


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__	:
							lowerCamelCase__:     List[Any]							    =     BlenderbotSmallConfig
							lowerCamelCase__:     Optional[int]							    =     {}
							lowerCamelCase__:     List[Any]							    =     """gelu"""

							def __init__(   self      :     Any      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     Union[str, Any]=1_3      ,							lowerCamelCase_      :     Union[str, Any]=7      ,							lowerCamelCase_      :     Union[str, Any]=True      ,							lowerCamelCase_      :     Dict=False      ,							lowerCamelCase_      :     Dict=9_9      ,							lowerCamelCase_      :     int=3_2      ,							lowerCamelCase_      :     Optional[int]=2      ,							lowerCamelCase_      :     Optional[int]=4      ,							lowerCamelCase_      :     str=3_7      ,							lowerCamelCase_      :     List[str]=0.1      ,							lowerCamelCase_      :     Tuple=0.1      ,							lowerCamelCase_      :     Optional[Any]=2_0      ,							lowerCamelCase_      :     Optional[Any]=2      ,							lowerCamelCase_      :     Union[str, Any]=1      ,							lowerCamelCase_      :     Optional[Any]=0      ,							):
												a_    :     Any										=    parent
												a_    :     Union[str, Any]										=    batch_size
												a_    :     Tuple										=    seq_length
												a_    :     List[Any]										=    is_training
												a_    :     Any										=    use_labels
												a_    :     Tuple										=    vocab_size
												a_    :     Optional[int]										=    hidden_size
												a_    :     Tuple										=    num_hidden_layers
												a_    :     str										=    num_attention_heads
												a_    :     str										=    intermediate_size

												a_    :     Union[str, Any]										=    hidden_dropout_prob
												a_    :     Tuple										=    attention_probs_dropout_prob
												a_    :     List[str]										=    max_position_embeddings
												a_    :     Optional[int]										=    eos_token_id
												a_    :     Optional[Any]										=    pad_token_id
												a_    :     Optional[int]										=    bos_token_id

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

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

												a_    :     str										=    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_    :     int										=    prepare_blenderbot_small_inputs_dict(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )
												return config, inputs_dict

							def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     Any    ):
												a_    :     Optional[Any]										=    TFBlenderbotSmallModel(config=lowerCamelCase_    ).get_decoder()
												a_    :     Optional[Any]										=    inputs_dict["""input_ids"""]

												a_    :     Any										=    input_ids[:1, :]
												a_    :     Optional[Any]										=    inputs_dict["""attention_mask"""][:1, :]
												a_    :     Any										=    inputs_dict["""head_mask"""]
												a_    :     Optional[Any]										=    1

												# first forward pass
												a_    :     Dict										=    model(lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							head_mask=lowerCamelCase_      ,							use_cache=lowerCamelCase_    )

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

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

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

												a_    :     List[Any]										=    model(lowerCamelCase_      ,							attention_mask=lowerCamelCase_    )[0]
												a_    :     Any										=    model(lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							past_key_values=lowerCamelCase_    )[0]

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

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

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



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase=None  ,  __UpperCamelCase=None  ,  __UpperCamelCase=None  ,  __UpperCamelCase=None  ,  __UpperCamelCase=None  ,  ):
					if attention_mask is None:
										a_    :     Tuple										=    tf.cast(tf.math.not_equal(__UpperCamelCase  ,  config.pad_token_id )  ,  tf.inta )
					if decoder_attention_mask is None:
										a_    :     Optional[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_    :     Union[str, Any]										=    tf.ones((config.encoder_layers, config.encoder_attention_heads) )
					if decoder_head_mask is None:
										a_    :     List[str]										=    tf.ones((config.decoder_layers, config.decoder_attention_heads) )
					if cross_attn_head_mask is None:
										a_    :     List[Any]										=    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__	(						lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase    ):
							lowerCamelCase__:     List[Any]							    =     (
							    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
							)
							lowerCamelCase__:     int							    =     (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
							lowerCamelCase__:     List[Any]							    =     (
							    {
							        """conversational""": TFBlenderbotSmallForConditionalGeneration,
							        """feature-extraction""": TFBlenderbotSmallModel,
							        """summarization""": TFBlenderbotSmallForConditionalGeneration,
							        """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
							        """translation""": TFBlenderbotSmallForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCamelCase__:     Optional[Any]							    =     True
							lowerCamelCase__:     Optional[int]							    =     False
							lowerCamelCase__:     str							    =     False

							def 						UpperCAmelCase(   self      :     Dict    ):
												a_    :     Union[str, Any]										=    TFBlenderbotSmallModelTester(self    )
												a_    :     Optional[Any]										=    ConfigTester(self      ,							config_class=lowerCamelCase_    )

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

							def 						UpperCAmelCase(   self      :     int    ):
												a_    :     List[str]										=    self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase_    )


@require_tokenizers
@require_tf
class   a__	(						unittest.TestCase    ):
							lowerCamelCase__:     Union[str, 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__:     Any							    =     """facebook/blenderbot_small-90M"""

							@cached_property
							def 						UpperCAmelCase(   self      :     Tuple    ):
												# use "old" tokenizer here because of bug when downloading new tokenizer
												return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""    )

							@cached_property
							def 						UpperCAmelCase(   self      :     Any    ):
												a_    :     Any										=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
												return model

							@slow
							def 						UpperCAmelCase(   self      :     Optional[int]    ):
												a_    :     Any										=    self.tokenizer(self.src_text      ,							return_tensors="""tf"""    )
												a_    :     List[str]										=    self.model.generate(
												    model_inputs.input_ids      ,							attention_mask=model_inputs.attention_mask      ,							num_beams=2      ,							use_cache=lowerCamelCase_      ,							)
												a_    :     int										=    self.tokenizer.batch_decode(generated_ids.numpy()      ,							skip_special_tokens=lowerCamelCase_    )[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.",
												)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase        =     {
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''PegasusXForConditionalGeneration''',
           '''PegasusXModel''',
           '''PegasusXPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_pegasus_x import (
                  PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PegasusXForConditionalGeneration,
                  PegasusXModel,
                  PegasusXPreTrainedModel,
              )


else:
       import sys

       __lowerCamelCase        =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



