# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)



@flax.struct.dataclass
class        lowercase_   :
							_lowerCamelCase						=		42

							# setable values
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		None



							@classmethod
							def 	UpperCamelCase	(						cls				,    lowercase_				,    lowercase_				,    lowercase_    ):
													return cls(common=lowercase_				,    init_noise_sigma=lowercase_				,    timesteps=lowercase_    )




@dataclass
class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		42




class        lowercase_   (					__snake_case						,			__snake_case		):
							_lowerCamelCase						=		[e.name for e in FlaxKarrasDiffusionSchedulers]

							_lowerCamelCase						=		42



							@property
							def 	UpperCamelCase	(						self    ):
													return True



							@register_to_config
							def __init__(						self				,    lowercase_ = 1_000				,    lowercase_ = 0.0_001				,    lowercase_ = 0.02				,    lowercase_ = "linear"				,    lowercase_ = None				,    lowercase_ = "fixed_small"				,    lowercase_ = True				,    lowercase_ = "epsilon"				,    lowercase_ = jnp.floataa				,    ):
													_snake_case						:					str    =   dtype



							def 	UpperCamelCase	(						self				,    lowercase_ = None    ):
													if common is None:
																			_snake_case						:					List[Any]    =   CommonSchedulerState.create(self    )

													# standard deviation of the initial noise distribution
													_snake_case						:					List[Any]    =   jnp.array(1.0				,    dtype=self.dtype    )

													_snake_case						:					str    =   jnp.arange(0				,    self.config.num_train_timesteps    ).round()[::-1]

													return DDPMSchedulerState.create(
													    common=lowercase_				,    init_noise_sigma=lowercase_				,    timesteps=lowercase_				,    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = None    ):
													return sample



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = ()    ):
													_snake_case						:					Optional[Any]    =   self.config.num_train_timesteps // num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# rounding to avoid issues when num_inference_step is power of 3
													_snake_case						:					Union[str, Any]    =   (jnp.arange(0				,    lowercase_    ) * step_ratio).round()[::-1]

													return state.replace(
													    num_inference_steps=lowercase_				,    timesteps=lowercase_				,    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_=None				,    lowercase_=None    ):
													_snake_case						:					List[Any]    =   state.common.alphas_cumprod[t]
													_snake_case						:					List[Any]    =   jnp.where(t > 0				,    state.common.alphas_cumprod[t - 1]				,    jnp.array(1.0				,    dtype=self.dtype    )    )

													# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
													# and sample from it to get previous sample
													# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
													_snake_case						:					List[str]    =   (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

													if variance_type is None:
																			_snake_case						:					int    =   self.config.variance_type

													# hacks - were probably added for training stability
													if variance_type == "fixed_small":
																			_snake_case						:					int    =   jnp.clip(lowercase_				,    a_min=1e-20    )
													# for rl-diffuser https://arxiv.org/abs/2205.09991
													elif variance_type == "fixed_small_log":
																			_snake_case						:					str    =   jnp.log(jnp.clip(lowercase_				,    a_min=1e-20    )    )
													elif variance_type == "fixed_large":
																			_snake_case						:					List[Any]    =   state.common.betas[t]
													elif variance_type == "fixed_large_log":
																			# Glide max_log
																			_snake_case						:					Any    =   jnp.log(state.common.betas[t]    )
													elif variance_type == "learned":
																			return predicted_variance
													elif variance_type == "learned_range":
																			_snake_case						:					Dict    =   variance
																			_snake_case						:					List[str]    =   state.common.betas[t]
																			_snake_case						:					Optional[int]    =   (predicted_variance + 1) / 2
																			_snake_case						:					str    =   frac * max_log + (1 - frac) * min_log

													return variance



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    lowercase_ = True				,    ):
													_snake_case						:					Any    =   timestep

													if key is None:
																			_snake_case						:					Any    =   jax.random.PRNGKey(0    )

													if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																			_snake_case    ,_snake_case						:					int    =   jnp.split(lowercase_				,    sample.shape[1]				,    axis=1    )
													else:
																			_snake_case						:					Tuple    =   None

													# 1. compute alphas, betas
													_snake_case						:					Optional[Any]    =   state.common.alphas_cumprod[t]
													_snake_case						:					List[Any]    =   jnp.where(t > 0				,    state.common.alphas_cumprod[t - 1]				,    jnp.array(1.0				,    dtype=self.dtype    )    )
													_snake_case						:					Optional[int]    =   1 - alpha_prod_t
													_snake_case						:					int    =   1 - alpha_prod_t_prev

													# 2. compute predicted original sample from predicted noise also called
													# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
													if self.config.prediction_type == "epsilon":
																			_snake_case						:					List[Any]    =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
													elif self.config.prediction_type == "sample":
																			_snake_case						:					Tuple    =   model_output
													elif self.config.prediction_type == "v_prediction":
																			_snake_case						:					Optional[Any]    =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
													else:
																			raise ValueError(
																			    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
																			    " for the FlaxDDPMScheduler."    )

													# 3. Clip "predicted x_0"
													if self.config.clip_sample:
																			_snake_case						:					Optional[int]    =   jnp.clip(lowercase_				,    -1				,    1    )

													# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
													# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
													_snake_case						:					Tuple    =   (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
													_snake_case						:					Tuple    =   state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

													# 5. Compute predicted previous sample µ_t
													# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
													_snake_case						:					Dict    =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

													# 6. Add noise
													def random_variance():
																			_snake_case						:					Optional[Any]    =   jax.random.split(lowercase_				,    num=1    )
																			_snake_case						:					List[str]    =   jax.random.normal(lowercase_				,    shape=model_output.shape				,    dtype=self.dtype    )
																			return (self._get_variance(lowercase_				,    lowercase_				,    predicted_variance=lowercase_    ) ** 0.5) * noise

													_snake_case						:					Dict    =   jnp.where(t > 0				,    random_variance()				,    jnp.zeros(model_output.shape				,    dtype=self.dtype    )    )

													_snake_case						:					Tuple    =   pred_prev_sample + variance

													if not return_dict:
																			return (pred_prev_sample, state)

													return FlaxDDPMSchedulerOutput(prev_sample=lowercase_				,    state=lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    ):
													return add_noise_common(state.common				,    lowercase_				,    lowercase_				,    lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    ):
													return get_velocity_common(state.common				,    lowercase_				,    lowercase_				,    lowercase_    )



							def __len__(						self    ):
													return self.config.num_train_timesteps
from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase=None ,      __lowercase=None						)	->	Tuple:

						'''simple docstring'''



						if attention_mask is None:
												_snake_case						:					List[Any]    =   tf.cast(tf.math.not_equal(__lowercase ,      config.pad_token_id						) ,      tf.inta						)
						return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class        lowercase_   :
							_lowerCamelCase						=		OPTConfig
							_lowerCamelCase						=		{}
							_lowerCamelCase						=		'gelu'



							def __init__(						self				,    lowercase_				,    lowercase_=13				,    lowercase_=7				,    lowercase_=True				,    lowercase_=False				,    lowercase_=99				,    lowercase_=16				,    lowercase_=2				,    lowercase_=4				,    lowercase_=4				,    lowercase_="gelu"				,    lowercase_=0.1				,    lowercase_=0.1				,    lowercase_=20				,    lowercase_=2				,    lowercase_=1				,    lowercase_=0				,    lowercase_=16				,    lowercase_=16				,    ):
													_snake_case						:					Dict    =   parent
													_snake_case						:					List[str]    =   batch_size
													_snake_case						:					Optional[Any]    =   seq_length
													_snake_case						:					Dict    =   is_training
													_snake_case						:					List[Any]    =   use_labels
													_snake_case						:					Dict    =   vocab_size
													_snake_case						:					Tuple    =   hidden_size
													_snake_case						:					Optional[int]    =   num_hidden_layers
													_snake_case						:					List[str]    =   num_attention_heads
													_snake_case						:					Tuple    =   intermediate_size
													_snake_case						:					Dict    =   hidden_act
													_snake_case						:					Any    =   hidden_dropout_prob
													_snake_case						:					Optional[int]    =   attention_probs_dropout_prob
													_snake_case						:					Tuple    =   max_position_embeddings
													_snake_case						:					List[Any]    =   eos_token_id
													_snake_case						:					Optional[int]    =   pad_token_id
													_snake_case						:					Dict    =   bos_token_id
													_snake_case						:					List[Any]    =   embed_dim
													_snake_case						:					Optional[int]    =   word_embed_proj_dim
													_snake_case						:					Union[str, Any]    =   False



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

													_snake_case						:					Optional[Any]    =   self.config_cls(
													    vocab_size=self.vocab_size				,    hidden_size=self.hidden_size				,    num_hidden_layers=self.num_hidden_layers				,    num_attention_heads=self.num_attention_heads				,    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				,    embed_dim=self.embed_dim				,    word_embed_proj_dim=self.word_embed_proj_dim				,    is_encoder_decoder=lowercase_				,    **self.config_updates				,    )
													_snake_case						:					Any    =   prepare_opt_inputs_dict(lowercase_				,    lowercase_    )
													return config, inputs_dict



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Any    =   TFOPTModel(config=lowercase_    )
													_snake_case						:					int    =   inputs_dict["input_ids"]

													_snake_case						:					Optional[int]    =   input_ids[:1, :]
													_snake_case						:					Any    =   inputs_dict["attention_mask"][:1, :]
													_snake_case						:					List[str]    =   1

													# first forward pass
													_snake_case						:					Union[str, Any]    =   model(lowercase_				,    attention_mask=lowercase_				,    use_cache=lowercase_    )

													_snake_case    ,_snake_case						:					str    =   outputs.to_tuple()

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

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

													_snake_case						:					Optional[Any]    =   model(lowercase_				,    attention_mask=lowercase_    )[0]
													_snake_case						:					Optional[int]    =   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
													_snake_case						:					int    =   int(ids_tensor((1,)				,    output_from_past.shape[-1]    )    )
													_snake_case						:					List[str]    =   output_from_no_past[:, -3:, random_slice_idx]
													_snake_case						:					List[Any]    =   output_from_past[:, :, random_slice_idx]

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




@require_tf
class        lowercase_   (					__snake_case						,			__snake_case						,			unittest.TestCase		):
							_lowerCamelCase						=		(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
							_lowerCamelCase						=		(TFOPTForCausalLM,) if is_tf_available() else ()
							_lowerCamelCase						=		(
							    {'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
							)
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		10



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   TFOPTModelTester(self    )
													_snake_case						:					Optional[int]    =   ConfigTester(self				,    config_class=lowercase_    )



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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.check_decoder_model_past_large_inputs(*lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case    ,_snake_case						:					str    =   self.model_tester.prepare_config_and_inputs_for_common()

													def _get_word_embedding_weight(lowercase_				,    lowercase_    ):
																			if hasattr(lowercase_				,    "weight"    ):
																									return embedding_layer.weight
																			else:
																									# Here we build the word embeddings weights if not exists.
																									# And then we retry to get the attribute once built.
																									model.build()
																									if hasattr(lowercase_				,    "weight"    ):
																															return embedding_layer.weight
																									else:
																															return None

													for model_class in self.all_model_classes:
																			for size in [config.vocab_size - 10, config.vocab_size + 10]:
																									# build the embeddings
																									_snake_case						:					Dict    =   model_class(config=lowercase_    )
																									_snake_case						:					Union[str, Any]    =   _get_word_embedding_weight(lowercase_				,    model.get_input_embeddings()    )
																									_snake_case						:					Optional[Any]    =   _get_word_embedding_weight(lowercase_				,    model.get_output_embeddings()    )

																									# reshape the embeddings
																									model.resize_token_embeddings(lowercase_    )
																									_snake_case						:					int    =   _get_word_embedding_weight(lowercase_				,    model.get_input_embeddings()    )
																									_snake_case						:					Tuple    =   _get_word_embedding_weight(lowercase_				,    model.get_output_embeddings()    )

																									# check that the resized embeddings size matches the desired size.
																									_snake_case						:					Any    =   size if size is not None else config.vocab_size

																									self.assertEqual(new_input_embeddings.shape[0]				,    lowercase_    )

																									# check that weights remain the same after resizing
																									_snake_case						:					Dict    =   True
																									for pa, pa in zip(old_input_embeddings.value()				,    new_input_embeddings.value()    ):
																															if tf.math.reduce_sum(tf.math.abs(pa - pa    )    ) > 0:
																																					_snake_case						:					Optional[int]    =   False
																									self.assertTrue(lowercase_    )

																									if old_output_embeddings is not None and new_output_embeddings is not None:
																															self.assertEqual(new_output_embeddings.shape[0]				,    lowercase_    )

																															_snake_case						:					Optional[int]    =   True
																															for pa, pa in zip(old_output_embeddings.value()				,    new_output_embeddings.value()    ):
																																					if tf.math.reduce_sum(tf.math.abs(pa - pa    )    ) > 0:
																																											_snake_case						:					str    =   False
																															self.assertTrue(lowercase_    )




def  snake_case						(__lowercase						)	->	Dict:

						'''simple docstring'''



						return tf.constant(__lowercase ,      dtype=tf.intaa						)



@require_tf
class        lowercase_   (					unittest.TestCase		):
							_lowerCamelCase						=		99



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   tf.ones((4, 1)				,    dtype=tf.intaa    ) * 2
													_snake_case						:					Optional[int]    =   tf.concat([ids_tensor((4, 6)				,    self.vocab_size - 3    ) + 3, eos_column_vector]				,    axis=1    )
													_snake_case						:					List[Any]    =   input_ids.shape[0]
													_snake_case						:					List[str]    =   OPTConfig(
													    vocab_size=self.vocab_size				,    hidden_size=24				,    num_hidden_layers=2				,    num_attention_heads=2				,    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




@require_sentencepiece
@require_tf
class        lowercase_   (					unittest.TestCase		):



							@slow
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   TFOPTModel.from_pretrained("facebook/opt-350m"    )
													_snake_case						:					Optional[Any]    =   _long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]    )
													_snake_case						:					Optional[int]    =   tf.not_equal(lowercase_				,    model.config.pad_token_id    )
													with tf.GradientTape():
																			_snake_case						:					List[Any]    =   model(input_ids=lowercase_				,    attention_mask=lowercase_    ).last_hidden_state
													_snake_case						:					List[str]    =   (1, 11, 512)
													self.assertEqual(output.shape				,    lowercase_    )
													_snake_case						:					Optional[int]    =   tf.constant(
													    [[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]]    )
													self.assertTrue(np.allclose(output[:, :3, :3]				,    lowercase_				,    atol=4e-3    )    )

													_snake_case						:					List[Any]    =   tf.function(lowercase_				,    jit_compile=lowercase_    )
													_snake_case						:					Tuple    =   xla_generate(lowercase_				,    lowercase_    )[0]
													self.assertTrue(np.allclose(output[:, :3, :3]				,    lowercase_				,    atol=4e-2    )    )




@require_tf
@slow
class        lowercase_   (					unittest.TestCase		):



							def 	UpperCamelCase	(						self    ):
													super().setUp()
													_snake_case						:					Optional[int]    =   "facebook/opt-350m"



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   TFOPTForCausalLM.from_pretrained(self.path_model    )
													_snake_case						:					Optional[Any]    =   GPTaTokenizer.from_pretrained(self.path_model    )

													_snake_case						:					List[str]    =   [
													    "Today is a beautiful day and I want to",
													    "In the city of",
													    "Paris is the capital of France and",
													    "Computers and mobile phones have taken",
													]
													# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
													_snake_case						:					List[Any]    =   tokenizer(lowercase_				,    return_tensors="tf"				,    padding=lowercase_				,    add_special_tokens=lowercase_    )
													_snake_case						:					List[str]    =   tf.math.reduce_mean(model(inputs.input_ids				,    attention_mask=inputs.attention_mask    )[0]				,    axis=-1    )
													_snake_case						:					Tuple    =   tf.constant(
													    [
													        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
													        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
													        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
													        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
													    ]    )
													self.assertTrue(np.allclose(lowercase_				,    lowercase_				,    atol=1e-4    )    )

													_snake_case						:					List[Any]    =   tf.function(lowercase_				,    jit_compile=lowercase_    )
													_snake_case						:					Tuple    =   tf.math.reduce_mean(xla_generate(inputs.input_ids				,    attention_mask=inputs.attention_mask    )[0]				,    axis=-1    )
													self.assertTrue(np.allclose(lowercase_				,    lowercase_				,    atol=1e-4    )    )




@require_tf
@slow
class        lowercase_   (					unittest.TestCase		):



							@property
							def 	UpperCamelCase	(						self    ):
													return [
													    "Today is a beautiful day and I want",
													    "In the city of",
													    "Paris is the capital of France and",
													    "Computers and mobile phones have taken",
													]



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   "facebook/opt-125m"

													_snake_case						:					int    =   [
													    "Today is a beautiful day and I want to",
													    "In the city of New York, the city",
													    "Paris is the capital of France and the capital",
													    "Computers and mobile phones have taken over the",
													]

													_snake_case						:					str    =   []
													_snake_case						:					Any    =   GPTaTokenizer.from_pretrained(lowercase_    )
													_snake_case						:					List[Any]    =   TFOPTForCausalLM.from_pretrained(lowercase_    )

													for prompt in self.prompts:
																			_snake_case						:					str    =   tokenizer(lowercase_				,    return_tensors="tf"    ).input_ids

																			_snake_case						:					Any    =   model.generate(lowercase_				,    max_length=10    )

																			_snake_case						:					List[str]    =   tokenizer.batch_decode(lowercase_				,    skip_special_tokens=lowercase_    )
																			predicted_outputs += generated_string

													self.assertListEqual(lowercase_				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   "facebook/opt-350m"

													_snake_case						:					Dict    =   GPTaTokenizer.from_pretrained(lowercase_    )
													_snake_case						:					Dict    =   TFOPTForCausalLM.from_pretrained(lowercase_    )

													_snake_case						:					int    =   "left"

													# use different length sentences to test batching
													_snake_case						:					Union[str, Any]    =   [
													    "Hello, my dog is a little",
													    "Today, I",
													]

													_snake_case						:					Optional[Any]    =   tokenizer(lowercase_				,    return_tensors="tf"				,    padding=lowercase_    )
													_snake_case						:					List[Any]    =   inputs["input_ids"]

													_snake_case						:					Union[str, Any]    =   model.generate(input_ids=lowercase_				,    attention_mask=inputs["attention_mask"]    )

													_snake_case						:					int    =   tokenizer(sentences[0]				,    return_tensors="tf"    ).input_ids
													_snake_case						:					List[str]    =   model.generate(input_ids=lowercase_    )

													_snake_case						:					Dict    =   inputs_non_padded.shape[-1] - tf.math.reduce_sum(
													    tf.cast(inputs["attention_mask"][-1]				,    tf.intaa    )    )
													_snake_case						:					int    =   tokenizer(sentences[1]				,    return_tensors="tf"    ).input_ids
													_snake_case						:					int    =   model.generate(input_ids=lowercase_				,    max_length=model.config.max_length - num_paddings    )

													_snake_case						:					Tuple    =   tokenizer.batch_decode(lowercase_				,    skip_special_tokens=lowercase_    )
													_snake_case						:					Dict    =   tokenizer.decode(output_non_padded[0]				,    skip_special_tokens=lowercase_    )
													_snake_case						:					Any    =   tokenizer.decode(output_padded[0]				,    skip_special_tokens=lowercase_    )

													_snake_case						:					Optional[Any]    =   [
													    "Hello, my dog is a little bit of a dork.\nI'm a little bit",
													    "Today, I was in the middle of a conversation with a friend about the",
													]
													self.assertListEqual(lowercase_				,    lowercase_    )
													self.assertListEqual(lowercase_				,    [non_padded_sentence, padded_sentence]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   "facebook/opt-350m"

													_snake_case						:					Optional[int]    =   [
													    "Today is a beautiful day and I want to",
													    "In the city of San Francisco, the city",
													    "Paris is the capital of France and the capital",
													    "Computers and mobile phones have taken over the",
													]

													_snake_case						:					str    =   []
													_snake_case						:					str    =   GPTaTokenizer.from_pretrained(lowercase_    )
													_snake_case						:					List[str]    =   TFOPTForCausalLM.from_pretrained(lowercase_    )

													for prompt in self.prompts:
																			_snake_case						:					Dict    =   tokenizer(lowercase_				,    return_tensors="tf"    ).input_ids

																			_snake_case						:					Any    =   model.generate(lowercase_				,    max_length=10    )

																			_snake_case						:					Tuple    =   tokenizer.batch_decode(lowercase_				,    skip_special_tokens=lowercase_    )
																			predicted_outputs += generated_string

													self.assertListEqual(lowercase_				,    lowercase_    )