




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 		__A   :
						UpperCamelCase      =       BlenderbotConfig
						UpperCamelCase      =       {}
						UpperCamelCase      =       """gelu"""

						def __init__(		self :Union[str, Any]     ,			__snake_case :Union[str, Any]     ,			__snake_case :Union[str, Any]=13     ,			__snake_case :Optional[Any]=7     ,			__snake_case :Optional[int]=True     ,			__snake_case :Optional[Any]=False     ,			__snake_case :Dict=99     ,			__snake_case :List[str]=32     ,			__snake_case :List[str]=2     ,			__snake_case :List[str]=4     ,			__snake_case :List[str]=37     ,			__snake_case :Any=0.1     ,			__snake_case :List[str]=0.1     ,			__snake_case :Union[str, Any]=20     ,			__snake_case :int=2     ,			__snake_case :Dict=1     ,			__snake_case :Any=0     ,			):
										'''simple docstring'''

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

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

						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						int										=ids_tensor([self.batch_size, self.seq_length - 1]     ,			self.vocab_size			)
										__magic_name__						:						Tuple										=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)     ,			1			)
										__magic_name__						:						Tuple										=tf.concat([input_ids, eos_tensor]     ,			axis=1			)

										__magic_name__						:						Optional[Any]										=ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size			)

										__magic_name__						:						Dict										=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     ,			)
										__magic_name__						:						List[str]										=prepare_blenderbot_inputs_dict(__snake_case     ,			__snake_case     ,			__snake_case			)
										return config, inputs_dict


						def      A__    (		self :List[str]     ,			__snake_case :Optional[Any]     ,			__snake_case :Dict			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=TFBlenderbotModel(config=__snake_case			).get_decoder()
										__magic_name__						:						Union[str, Any]										=inputs_dict["""input_ids"""]

										__magic_name__						:						Any										=input_ids[:1, :]
										__magic_name__						:						List[str]										=inputs_dict["""attention_mask"""][:1, :]
										__magic_name__						:						Dict										=inputs_dict["""head_mask"""]
										__magic_name__						:						Optional[Any]										=1

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

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

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

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

										__magic_name__						:						Optional[int]										=model(__snake_case     ,			attention_mask=__snake_case			)[0]
										__magic_name__						:						Dict										=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
										__magic_name__						:						Union[str, Any]										=int(ids_tensor((1,)     ,			output_from_past.shape[-1]			)			)
										__magic_name__						:						Any										=output_from_no_past[:, -3:, random_slice_idx]
										__magic_name__						:						Union[str, Any]										=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_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=None						,  lowerCamelCase=None						,  lowerCamelCase=None						,  lowerCamelCase=None						,  lowerCamelCase=None						,  ):
				if attention_mask is None:
								__magic_name__						:						Optional[int]										=tf.cast(tf.math.not_equal(lowerCamelCase						,  config.pad_token_id )						,  tf.inta )
				if decoder_attention_mask is None:
								__magic_name__						:						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:
								__magic_name__						:						Optional[Any]										=tf.ones((config.encoder_layers, config.encoder_attention_heads) )
				if decoder_head_mask is None:
								__magic_name__						:						Tuple										=tf.ones((config.decoder_layers, config.decoder_attention_heads) )
				if cross_attn_head_mask is None:
								__magic_name__						:						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 		__A   (      UpperCamelCase__       ,    UpperCamelCase__       ,    unittest.TestCase      ):
						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      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						str										=TFBlenderbotModelTester(self			)
										__magic_name__						:						Dict										=ConfigTester(self     ,			config_class=__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										self.config_tester.run_common_tests()


						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=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 		__A   (      unittest.TestCase      ):
						UpperCamelCase      =       ["""My friends are cool but they eat too many carbs."""]
						UpperCamelCase      =       """facebook/blenderbot-400M-distill"""

						@cached_property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return BlenderbotTokenizer.from_pretrained(self.model_name			)

						@cached_property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
										return model


						@slow
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

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










# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=torch.linspace(1     ,			self.config.sampling_eps     ,			__snake_case     ,			device=__snake_case			)

						def      A__    (		self :List[str]     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										if self.timesteps is None:
														raise ValueError(
														    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""			)

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										__magic_name__						:						int										=(
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										__magic_name__						:						Optional[int]										=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
										__magic_name__						:						str										=std.flatten()
										while len(std.shape			) < len(score.shape			):
														__magic_name__						:						List[str]										=std.unsqueeze(-1			)
										__magic_name__						:						Union[str, Any]										=-score / std

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

										__magic_name__						:						int										=self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										__magic_name__						:						Dict										=beta_t.flatten()
										while len(beta_t.shape			) < len(x.shape			):
														__magic_name__						:						Any										=beta_t.unsqueeze(-1			)
										__magic_name__						:						Dict										=-0.5 * beta_t * x

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

										# add noise
										__magic_name__						:						Optional[int]										=randn_tensor(x.shape     ,			layout=x.layout     ,			generator=__snake_case     ,			device=x.device     ,			dtype=x.dtype			)
										__magic_name__						:						Optional[Any]										=x_mean + diffusion * math.sqrt(-dt			) * noise

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




