

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 	lowercase    :


		"""simple docstring"""




		a__					       =  BlenderbotSmallConfig
		a__					       =  {}
		a__					       =  "gelu"


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

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


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

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

			_UpperCamelCase   :		int			=						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   :		Any			=						prepare_blenderbot_small_inputs_dict(__snake_case	,       __snake_case	,       __snake_case)
			return config, inputs_dict






		def 		A__					(				self	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Tuple			=						TFBlenderbotSmallModel(config=__snake_case).get_decoder()
			_UpperCamelCase   :		Dict			=						inputs_dict['input_ids']

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

			# first forward pass
			_UpperCamelCase   :		Tuple			=						model(__snake_case	,       attention_mask=__snake_case	,       head_mask=__snake_case	,       use_cache=__snake_case)

			_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						outputs.to_tuple()

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

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

			_UpperCamelCase   :		Union[str, Any]			=						model(__snake_case	,       attention_mask=__snake_case)[0]
			_UpperCamelCase   :		Union[str, Any]			=						model(__snake_case	,       attention_mask=__snake_case	,       past_key_values=__snake_case)[0]

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

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

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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     Union[str, Any]=None							,       UpperCAmelCase_							:     List[str]=None							,       UpperCAmelCase_							:     List[str]=None							,       UpperCAmelCase_							:     Dict=None							,       UpperCAmelCase_							:     Dict=None							,       )						->				Any:




	'''simple docstring'''




	if attention_mask is None:
		_UpperCamelCase   :		Optional[int]			=						tf.cast(tf.math.not_equal(UpperCAmelCase_							,       config.pad_token_id	)							,       tf.inta	)
	if decoder_attention_mask is None:
		_UpperCamelCase   :		List[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:
		_UpperCamelCase   :		Any			=						tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
	if decoder_head_mask is None:
		_UpperCamelCase   :		str			=						tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
	if cross_attn_head_mask is None:
		_UpperCamelCase   :		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 	lowercase    (				_lowercase    ,							_lowercase    ,							unittest.TestCase							):


		"""simple docstring"""




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


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						TFBlenderbotSmallModelTester(self)
			_UpperCamelCase   :		Any			=						ConfigTester(self	,       config_class=__snake_case)


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






		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.check_decoder_model_past_large_inputs(*__snake_case)







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


		"""simple docstring"""




		a__					       =  [
		    "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__					       =  "facebook/blenderbot_small-90M"


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


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






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Optional[int]			=						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=__snake_case	,       )
			_UpperCamelCase   :		Dict			=						self.tokenizer.batch_decode(generated_ids.numpy()	,       skip_special_tokens=__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.",
			)







from typing import TYPE_CHECKING

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


lowerCAmelCase__								=		{
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		["""BlipImageProcessor"""]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BlipModel""",
							    """BlipPreTrainedModel""",
							    """BlipForConditionalGeneration""",
							    """BlipForQuestionAnswering""",
							    """BlipVisionModel""",
							    """BlipTextModel""",
							    """BlipForImageTextRetrieval""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFBlipModel""",
							    """TFBlipPreTrainedModel""",
							    """TFBlipForConditionalGeneration""",
							    """TFBlipForQuestionAnswering""",
							    """TFBlipVisionModel""",
							    """TFBlipTextModel""",
							    """TFBlipForImageTextRetrieval""",
							]

if TYPE_CHECKING:
							from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
							from .processing_blip import BlipProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_blip import BlipImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blip import (
														    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlipForConditionalGeneration,
														    BlipForImageTextRetrieval,
														    BlipForQuestionAnswering,
														    BlipModel,
														    BlipPreTrainedModel,
														    BlipTextModel,
														    BlipVisionModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blip import (
														    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFBlipForConditionalGeneration,
														    TFBlipForImageTextRetrieval,
														    TFBlipForQuestionAnswering,
														    TFBlipModel,
														    TFBlipPreTrainedModel,
														    TFBlipTextModel,
														    TFBlipVisionModel,
														)

else:
							import sys

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




