
"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel




@require_tf
class 				lowercase__:
	'''simple docstring'''

	UpperCamelCase				=  BlenderbotConfig
	UpperCamelCase				=  {}
	UpperCamelCase				=  'gelu'





	def __init__(						self    :List[Any]		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :str=13		, lowerCamelCase_    :Any=7		, lowerCamelCase_    :Any=True		, lowerCamelCase_    :Dict=False		, lowerCamelCase_    :Optional[int]=99		, lowerCamelCase_    :Any=32		, lowerCamelCase_    :List[str]=2		, lowerCamelCase_    :Optional[Any]=4		, lowerCamelCase_    :Tuple=37		, lowerCamelCase_    :Optional[Any]=0.1		, lowerCamelCase_    :List[str]=0.1		, lowerCamelCase_    :Any=20		, lowerCamelCase_    :Tuple=2		, lowerCamelCase_    :int=1		, lowerCamelCase_    :List[str]=0		, )							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= parent
			SCREAMING_SNAKE_CASE       :     Any				= batch_size
			SCREAMING_SNAKE_CASE       :     List[str]				= seq_length
			SCREAMING_SNAKE_CASE       :     int				= is_training
			SCREAMING_SNAKE_CASE       :     Any				= use_labels
			SCREAMING_SNAKE_CASE       :     str				= vocab_size
			SCREAMING_SNAKE_CASE       :     str				= hidden_size
			SCREAMING_SNAKE_CASE       :     List[str]				= num_hidden_layers
			SCREAMING_SNAKE_CASE       :     str				= num_attention_heads
			SCREAMING_SNAKE_CASE       :     Any				= intermediate_size

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





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= ids_tensor([self.batch_size, self.seq_length - 1]		, self.vocab_size					)
			SCREAMING_SNAKE_CASE       :     Dict				= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)		, 1					)
			SCREAMING_SNAKE_CASE       :     List[str]				= tf.concat([input_ids, eos_tensor]		, axis=1					)

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

			SCREAMING_SNAKE_CASE       :     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		, )
			SCREAMING_SNAKE_CASE       :     int				= prepare_blenderbot_inputs_dict(UpperCamelCase_		, UpperCamelCase_		, UpperCamelCase_					)
			return config, inputs_dict







	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :int		, lowerCamelCase_    :Optional[int]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= TFBlenderbotModel(config=UpperCamelCase_					).get_decoder()
			SCREAMING_SNAKE_CASE       :     Dict				= inputs_dict['''input_ids''']

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

			# first forward pass
			SCREAMING_SNAKE_CASE       :     Any				= model(UpperCamelCase_		, attention_mask=UpperCamelCase_		, head_mask=UpperCamelCase_		, use_cache=UpperCamelCase_					)

			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= outputs.to_tuple()

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

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

			SCREAMING_SNAKE_CASE       :     Any				= model(UpperCamelCase_		, attention_mask=UpperCamelCase_					)[0]
			SCREAMING_SNAKE_CASE       :     List[str]				= model(UpperCamelCase_		, attention_mask=UpperCamelCase_		, past_key_values=UpperCamelCase_					)[0]

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

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

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





def __A						(   a_ :							List[str]		,		a_ :							str		,		a_ :							str		,		a_ :							int=None		,		a_ :							List[Any]=None		,		a_ :							Union[str, Any]=None		,		a_ :							Optional[Any]=None		,		a_ :							Tuple=None		,		)->							Tuple:




		'''simple docstring'''






		if attention_mask is None:
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= tf.cast(tf.math.not_equal(A__		,		config.pad_token_id     )		,		tf.inta     )
		if decoder_attention_mask is None:
				SCREAMING_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:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE       :     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__(				__lowerCamelCase		,						__lowerCamelCase		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
	UpperCamelCase				=  (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
	UpperCamelCase				=  (
	    {
	        'conversational': TFBlenderbotForConditionalGeneration,
	        'feature-extraction': TFBlenderbotModel,
	        'summarization': TFBlenderbotForConditionalGeneration,
	        'text2text-generation': TFBlenderbotForConditionalGeneration,
	        'translation': TFBlenderbotForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	UpperCamelCase				=  True
	UpperCamelCase				=  False
	UpperCamelCase				=  False





	def   __lowerCAmelCase (						self    :str					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= TFBlenderbotModelTester(self					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= ConfigTester(self		, config_class=UpperCamelCase_					)





	def   __lowerCAmelCase (						self    :Tuple					)							->			Dict:







			'''simple docstring'''




			self.config_tester.run_common_tests()







	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_					)





@require_tokenizers
@require_tf
class 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  ['My friends are cool but they eat too many carbs.']
	UpperCamelCase				=  'facebook/blenderbot-400M-distill'





	@cached_property
	def   __lowerCAmelCase (						self    :int					)							->			List[Any]:







			'''simple docstring'''




			return BlenderbotTokenizer.from_pretrained(self.model_name					)





	@cached_property
	def   __lowerCAmelCase (						self    :List[str]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
			return model







	@slow
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= self.tokenizer(self.src_text		, return_tensors='''tf'''					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.model.generate(
			    model_inputs.input_ids		, )
			SCREAMING_SNAKE_CASE       :     Dict				= self.tokenizer.batch_decode(generated_ids.numpy()		, skip_special_tokens=UpperCamelCase_					)[0]
			assert (
			    generated_words
			    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
			)







"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


lowerCamelCase__ :       Dict						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Dict						     =			{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """layoutlmv3"""





	def __init__(						self    :str		, lowerCamelCase_    :Optional[Any]=5_02_65		, lowerCamelCase_    :Dict=7_68		, lowerCamelCase_    :Union[str, Any]=12		, lowerCamelCase_    :Optional[Any]=12		, lowerCamelCase_    :Union[str, Any]=30_72		, lowerCamelCase_    :Any="gelu"		, lowerCamelCase_    :Union[str, Any]=0.1		, lowerCamelCase_    :str=0.1		, lowerCamelCase_    :Any=5_12		, lowerCamelCase_    :int=2		, lowerCamelCase_    :Optional[Any]=0.0_2		, lowerCamelCase_    :Optional[int]=1E-5		, lowerCamelCase_    :Dict=1		, lowerCamelCase_    :int=0		, lowerCamelCase_    :Tuple=2		, lowerCamelCase_    :List[str]=10_24		, lowerCamelCase_    :Tuple=1_28		, lowerCamelCase_    :Any=1_28		, lowerCamelCase_    :Optional[Any]=True		, lowerCamelCase_    :str=32		, lowerCamelCase_    :int=1_28		, lowerCamelCase_    :int=64		, lowerCamelCase_    :List[Any]=2_56		, lowerCamelCase_    :Any=True		, lowerCamelCase_    :str=True		, lowerCamelCase_    :Union[str, Any]=True		, lowerCamelCase_    :List[str]=2_24		, lowerCamelCase_    :Dict=3		, lowerCamelCase_    :Union[str, Any]=16		, lowerCamelCase_    :Any=None		, **lowerCamelCase_    :Optional[Any]		, )							->			int:







			'''simple docstring'''




			super().__init__(
			    vocab_size=lowerCamelCase_		, hidden_size=lowerCamelCase_		, num_hidden_layers=lowerCamelCase_		, num_attention_heads=lowerCamelCase_		, intermediate_size=lowerCamelCase_		, hidden_act=lowerCamelCase_		, hidden_dropout_prob=lowerCamelCase_		, attention_probs_dropout_prob=lowerCamelCase_		, max_position_embeddings=lowerCamelCase_		, type_vocab_size=lowerCamelCase_		, initializer_range=lowerCamelCase_		, layer_norm_eps=lowerCamelCase_		, pad_token_id=lowerCamelCase_		, bos_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, **lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= max_ad_position_embeddings
			SCREAMING_SNAKE_CASE       :     List[Any]				= coordinate_size
			SCREAMING_SNAKE_CASE       :     Tuple				= shape_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= has_relative_attention_bias
			SCREAMING_SNAKE_CASE       :     List[Any]				= rel_pos_bins
			SCREAMING_SNAKE_CASE       :     int				= max_rel_pos
			SCREAMING_SNAKE_CASE       :     Any				= has_spatial_attention_bias
			SCREAMING_SNAKE_CASE       :     List[Any]				= rel_ad_pos_bins
			SCREAMING_SNAKE_CASE       :     Dict				= max_rel_ad_pos
			SCREAMING_SNAKE_CASE       :     Optional[int]				= text_embed
			SCREAMING_SNAKE_CASE       :     Any				= visual_embed
			SCREAMING_SNAKE_CASE       :     Any				= input_size
			SCREAMING_SNAKE_CASE       :     Tuple				= num_channels
			SCREAMING_SNAKE_CASE       :     List[str]				= patch_size
			SCREAMING_SNAKE_CASE       :     str				= classifier_dropout





class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  version.parse("""1.12"""   )





	@property
	def   __lowerCAmelCase (						self    :List[Any]					)							->			Mapping[str, Mapping[int, str]]:







			'''simple docstring'''




			if self.task in ["question-answering", "sequence-classification"]:
					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
					        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
					        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
					    ]					)
			else:
					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
					        ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
					    ]					)





	@property
	def   __lowerCAmelCase (						self    :Optional[int]					)							->			float:







			'''simple docstring'''




			return 1E-5





	@property
	def   __lowerCAmelCase (						self    :Tuple					)							->			int:







			'''simple docstring'''




			return 12







	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :"ProcessorMixin"		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional["TensorType"] = None		, lowerCamelCase_    :int = 3		, lowerCamelCase_    :int = 40		, lowerCamelCase_    :int = 40		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			setattr(processor.image_processor		, '''apply_ocr'''		, lowerCamelCase_					)

			# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
			SCREAMING_SNAKE_CASE       :     Dict				= compute_effective_axis_dimension(
			    lowerCamelCase_		, fixed_dimension=OnnxConfig.default_fixed_batch		, num_token_to_add=0					)
			# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= processor.tokenizer.num_special_tokens_to_add(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Any				= compute_effective_axis_dimension(
			    lowerCamelCase_		, fixed_dimension=OnnxConfig.default_fixed_sequence		, num_token_to_add=lowerCamelCase_					)
			# Generate dummy inputs according to compute batch and sequence
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [[''' '''.join([processor.tokenizer.unk_token]					) * seq_length]] * batch_size

			# Generate dummy bounding boxes
			SCREAMING_SNAKE_CASE       :     int				= [[[48, 84, 73, 1_28]]] * batch_size

			# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
			# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
			SCREAMING_SNAKE_CASE       :     List[Any]				= self._generate_dummy_images(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     int				= dict(
			    processor(
			        lowerCamelCase_		, text=lowerCamelCase_		, boxes=lowerCamelCase_		, return_tensors=lowerCamelCase_		, )					)

			return inputs





