

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 					_a		:


					'''simple docstring'''






					UpperCAmelCase__:     List[str]													= BlenderbotSmallConfig
					UpperCAmelCase__:     Dict													= {}
					UpperCAmelCase__:     List[str]													= '''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					, ):
						A__							:					List[str]       =	parent
						A__							:					List[Any]       =	batch_size
						A__							:					Optional[Any]       =	seq_length
						A__							:					Dict       =	is_training
						A__							:					int       =	use_labels
						A__							:					str       =	vocab_size
						A__							:					Tuple       =	hidden_size
						A__							:					List[Any]       =	num_hidden_layers
						A__							:					Union[str, Any]       =	num_attention_heads
						A__							:					Optional[int]       =	intermediate_size

						A__							:					List[str]       =	hidden_dropout_prob
						A__							:					List[str]       =	attention_probs_dropout_prob
						A__							:					List[str]       =	max_position_embeddings
						A__							:					int       =	eos_token_id
						A__							:					Tuple       =	pad_token_id
						A__							:					List[Any]       =	bos_token_id






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

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

						A__							:					Optional[Any]       =	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__							:					Any       =	prepare_blenderbot_small_inputs_dict(lowercase__					, lowercase__					, lowercase__    )
						return config, inputs_dict






					def 						__A    (				self					, A__					, A__    ):
						A__							:					int       =	TFBlenderbotSmallModel(config=lowercase__    ).get_decoder()
						A__							:					Union[str, Any]       =	inputs_dict["input_ids"]

						A__							:					List[Any]       =	input_ids[:1, :]
						A__							:					List[str]       =	inputs_dict["attention_mask"][:1, :]
						A__							:					Any       =	inputs_dict["head_mask"]
						A__							:					List[Any]       =	1

						# first forward pass
						A__							:					Optional[int]       =	model(lowercase__					, attention_mask=lowercase__					, head_mask=lowercase__					, use_cache=lowercase__    )

						A__							:					List[str]       =	outputs.to_tuple()

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

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

						A__							:					Optional[Any]       =	model(lowercase__					, attention_mask=lowercase__    )[0]
						A__							:					Any       =	model(lowercase__					, attention_mask=lowercase__					, past_key_values=lowercase__    )[0]

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

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

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



def 		UpperCamelCase      (lowercase_:				List[Any]					,   lowercase_:				Union[str, Any]					,   lowercase_:				Union[str, Any]					,   lowercase_:				Tuple=None					,   lowercase_:				Tuple=None					,   lowercase_:				Union[str, Any]=None					,   lowercase_:				List[Any]=None					,   lowercase_:				int=None					,   )  ->   int:

	if attention_mask is None:
		A__							:					int       =	tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE_					,   config.pad_token_id      )					,   tf.inta      )
	if decoder_attention_mask is None:
		A__							:					Union[str, Any]       =	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__							:					Any       =	tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
	if decoder_head_mask is None:
		A__							:					Optional[int]       =	tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
	if cross_attn_head_mask is None:
		A__							:					Optional[int]       =	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 					_a		(_UpperCAmelCase	,     _UpperCAmelCase	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     Any													= (
					    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
					)
					UpperCAmelCase__:     Dict													= (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
					UpperCAmelCase__:     Optional[int]													= (
					    {
					        '''conversational''': TFBlenderbotSmallForConditionalGeneration,
					        '''feature-extraction''': TFBlenderbotSmallModel,
					        '''summarization''': TFBlenderbotSmallForConditionalGeneration,
					        '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
					        '''translation''': TFBlenderbotSmallForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					UpperCAmelCase__:     List[str]													= True
					UpperCAmelCase__:     Optional[Any]													= False
					UpperCAmelCase__:     Optional[int]													= False






					def 						__A    (				self    ):
						A__							:					str       =	TFBlenderbotSmallModelTester(self    )
						A__							:					Dict       =	ConfigTester(self					, config_class=lowercase__    )






					def 						__A    (				self    ):
						self.config_tester.run_common_tests()






					def 						__A    (				self    ):
						A__							:					int       =	self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*lowercase__    )



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


					'''simple docstring'''






					UpperCAmelCase__:     str													= [
					    '''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?'''
					]
					UpperCAmelCase__:     Tuple													= '''facebook/blenderbot_small-90M'''






					@cached_property
					def 						__A    (				self    ):
						return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""    )






					@cached_property
					def 						__A    (				self    ):
						A__							:					str       =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
						return model






					@slow
					def 						__A    (				self    ):
						A__							:					int       =	self.tokenizer(self.src_text					, return_tensors="""tf"""    )
						A__							:					str       =	self.model.generate(
						    model_inputs.input_ids					, attention_mask=model_inputs.attention_mask					, num_beams=2					, use_cache=lowercase__					, )
						A__							:					str       =	self.tokenizer.batch_decode(generated_ids.numpy()					, skip_special_tokens=lowercase__    )[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.",
						)






import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def 		UpperCamelCase      (*lowercase_:				Optional[int]					,   lowercase_:				Optional[Union[Dict, Any]] = None					,   lowercase_:				Dict=True					,   lowercase_:				Tuple=2      )  ->   Dict:
	from .. import __version__

	A__							:					Dict       =	take_from
	A__							:					str       =	()
	if not isinstance(args[0]					,   lowercase_      ):
		A__							:					int       =	(args,)

	for attribute, version_name, message in args:
		if version.parse(version.parse(lowercase_      ).base_version      ) >= version.parse(lowercase_      ):
			raise ValueError(
			    f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
			    f""" version {__version__} is >= {version_name}"""      )

		A__							:					Any       =	None
		if isinstance(lowercase_					,   lowercase_      ) and attribute in deprecated_kwargs:
			values += (deprecated_kwargs.pop(lowercase_      ),)
			A__							:					List[str]       =	f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
		elif hasattr(lowercase_					,   lowercase_      ):
			values += (getattr(lowercase_					,   lowercase_      ),)
			A__							:					Optional[Any]       =	f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
		elif deprecated_kwargs is None:
			A__							:					int       =	f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

		if warning is not None:
			A__							:					int       =	warning + """ """ if standard_warn else """"""
			warnings.warn(warning + message					,   lowercase_					,   stacklevel=lowercase_      )

	if isinstance(lowercase_					,   lowercase_      ) and len(lowercase_      ) > 0:
		A__							:					Union[str, Any]       =	inspect.getouterframes(inspect.currentframe()      )[1]
		A__							:					Optional[Any]       =	call_frame.filename
		A__							:					Optional[int]       =	call_frame.lineno
		A__							:					Any       =	call_frame.function
		A__     ,     A__							:					List[str]       =	next(iter(deprecated_kwargs.items()      )      )
		raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""      )

	if len(lowercase_      ) == 0:
		return
	elif len(lowercase_      ) == 1:
		return values[0]
	return values



