





'''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 			SCREAMING_SNAKE_CASE   :
     lowerCAmelCase		      =	BlenderbotConfig
     lowerCAmelCase		      =	{}
     lowerCAmelCase		      =	'''gelu'''


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase=13						,     _UpperCAmelCase=7						,     _UpperCAmelCase=True						,     _UpperCAmelCase=False						,     _UpperCAmelCase=99						,     _UpperCAmelCase=32						,     _UpperCAmelCase=2						,     _UpperCAmelCase=4						,     _UpperCAmelCase=37						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=20						,     _UpperCAmelCase=2						,     _UpperCAmelCase=1						,     _UpperCAmelCase=0						,     ):

          '''simple docstring'''






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

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	ids_tensor([self.batch_size, self.seq_length - 1]						,     self.vocab_size)
          __A						:			List[Any]								=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)						,     1)
          __A						:			Union[str, Any]								=	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						:			List[str]								=	prepare_blenderbot_inputs_dict(lowercase_						,     lowercase_						,     lowercase_)
          return config, inputs_dict


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	TFBlenderbotModel(config=lowercase_).get_decoder()
          __A						:			Optional[Any]								=	inputs_dict['input_ids']

          __A						:			Any								=	input_ids[:1, :]
          __A						:			Union[str, Any]								=	inputs_dict['attention_mask'][:1, :]
          __A						:			Any								=	inputs_dict['head_mask']
          __A						:			Any								=	1

          # first forward pass
          __A						:			List[Any]								=	model(lowercase_						,     attention_mask=lowercase_						,     head_mask=lowercase_						,     use_cache=lowercase_)

          __A			,__A						:			Any								=	outputs.to_tuple()

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

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

          __A						:			Dict								=	model(lowercase_						,     attention_mask=lowercase_)[0]
          __A						:			List[str]								=	model(lowercase_						,     attention_mask=lowercase_						,     past_key_values=lowercase_)[0]

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

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

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






def 	_lowerCAmelCase      (  __snake_case  :			int			,							__snake_case  :			Optional[Any]			,							__snake_case  :			Union[str, Any]			,							__snake_case  :			int=None			,							__snake_case  :			Dict=None			,							__snake_case  :			str=None			,							__snake_case  :			str=None			,							__snake_case  :			int=None			,							)					->					Tuple:

     if attention_mask is None:
          __A						:			List[str]								=	tf.cast(tf.math.not_equal(a__			,							config.pad_token_id      )			,							tf.inta      )
     if decoder_attention_mask is None:
          __A						:			Dict								=	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						:			Optional[int]								=	tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
     if decoder_head_mask is None:
          __A						:			str								=	tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
     if cross_attn_head_mask is None:
          __A						:			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 			SCREAMING_SNAKE_CASE   (a__ ,		a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     lowerCAmelCase		      =	(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     lowerCAmelCase		      =	(
         {
             '''conversational''': TFBlenderbotForConditionalGeneration,
             '''feature-extraction''': TFBlenderbotModel,
             '''summarization''': TFBlenderbotForConditionalGeneration,
             '''text2text-generation''': TFBlenderbotForConditionalGeneration,
             '''translation''': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     lowerCAmelCase		      =	True
     lowerCAmelCase		      =	False
     lowerCAmelCase		      =	False


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	TFBlenderbotModelTester(self)
          __A						:			Optional[int]								=	ConfigTester(self						,     config_class=lowercase_)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          self.config_tester.run_common_tests()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*lowercase_)




@require_tokenizers
@require_tf
class 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):
     lowerCAmelCase		      =	['''My friends are cool but they eat too many carbs.''']
     lowerCAmelCase		      =	'''facebook/blenderbot-400M-distill'''


     @cached_property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return BlenderbotTokenizer.from_pretrained(self.model_name)


     @cached_property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			str								=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
          return model


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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






'''simple docstring'''




from math import pi, sqrt, tan






def 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if side_length < 0:
          raise ValueError('surface_area_cube() only accepts non-negative values'      )
     return 6 * side_length**2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if length < 0 or breadth < 0 or height < 0:
          raise ValueError('surface_area_cuboid() only accepts non-negative values'      )
     return 2 * ((length * breadth) + (breadth * height) + (length * height))






def 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if radius < 0:
          raise ValueError('surface_area_sphere() only accepts non-negative values'      )
     return 4 * pi * radius**2






def 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if radius < 0:
          raise ValueError('surface_area_hemisphere() only accepts non-negative values'      )
     return 3 * pi * radius**2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if radius < 0 or height < 0:
          raise ValueError('surface_area_cone() only accepts non-negative values'      )
     return pi * radius * (radius + (height**2 + radius**2) ** 0.5)






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if radius_a < 0 or radius_a < 0 or height < 0:
          raise ValueError(
              'surface_area_conical_frustum() only accepts non-negative values'      )
     __A						:			Union[str, Any]								=	(height**2 + (radius_a - radius_a) ** 2) ** 0.5
     return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if radius < 0 or height < 0:
          raise ValueError('surface_area_cylinder() only accepts non-negative values'      )
     return 2 * pi * radius * (height + radius)






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if torus_radius < 0 or tube_radius < 0:
          raise ValueError('surface_area_torus() only accepts non-negative values'      )
     if torus_radius < tube_radius:
          raise ValueError(
              'surface_area_torus() does not support spindle or self intersecting tori'      )
     return 4 * pow(__snake_case			,							2      ) * torus_radius * tube_radius






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if length < 0 or width < 0:
          raise ValueError('area_rectangle() only accepts non-negative values'      )
     return length * width






def 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if side_length < 0:
          raise ValueError('area_square() only accepts non-negative values'      )
     return side_length**2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if base < 0 or height < 0:
          raise ValueError('area_triangle() only accepts non-negative values'      )
     return (base * height) / 2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if sidea < 0 or sidea < 0 or sidea < 0:
          raise ValueError('area_triangle_three_sides() only accepts non-negative values'      )
     elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
          raise ValueError('Given three sides do not form a triangle'      )
     __A						:			int								=	(sidea + sidea + sidea) / 2
     __A						:			Tuple								=	sqrt(
         semi_perimeter
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)      )
     return area






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if base < 0 or height < 0:
          raise ValueError('area_parallelogram() only accepts non-negative values'      )
     return base * height






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if basea < 0 or basea < 0 or height < 0:
          raise ValueError('area_trapezium() only accepts non-negative values'      )
     return 1 / 2 * (basea + basea) * height






def 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if radius < 0:
          raise ValueError('area_circle() only accepts non-negative values'      )
     return pi * radius**2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if radius_x < 0 or radius_y < 0:
          raise ValueError('area_ellipse() only accepts non-negative values'      )
     return pi * radius_x * radius_y






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if diagonal_a < 0 or diagonal_a < 0:
          raise ValueError('area_rhombus() only accepts non-negative values'      )
     return 1 / 2 * diagonal_a * diagonal_a






def 	_lowerCAmelCase      (  __snake_case  :			int			,							__snake_case  :			float      )					->					float:
     if not isinstance(__snake_case			,							__snake_case      ) or sides < 3:
          raise ValueError(
              'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'      )
     elif length < 0:
          raise ValueError(
              'area_reg_polygon() only accepts non-negative values as \
length of a side'      )
     return (sides * length**2) / (4 * tan(pi / sides      ))
     return (sides * length**2) / (4 * tan(pi / sides      ))


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

  print('''[DEMO] Areas of various geometric shapes: \n''')
  print(f"""Rectangle: {area_rectangle(10, 20) = }""")
  print(f"""Square: {area_square(10) = }""")
  print(f"""Triangle: {area_triangle(10, 10) = }""")
  print(f"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
  print(f"""Parallelogram: {area_parallelogram(10, 20) = }""")
  print(f"""Rhombus: {area_rhombus(10, 20) = }""")
  print(f"""Trapezium: {area_trapezium(10, 20, 30) = }""")
  print(f"""Circle: {area_circle(20) = }""")
  print(f"""Ellipse: {area_ellipse(10, 20) = }""")
  print('''\nSurface Areas of various geometric shapes: \n''')
  print(f"""Cube: {surface_area_cube(20) = }""")
  print(f"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
  print(f"""Sphere: {surface_area_sphere(20) = }""")
  print(f"""Hemisphere: {surface_area_hemisphere(20) = }""")
  print(f"""Cone: {surface_area_cone(10, 20) = }""")
  print(f"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
  print(f"""Cylinder: {surface_area_cylinder(10, 20) = }""")
  print(f"""Torus: {surface_area_torus(20, 10) = }""")
  print(f"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
  print(f"""Square: {area_reg_polygon(4, 10) = }""")
  print(f"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")