
'''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 						a   :
				_snake_case		    = BlenderbotSmallConfig
				_snake_case		    = {}
				_snake_case		    = '''gelu'''




				def __init__(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				List[Any]=13,    SCREAMING_SNAKE_CASE_	:				List[str]=7,    SCREAMING_SNAKE_CASE_	:				str=True,    SCREAMING_SNAKE_CASE_	:				Any=False,    SCREAMING_SNAKE_CASE_	:				Optional[int]=99,    SCREAMING_SNAKE_CASE_	:				List[str]=32,    SCREAMING_SNAKE_CASE_	:				Optional[int]=2,    SCREAMING_SNAKE_CASE_	:				str=4,    SCREAMING_SNAKE_CASE_	:				List[Any]=37,    SCREAMING_SNAKE_CASE_	:				int=0.1,    SCREAMING_SNAKE_CASE_	:				int=0.1,    SCREAMING_SNAKE_CASE_	:				List[str]=20,    SCREAMING_SNAKE_CASE_	:				Tuple=2,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=1,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=0,    ):
										snake_case       :     List[Any] =    parent
										snake_case       :     str =    batch_size
										snake_case       :     Optional[int] =    seq_length
										snake_case       :     Optional[int] =    is_training
										snake_case       :     Dict =    use_labels
										snake_case       :     Optional[Any] =    vocab_size
										snake_case       :     int =    hidden_size
										snake_case       :     List[str] =    num_hidden_layers
										snake_case       :     str =    num_attention_heads
										snake_case       :     Union[str, Any] =    intermediate_size

										snake_case       :     Tuple =    hidden_dropout_prob
										snake_case       :     Tuple =    attention_probs_dropout_prob
										snake_case       :     Optional[int] =    max_position_embeddings
										snake_case       :     Dict =    eos_token_id
										snake_case       :     Any =    pad_token_id
										snake_case       :     Optional[Any] =    bos_token_id




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

										snake_case       :     List[Any] =    ids_tensor([self.batch_size, self.seq_length],    self.vocab_size      )

										snake_case       :     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,    )
										snake_case       :     str =    prepare_blenderbot_small_inputs_dict(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										return config, inputs_dict




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										snake_case       :     Any =    TFBlenderbotSmallModel(config=SCREAMING_SNAKE_CASE_      ).get_decoder()
										snake_case       :     Dict =    inputs_dict['''input_ids''']

										snake_case       :     int =    input_ids[:1, :]
										snake_case       :     str =    inputs_dict['''attention_mask'''][:1, :]
										snake_case       :     int =    inputs_dict['''head_mask''']
										snake_case       :     Dict =    1

										# first forward pass
										snake_case       :     Union[str, Any] =    model(SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    head_mask=SCREAMING_SNAKE_CASE_,    use_cache=SCREAMING_SNAKE_CASE_      )

										snake_case,					snake_case       :     Optional[int] =    outputs.to_tuple()

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

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

										snake_case       :     int =    model(SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_      )[0]
										snake_case       :     int =    model(SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    past_key_values=SCREAMING_SNAKE_CASE_      )[0]

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

										# select random slice
										snake_case       :     str =    int(ids_tensor((1,),    output_from_past.shape[-1]      )      )
										snake_case       :     Optional[Any] =    output_from_no_past[:, -3:, random_slice_idx]
										snake_case       :     Any =    output_from_past[:, :, random_slice_idx]

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




def      A (       A_							:					Any    ,							A_							:					int    ,							A_							:					Dict    ,							A_							:					Optional[int]=None    ,							A_							:					List[str]=None    ,							A_							:					int=None    ,							A_							:					Optional[Any]=None    ,							A_							:					Union[str, Any]=None    ,							):
						if attention_mask is None:
												snake_case       :     Optional[Any] =    tf.cast(tf.math.not_equal(A_    ,							config.pad_token_id       )    ,							tf.inta       )
						if decoder_attention_mask is None:
												snake_case       :     int =    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:
												snake_case       :     str =    tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
						if decoder_head_mask is None:
												snake_case       :     Union[str, Any] =    tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
						if cross_attn_head_mask is None:
												snake_case       :     str =    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   (				__magic_name__				,__magic_name__				,unittest.TestCase    ):
				_snake_case		    = (
				    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
				)
				_snake_case		    = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
				_snake_case		    = (
				    {
				        '''conversational''': TFBlenderbotSmallForConditionalGeneration,
				        '''feature-extraction''': TFBlenderbotSmallModel,
				        '''summarization''': TFBlenderbotSmallForConditionalGeneration,
				        '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
				        '''translation''': TFBlenderbotSmallForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				_snake_case		    = True
				_snake_case		    = False
				_snake_case		    = False




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     Union[str, Any] =    TFBlenderbotSmallModelTester(self      )
										snake_case       :     int =    ConfigTester(self,    config_class=SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				int      ):
										self.config_tester.run_common_tests()




				def 	__snake_case							(							self	:				int      ):
										snake_case       :     int =    self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_      )




@require_tokenizers
@require_tf
class 						a   (				unittest.TestCase    ):
				_snake_case		    = [
				    '''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?'''
				]
				_snake_case		    = '''facebook/blenderbot_small-90M'''




				@cached_property
				def 	__snake_case							(							self	:				List[Any]      ):
										# use "old" tokenizer here because of bug when downloading new tokenizer
										return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''      )




				@cached_property
				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     List[str] =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
										return model




				@slow
				def 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     Union[str, Any] =    self.tokenizer(self.src_text,    return_tensors='''tf'''      )
										snake_case       :     int =    self.model.generate(
										    model_inputs.input_ids,    attention_mask=model_inputs.attention_mask,    num_beams=2,    use_cache=SCREAMING_SNAKE_CASE_,    )
										snake_case       :     int =    self.tokenizer.batch_decode(generated_ids.numpy(),    skip_special_tokens=SCREAMING_SNAKE_CASE_      )[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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase    					=   {
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						UpperCAmelCase    					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

