






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
							import os

							# The slow tests are often failing with OOM error on GPU
							# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
							# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
							A_       :Any       =						'''platform'''

							import jax
							import jax.numpy as jnp

							from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
							    FlaxBlenderbotSmallForConditionalGeneration,
							    FlaxBlenderbotSmallModel,
							    shift_tokens_right,
							)






def 							A     (       a_							,a_							,a_=None							,a_=None							,a_=None							,a_=None							,a_=None							,a_=None							,)     ->						Tuple:







					if attention_mask is None:
										__UpperCamelCase      :					Optional[int]       		=np.where(input_ids != config.pad_token_id							,1							,0					)
					if decoder_attention_mask is None:
										__UpperCamelCase      :					Tuple       		=np.where(decoder_input_ids != config.pad_token_id							,1							,0					)
					if head_mask is None:
										__UpperCamelCase      :					Optional[int]       		=np.ones((config.encoder_layers, config.encoder_attention_heads)					)
					if decoder_head_mask is None:
										__UpperCamelCase      :					List[str]       		=np.ones((config.decoder_layers, config.decoder_attention_heads)					)
					if cross_attn_head_mask is None:
										__UpperCamelCase      :					Tuple       		=np.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": attention_mask,
					}




class 				__A     :





				"""simple docstring"""







				def __init__( self							,  lowerCamelCase__							,  lowerCamelCase__=13							,  lowerCamelCase__=7							,  lowerCamelCase__=True							,  lowerCamelCase__=False							,  lowerCamelCase__=99							,  lowerCamelCase__=16							,  lowerCamelCase__=2							,  lowerCamelCase__=4							,  lowerCamelCase__=4							,  lowerCamelCase__="gelu"							,  lowerCamelCase__=0.1							,  lowerCamelCase__=0.1							,  lowerCamelCase__=32							,  lowerCamelCase__=2							,  lowerCamelCase__=1							,  lowerCamelCase__=0							,  lowerCamelCase__=0.02							,  ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=parent
									__UpperCamelCase      :					int       		=batch_size
									__UpperCamelCase      :					int       		=seq_length
									__UpperCamelCase      :					Union[str, Any]       		=is_training
									__UpperCamelCase      :					str       		=use_labels
									__UpperCamelCase      :					str       		=vocab_size
									__UpperCamelCase      :					Optional[int]       		=hidden_size
									__UpperCamelCase      :					Tuple       		=num_hidden_layers
									__UpperCamelCase      :					Optional[int]       		=num_attention_heads
									__UpperCamelCase      :					List[str]       		=intermediate_size
									__UpperCamelCase      :					str       		=hidden_act
									__UpperCamelCase      :					Optional[Any]       		=hidden_dropout_prob
									__UpperCamelCase      :					Dict       		=attention_probs_dropout_prob
									__UpperCamelCase      :					Any       		=max_position_embeddings
									__UpperCamelCase      :					List[Any]       		=eos_token_id
									__UpperCamelCase      :					Tuple       		=pad_token_id
									__UpperCamelCase      :					Optional[int]       		=bos_token_id
									__UpperCamelCase      :					List[str]       		=initializer_range


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[Any]       		=np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,  self.vocab_size    )							,  3							,  self.vocab_size    )
									__UpperCamelCase      :					Any       		=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,  dtype=np.intaa    ))							,  -1    )

									__UpperCamelCase      :					Any       		=shift_tokens_right(lowercase_							,  1							,  2    )

									__UpperCamelCase      :					Dict       		=BlenderbotSmallConfig(
									    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_id=self.eos_token_id							,  bos_token_id=self.bos_token_id							,  pad_token_id=self.pad_token_id							,  initializer_range=self.initializer_range							,  use_cache=lowercase_							,  )
									__UpperCamelCase      :					Optional[int]       		=prepare_blenderbot_inputs_dict(lowercase_							,  lowercase_							,  lowercase_    )
									return config, inputs_dict


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase		,				__UpperCamelCase      :					Optional[int]       		=self.prepare_config_and_inputs()
									return config, inputs_dict


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		=20
									__UpperCamelCase      :					List[Any]       		=model_class_name(lowercase_    )

									__UpperCamelCase      :					Optional[int]       		=model.encode(inputs_dict['input_ids']    )

									__UpperCamelCase		,				__UpperCamelCase      :					Optional[Any]       		=(
									    inputs_dict['decoder_input_ids'],
									    inputs_dict['decoder_attention_mask'],
									)

									__UpperCamelCase      :					int       		=model.init_cache(decoder_input_ids.shape[0]							,  lowercase_							,  lowercase_    )
									__UpperCamelCase      :					Union[str, Any]       		=jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,  dtype='i4'    )

									__UpperCamelCase      :					List[Any]       		=jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]							,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,  )
									__UpperCamelCase      :					int       		=model.decode(
									    decoder_input_ids[:, :-1]							,  lowercase_							,  decoder_attention_mask=lowercase_							,  past_key_values=lowercase_							,  decoder_position_ids=lowercase_							,  )

									__UpperCamelCase      :					List[str]       		=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,  dtype='i4'    )
									__UpperCamelCase      :					Any       		=model.decode(
									    decoder_input_ids[:, -1:]							,  lowercase_							,  decoder_attention_mask=lowercase_							,  past_key_values=outputs_cache.past_key_values							,  decoder_position_ids=lowercase_							,  )

									__UpperCamelCase      :					Optional[Any]       		=model.decode(lowercase_							,  lowercase_    )

									__UpperCamelCase      :					List[str]       		=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
									self.parent.assertTrue(diff < 1E-3							,  msg=f'Max diff is {diff}'    )







				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					Tuple       		=20
									__UpperCamelCase      :					str       		=model_class_name(lowercase_    )

									__UpperCamelCase      :					List[Any]       		=model.encode(inputs_dict['input_ids']    )

									__UpperCamelCase		,				__UpperCamelCase      :					Dict       		=(
									    inputs_dict['decoder_input_ids'],
									    inputs_dict['decoder_attention_mask'],
									)

									__UpperCamelCase      :					Any       		=jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])    ),
									    ]							,  axis=-1							,  )

									__UpperCamelCase      :					Any       		=model.init_cache(decoder_input_ids.shape[0]							,  lowercase_							,  lowercase_    )
									__UpperCamelCase      :					int       		=jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]							,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,  )

									__UpperCamelCase      :					str       		=model.decode(
									    decoder_input_ids[:, :-1]							,  lowercase_							,  decoder_attention_mask=lowercase_							,  past_key_values=lowercase_							,  decoder_position_ids=lowercase_							,  )
									__UpperCamelCase      :					List[str]       		=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,  dtype='i4'    )
									__UpperCamelCase      :					List[Any]       		=model.decode(
									    decoder_input_ids[:, -1:]							,  lowercase_							,  past_key_values=outputs_cache.past_key_values							,  decoder_attention_mask=lowercase_							,  decoder_position_ids=lowercase_							,  )

									__UpperCamelCase      :					Any       		=model.decode(lowercase_							,  lowercase_							,  decoder_attention_mask=lowercase_    )

									__UpperCamelCase      :					int       		=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
									self.parent.assertTrue(diff < 1E-3							,  msg=f'Max diff is {diff}'    )




@require_flax
class 				__A     (			unittest.TestCase ):





				"""simple docstring"""







				UpperCamelCase__ :      Optional[Any]			 =9_9


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[Any]       		=np.array(
									    [
									        [71, 82, 18, 33, 46, 91, 2],
									        [68, 34, 26, 58, 30, 82, 2],
									        [5, 97, 17, 39, 94, 40, 2],
									        [76, 83, 94, 25, 70, 78, 2],
									        [87, 59, 41, 35, 48, 66, 2],
									        [55, 13, 16, 58, 5, 2, 1],  # note padding
									        [64, 27, 31, 51, 12, 75, 2],
									        [52, 64, 86, 17, 83, 39, 2],
									        [48, 61, 9, 24, 71, 82, 2],
									        [26, 1, 60, 48, 22, 13, 2],
									        [21, 5, 62, 28, 14, 76, 2],
									        [45, 98, 37, 86, 59, 48, 2],
									        [70, 70, 50, 9, 28, 0, 2],
									    ]							,  dtype=np.intaa							,  )

									__UpperCamelCase      :					Tuple       		=input_ids.shape[0]
									__UpperCamelCase      :					Any       		=BlenderbotSmallConfig(
									    vocab_size=self.vocab_size							,  d_model=24							,  encoder_layers=2							,  decoder_layers=2							,  encoder_attention_heads=2							,  decoder_attention_heads=2							,  encoder_ffn_dim=32							,  decoder_ffn_dim=32							,  max_position_embeddings=48							,  eos_token_id=2							,  pad_token_id=1							,  bos_token_id=0							,  )
									return config, input_ids, batch_size


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase      :					Optional[int]       		=self._get_config_and_data()
									__UpperCamelCase      :					Any       		=FlaxBlenderbotSmallForConditionalGeneration(lowercase_    )
									__UpperCamelCase      :					str       		=lm_model(input_ids=lowercase_    )
									__UpperCamelCase      :					Any       		=(batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['logits'].shape							,  lowercase_    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[Any]       		=BlenderbotSmallConfig(
									    vocab_size=self.vocab_size							,  d_model=14							,  encoder_layers=2							,  decoder_layers=2							,  encoder_attention_heads=2							,  decoder_attention_heads=2							,  encoder_ffn_dim=8							,  decoder_ffn_dim=8							,  max_position_embeddings=48							,  )
									__UpperCamelCase      :					Optional[int]       		=FlaxBlenderbotSmallForConditionalGeneration(lowercase_    )
									__UpperCamelCase      :					Union[str, Any]       		=np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]							,  dtype=np.intaa    )
									__UpperCamelCase      :					Tuple       		=np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]							,  dtype=np.intaa    )
									__UpperCamelCase      :					Dict       		=lm_model(input_ids=lowercase_							,  decoder_input_ids=lowercase_    )
									__UpperCamelCase      :					str       		=(*summary.shape, config.vocab_size)
									self.assertEqual(outputs['logits'].shape							,  lowercase_    )







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[Any]       		=np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]							,  dtype=np.intaa    )
									__UpperCamelCase      :					str       		=shift_tokens_right(lowercase_							,  1							,  2    )
									__UpperCamelCase      :					int       		=np.equal(lowercase_							,  1    ).astype(np.floataa    ).sum()
									__UpperCamelCase      :					Any       		=np.equal(lowercase_							,  1    ).astype(np.floataa    ).sum()
									self.assertEqual(shifted.shape							,  input_ids.shape    )
									self.assertEqual(lowercase_							,  n_pad_before - 1    )
									self.assertTrue(np.equal(shifted[:, 0]							,  2    ).all()    )




@require_flax
class 				__A     (			lowerCamelCase__      ,     unittest.TestCase      ,     lowerCamelCase__ ):





				"""simple docstring"""







				UpperCamelCase__ :      Optional[Any]			 =True
				UpperCamelCase__ :      List[Any]			 =(
				    (
				        FlaxBlenderbotSmallModel,
				        FlaxBlenderbotSmallForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				UpperCamelCase__ :      Optional[Any]			 =(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		=FlaxBlenderbotSmallModelTester(self    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase		,				__UpperCamelCase      :					str       		=self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward(lowercase_							,  lowercase_							,  lowercase_    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase		,				__UpperCamelCase      :					Union[str, Any]       		=self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_							,  lowercase_							,  lowercase_    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase		,				__UpperCamelCase      :					List[Any]       		=self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__    ):
																			__UpperCamelCase      :					str       		=self._prepare_for_class(lowercase_							,  lowercase_    )
																			__UpperCamelCase      :					Union[str, Any]       		=model_class(lowercase_    )

																			@jax.jit
																			def encode_jitted(lowerCamelCase__							,  lowerCamelCase__=None							,  **lowerCamelCase__    ):
																								return model.encode(input_ids=lowercase_							,  attention_mask=lowercase_    )

																			with self.subTest('JIT Enabled'    ):
																								__UpperCamelCase      :					Tuple       		=encode_jitted(**lowercase_    ).to_tuple()

																			with self.subTest('JIT Disabled'    ):
																								with jax.disable_jit():
																													__UpperCamelCase      :					str       		=encode_jitted(**lowercase_    ).to_tuple()

																			self.assertEqual(len(lowercase_    )							,  len(lowercase_    )    )
																			for jitted_output, output in zip(lowercase_							,  lowercase_    ):
																								self.assertEqual(jitted_output.shape							,  output.shape    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase		,				__UpperCamelCase      :					Optional[Any]       		=self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__    ):
																			__UpperCamelCase      :					Dict       		=model_class(lowercase_    )
																			__UpperCamelCase      :					Union[str, Any]       		=model.encode(inputs_dict['input_ids']							,  inputs_dict['attention_mask']    )

																			__UpperCamelCase      :					str       		={
																			    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																			    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																			    'encoder_outputs': encoder_outputs,
																			}

																			@jax.jit
																			def decode_jitted(lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__    ):
																								return model.decode(
																								    decoder_input_ids=lowercase_							,  decoder_attention_mask=lowercase_							,  encoder_outputs=lowercase_							,  )

																			with self.subTest('JIT Enabled'    ):
																								__UpperCamelCase      :					Optional[Any]       		=decode_jitted(**lowercase_    ).to_tuple()

																			with self.subTest('JIT Disabled'    ):
																								with jax.disable_jit():
																													__UpperCamelCase      :					str       		=decode_jitted(**lowercase_    ).to_tuple()

																			self.assertEqual(len(lowercase_    )							,  len(lowercase_    )    )
																			for jitted_output, output in zip(lowercase_							,  lowercase_    ):
																								self.assertEqual(jitted_output.shape							,  output.shape    )







				@slow
				def       __lowercase							( self    ):




									"""simple docstring"""

									for model_class_name in self.all_model_classes:
														__UpperCamelCase      :					Tuple       		=model_class_name.from_pretrained('facebook/blenderbot_small-90M'    )
														# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
														__UpperCamelCase      :					Tuple       		=np.ones((1, 1)    ) * model.config.eos_token_id
														__UpperCamelCase      :					List[str]       		=model(lowercase_    )
														self.assertIsNotNone(lowercase_    )




from __future__ import annotations




def 			UpperCAmelCase__				(			_A      : float	,      _A      : float	,      _A      : float	,      ):





  '''simple docstring'''







  if (stress, tangential_force, area).count(0							) != 1:
    raise ValueError('''You cannot supply more or less than 2 values'''							)
  elif stress < 0:
    raise ValueError('''Stress cannot be negative'''							)
  elif tangential_force < 0:
    raise ValueError('''Tangential Force cannot be negative'''							)
  elif area < 0:
    raise ValueError('''Area cannot be negative'''							)
  elif stress == 0:
    return (
        "stress",
        tangential_force / area,
    )
  elif tangential_force == 0:
    return (
        "tangential_force",
        stress * area,
    )
  else:
    return (
        "area",
        tangential_force / stress,
    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
