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_					:






    '''simple docstring'''






    a__     :						List[Any]      =				BlenderbotSmallConfig
    a__     :						int      =				{}
    a__     :						str      =				"""gelu"""







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

        __UpperCamelCase					:Any        =		hidden_dropout_prob
        __UpperCamelCase					:List[str]        =		attention_probs_dropout_prob
        __UpperCamelCase					:Union[str, Any]        =		max_position_embeddings
        __UpperCamelCase					:Union[str, Any]        =		eos_token_id
        __UpperCamelCase					:Dict        =		pad_token_id
        __UpperCamelCase					:List[str]        =		bos_token_id







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

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

        __UpperCamelCase					:List[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			,			)
        __UpperCamelCase					:List[str]        =		prepare_blenderbot_small_inputs_dict(a_			,			a_			,			a_)
        return config, inputs_dict







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase)	->     Optional[Any]:
        __UpperCamelCase					:Any        =		TFBlenderbotSmallModel(config=a_).get_decoder()
        __UpperCamelCase					:Tuple        =		inputs_dict['''input_ids''']

        __UpperCamelCase					:Union[str, Any]        =		input_ids[:1, :]
        __UpperCamelCase					:Any        =		inputs_dict['''attention_mask'''][:1, :]
        __UpperCamelCase					:Optional[int]        =		inputs_dict['''head_mask''']
        __UpperCamelCase					:List[Any]        =		1

        # first forward pass
        __UpperCamelCase					:Any        =		model(a_			,			attention_mask=a_			,			head_mask=a_			,			use_cache=a_)

        __UpperCamelCase					:Union[str, Any]        =		outputs.to_tuple()

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

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

        __UpperCamelCase					:Optional[int]        =		model(a_			,			attention_mask=a_)[0]
        __UpperCamelCase					:Union[str, Any]        =		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					:List[Any]        =		int(ids_tensor((1,)			,			output_from_past.shape[-1]))
        __UpperCamelCase					:List[Any]        =		output_from_no_past[:, -3:, random_slice_idx]
        __UpperCamelCase					:str        =		output_from_past[:, :, random_slice_idx]

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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=None  ,      SCREAMING_SNAKE_CASE=None  ,      SCREAMING_SNAKE_CASE=None  ,      SCREAMING_SNAKE_CASE=None  ,      SCREAMING_SNAKE_CASE=None  ,      ):
    '''simple docstring'''







    if attention_mask is None:
        __UpperCamelCase					:Any        =		tf.cast(tf.math.not_equal(_snake_case  ,      config.pad_token_id     )  ,      tf.inta     )
    if decoder_attention_mask is None:
        __UpperCamelCase					: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:
        __UpperCamelCase					:Optional[int]        =		tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
    if decoder_head_mask is None:
        __UpperCamelCase					:Tuple        =		tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
    if cross_attn_head_mask is None:
        __UpperCamelCase					: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      lowerCamelCase_					(       __snake_case						,     __snake_case						,     unittest.TestCase			):






    '''simple docstring'''






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







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:str        =		TFBlenderbotSmallModelTester(self)
        __UpperCamelCase					:List[str]        =		ConfigTester(self			,			config_class=a_)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        self.config_tester.run_common_tests()







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __UpperCamelCase					:int        =		self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*a_)


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






    '''simple docstring'''






    a__     :						Dict      =				[
        """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?"""
    ]
    a__     :						Dict      =				"""facebook/blenderbot_small-90M"""







    @cached_property
    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')







    @cached_property
    def       UpperCamelCase__       (     self)	->     str:
        __UpperCamelCase					:Tuple        =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
        return model







    @slow
    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:Any        =		self.tokenizer(self.src_text			,			return_tensors='''tf''')
        __UpperCamelCase					:List[str]        =		self.model.generate(
            model_inputs.input_ids			,			attention_mask=model_inputs.attention_mask			,			num_beams=2			,			use_cache=a_			,			)
        __UpperCamelCase					:Optional[Any]        =		self.tokenizer.batch_decode(generated_ids.numpy()			,			skip_special_tokens=a_)[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_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__lowercase										= {
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= ['''LayoutLMv3TokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= [
        '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''LayoutLMv3ForQuestionAnswering''',
        '''LayoutLMv3ForSequenceClassification''',
        '''LayoutLMv3ForTokenClassification''',
        '''LayoutLMv3Model''',
        '''LayoutLMv3PreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= [
        '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFLayoutLMv3ForQuestionAnswering''',
        '''TFLayoutLMv3ForSequenceClassification''',
        '''TFLayoutLMv3ForTokenClassification''',
        '''TFLayoutLMv3Model''',
        '''TFLayoutLMv3PreTrainedModel''',
    ]

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= ['''LayoutLMv3FeatureExtractor''']
    __lowercase										= ['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
    from .configuration_layoutlmva import (
        LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
        LayoutLMvaConfig,
        LayoutLMvaOnnxConfig,
    )
    from .processing_layoutlmva import LayoutLMvaProcessor
    from .tokenization_layoutlmva import LayoutLMvaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlmva import (
            LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMvaForQuestionAnswering,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaModel,
            LayoutLMvaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlmva import (
            TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
            TFLayoutLMvaModel,
            TFLayoutLMvaPreTrainedModel,
        )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
        from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
    import sys

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





