCase					:    Union[str, Any]=0.1     ,					__UpperCAmelCase					:    str=2_0     ,					__UpperCAmelCase					:    str=2     ,					__UpperCAmelCase					:    Any=1     ,					__UpperCAmelCase					:    Union[str, Any]=0     ,					) ->     int:
						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					:    Union[str, Any]							) ->     Optional[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					:    List[str]     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    List[Any]							) ->     int:
						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[Any]       		=				(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
			lowerCamelCase__ :		Union[str, Any]       		=				(
			    {
			        'conversational': TFBlenderbotSmallForConditionalGeneration,
			        'feature-extraction': TFBlenderbotSmallModel,
			        'summarization': TFBlenderbotSmallForConditionalGeneration,
			        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
			        'translation': TFBlenderbotSmallForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			lowerCamelCase__ :		int       		=				True
			lowerCamelCase__ :		Tuple       		=				False
			lowerCamelCase__ :		Optional[int]       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   TFBlenderbotSmallModelTester(self							)
						SCREAMING_SNAKE_CASE__										=   ConfigTester(self     ,					config_class=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Optional[int]:
						self.config_tester.run_common_tests()





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

			@cached_property
			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     List[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					:    Optional[int]							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
						return model





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[str]:
						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 __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


A_				:      Union[str, Any]           =			{
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		int       		=				'ernie_m'
			lowerCamelCase__ :		Dict[str, str]       		=				{"dropout": "classifier_dropout", "num_classes": "num_labels"}

			def __init__(       self					:    List[str]     ,					__UpperCAmelCase					:    int = 2_5_0_0_0_2     ,					__UpperCAmelCase					:    int = 7_6_8     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 3_0_7_2     ,					__UpperCAmelCase					:    str = "gelu"     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    int = 5_1_4     ,					__UpperCAmelCase					:    float = 0.02     ,					__UpperCAmelCase					:    int = 1     ,					__UpperCAmelCase					:    float = 1e-05     ,					__UpperCAmelCase					:    Union[str, Any]=None     ,					__UpperCAmelCase					:    List[Any]=False     ,					__UpperCAmelCase					:    Union[str, Any]=0.0     ,					**__UpperCAmelCase					:    Any     ,					) ->     Optional[Any]:
						super().__init__(pad_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)
						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_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   classifier_dropout
						SCREAMING_SNAKE_CASE__										=   is_decoder
						SCREAMING_SNAKE_CASE__										=   act_dropout


