
"""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    _UpperCAmelCase:
		lowercase__      =				BlenderbotConfig
		lowercase__      =				{}
		lowercase__      =				'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    ,		)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  parent
							_UpperCamelCase										=  batch_size
							_UpperCamelCase										=  seq_length
							_UpperCamelCase										=  is_training
							_UpperCamelCase										=  use_labels
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  intermediate_size

							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  eos_token_id
							_UpperCamelCase										=  pad_token_id
							_UpperCamelCase										=  bos_token_id



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length - 1]    ,		self.vocab_size)
							_UpperCamelCase										=  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)    ,		1)
							_UpperCamelCase										=  tf.concat([input_ids, eos_tensor]    ,		axis=1)

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

							_UpperCamelCase										=  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										=  prepare_blenderbot_inputs_dict(__a    ,		__a    ,		__a)
							return config, inputs_dict




		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  TFBlenderbotModel(config=__a).get_decoder()
							_UpperCamelCase										=  inputs_dict['''input_ids''']

							_UpperCamelCase										=  input_ids[:1, :]
							_UpperCamelCase										=  inputs_dict['''attention_mask'''][:1, :]
							_UpperCamelCase										=  inputs_dict['''head_mask''']
							_UpperCamelCase										=  1

							# first forward pass
							_UpperCamelCase										=  model(__a    ,		attention_mask=__a    ,		head_mask=__a    ,		use_cache=__a)

							_UpperCamelCase			,  _UpperCamelCase										=  outputs.to_tuple()

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

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

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

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





def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case=None,					__snake_case=None,					__snake_case=None,					__snake_case=None,					__snake_case=None,					)							->		Optional[Any]:


					"""simple docstring"""





					if attention_mask is None:
										_UpperCamelCase										=  tf.cast(tf.math.not_equal(__snake_case,					config.pad_token_id     ),					tf.inta     )
					if decoder_attention_mask is None:
										_UpperCamelCase										=  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										=  tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
					if decoder_head_mask is None:
										_UpperCamelCase										=  tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
					if cross_attn_head_mask is None:
										_UpperCamelCase										=  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(   lowerCamelCase				,					lowerCamelCase				,					unittest.TestCase     ):
		lowercase__      =				(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
		lowercase__      =				(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
		lowercase__      =				(
		    {
		        'conversational': TFBlenderbotForConditionalGeneration,
		        'feature-extraction': TFBlenderbotModel,
		        'summarization': TFBlenderbotForConditionalGeneration,
		        'text2text-generation': TFBlenderbotForConditionalGeneration,
		        'translation': TFBlenderbotForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		lowercase__      =				True
		lowercase__      =				False
		lowercase__      =				False



		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  TFBlenderbotModelTester(self)
							_UpperCamelCase										=  ConfigTester(self    ,		config_class=__a)



		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							self.config_tester.run_common_tests()




		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  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     ):
		lowercase__      =				['My friends are cool but they eat too many carbs.']
		lowercase__      =				'facebook/blenderbot-400M-distill'



		@cached_property
		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							return BlenderbotTokenizer.from_pretrained(self.model_name)



		@cached_property
		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
							return model




		@slow
		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.tokenizer(self.src_text    ,		return_tensors='''tf''')
							_UpperCamelCase										=  self.model.generate(
							    model_inputs.input_ids    ,		)
							_UpperCamelCase										=  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"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a														=		logging.get_logger(__name__)

_a														=		{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'bert'



		def __init__( self    ,		__a=3_05_22    ,		__a=7_68    ,		__a=12    ,		__a=12    ,		__a=30_72    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_12    ,		__a=2    ,		__a=0.02    ,		__a=1e-12    ,		__a=0    ,		__a="absolute"    ,		__a=True    ,		__a=None    ,		**__a    ,		)	->	Optional[Any]:






							'''simple docstring'''
							super().__init__(pad_token_id=__a    ,		**__a)

							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  type_vocab_size
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  position_embedding_type
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  classifier_dropout






class    _UpperCAmelCase(   lowerCamelCase     ):



		@property
		def 						UpperCAmelCase							( self)	->	Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							if self.task == "multiple-choice":
												_UpperCamelCase										=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												_UpperCamelCase										=  {0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							        ('''token_type_ids''', dynamic_axis),
							    ])






