






"""simple docstring"""







import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)





def 						lowercase__(A						)    ->List[str]:




						"""simple docstring"""



						if model.config.model_type == "gpt2":
												return model.transformer.h[0].mlp.c_fc
						return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
			import torch
			import torch.nn as nn


			class    __UpperCAmelCase(       nn.Module							):




									"""simple docstring"""




									def __init__(       self	,  snake_case__	,  snake_case__      ):
															'''simple docstring'''
															super().__init__()
															lowercase__    :				Tuple=	module
															lowercase__    :				str=	nn.Sequential(
															    nn.Linear(module.in_features	,  snake_case__	,  bias=snake_case__      )	,  nn.Linear(snake_case__	,  module.out_features	,  bias=snake_case__      )	,  )
															lowercase__    :				Any=	(2.0 / (5 * min(module.in_features	,  module.out_features      ))) ** 0.5
															nn.init.normal_(self.adapter[0].weight	,  std=snake_case__      )
															nn.init.zeros_(self.adapter[1].weight      )
															self.adapter.to(module.weight.device      )




									def 		UpperCAmelCase_	(       self	,  snake_case__	,  *snake_case__	,  **snake_case__      ):
															'''simple docstring'''
															return self.module(snake_case__	,  *snake_case__	,  **snake_case__      ) + self.adapter(snake_case__      )


@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class    __UpperCAmelCase(       unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase    	= "bigscience/bloom-1b7"

						# Constant values
						__lowerCamelCase    	= 2.1_09_65_95_52_69_25_74

						__lowerCamelCase    	= "Hello my name is"
						__lowerCamelCase    	= set()
						EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I"							)
						EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n"							)
						EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University"							)
						__lowerCamelCase    	= 10




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# Models and tokenizer
												lowercase__    :				Optional[Any]=	AutoTokenizer.from_pretrained(self.model_name      )


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												super().setUp()

												# Models and tokenizer
												lowercase__    :				Optional[Any]=	AutoModelForCausalLM.from_pretrained(
												    self.model_name	,  torch_dtype=torch.floataa	,  device_map="auto"      )
												lowercase__    :				str=	AutoModelForCausalLM.from_pretrained(self.model_name	,  load_in_abit=snake_case__	,  device_map="auto"      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												del self.model_fpaa
												del self.model_abit

												gc.collect()
												torch.cuda.empty_cache()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				str=	self.model_abit.config

												self.assertTrue(hasattr(snake_case__	,  "quantization_config"      )      )

												lowercase__    :				List[Any]=	config.to_dict()
												lowercase__    :				List[Any]=	config.to_diff_dict()

												lowercase__    :				Tuple=	config.to_json_string()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												from bitsandbytes.nn import Paramsabit

												lowercase__    :				str=	self.model_fpaa.get_memory_footprint()
												lowercase__    :				List[str]=	self.model_abit.get_memory_footprint()

												self.assertAlmostEqual(mem_fpaa / mem_abit	,  self.EXPECTED_RELATIVE_DIFFERENCE      )
												lowercase__    :				Optional[Any]=	get_some_linear_layer(self.model_abit      )
												self.assertTrue(linear.weight.__class__ == Paramsabit      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												from transformers import TaPreTrainedModel

												self.model_fpaa.get_memory_footprint()
												self.model_abit.get_memory_footprint()

												for name, module in self.model_abit.named_modules():
																		if isinstance(snake_case__	,  torch.nn.Linear      ):
																								if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
																														# 4-bit parameters are packed in uint8 variables
																														self.assertTrue(module.weight.dtype == torch.uinta      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				str=	self.tokenizer(self.input_text	,  return_tensors="pt"      )
												lowercase__    :				List[Any]=	self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0      )	,  max_new_tokens=10      )

												self.assertIn(self.tokenizer.decode(output_sequences[0]	,  skip_special_tokens=snake_case__      )	,  self.EXPECTED_OUTPUTS      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Any=	BitsAndBytesConfig()
												lowercase__    :				Dict=	True

												lowercase__    :				Dict=	AutoModelForCausalLM.from_pretrained(
												    self.model_name	,  quantization_config=snake_case__	,  device_map="auto"      )

												lowercase__    :				Optional[Any]=	self.tokenizer(self.input_text	,  return_tensors="pt"      )
												lowercase__    :				str=	model_abit_from_config.generate(
												    input_ids=encoded_input["input_ids"].to(0      )	,  max_new_tokens=10      )

												self.assertIn(self.tokenizer.decode(output_sequences[0]	,  skip_special_tokens=snake_case__      )	,  self.EXPECTED_OUTPUTS      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												with self.assertRaises(snake_case__      ), tempfile.TemporaryDirectory() as tmpdirname:
																		self.model_abit.save_pretrained(snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Dict=	BitsAndBytesConfig()

												with self.assertRaises(snake_case__      ):
																		lowercase__    :				List[str]=	AutoModelForCausalLM.from_pretrained(
																		    self.model_name	,  quantization_config=snake_case__	,  load_in_abit=snake_case__	,  device_map="auto"	,  bnb_abit_quant_type="nf4"	,  )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												with self.assertRaises(snake_case__      ):
																		# Tries with `str`
																		self.model_abit.to("cpu"      )

												with self.assertRaises(snake_case__      ):
																		# Tries with a `dtype``
																		self.model_abit.to(torch.floataa      )

												with self.assertRaises(snake_case__      ):
																		# Tries with a `device`
																		self.model_abit.to(torch.device("cuda:0"      )      )

												with self.assertRaises(snake_case__      ):
																		# Tries with a `device`
																		self.model_abit.float()

												with self.assertRaises(snake_case__      ):
																		# Tries with a `device`
																		self.model_abit.half()

												# Test if we did not break anything
												lowercase__    :				Optional[int]=	self.tokenizer(self.input_text	,  return_tensors="pt"      )

												lowercase__    :				Dict=	self.model_fpaa.to(torch.floataa      )
												lowercase__    :				str=	self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0      )	,  max_new_tokens=10      )

												# Check this does not throw an error
												lowercase__    :				Dict=	self.model_fpaa.to("cpu"      )

												# Check this does not throw an error
												lowercase__    :				Tuple=	self.model_fpaa.half()

												# Check this does not throw an error
												lowercase__    :				Dict=	self.model_fpaa.float()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	AutoModelForSeqaSeqLM.from_pretrained("t5-small"	,  load_in_abit=snake_case__	,  device_map="auto"      )
												self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa      )


@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class    __UpperCAmelCase(       unittest.TestCase							):




						"""simple docstring"""




						@classmethod
						def 		UpperCAmelCase_	(       cls      ):
												'''simple docstring'''
												lowercase__    :				Any=	"t5-small"
												lowercase__    :				List[Any]=	"google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
												lowercase__    :				Optional[int]=	AutoTokenizer.from_pretrained(cls.model_name      )
												lowercase__    :				List[Any]=	"Translate in German: Hello, my dog is cute"




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												gc.collect()
												torch.cuda.empty_cache()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												from transformers import TaForConditionalGeneration

												lowercase__    :				Optional[int]=	TaForConditionalGeneration._keep_in_fpaa_modules
												lowercase__    :				List[str]=	None

												# test with `t5-small`
												lowercase__    :				List[Any]=	TaForConditionalGeneration.from_pretrained(self.model_name	,  load_in_abit=snake_case__	,  device_map="auto"      )
												lowercase__    :				Dict=	self.tokenizer(self.input_text	,  return_tensors="pt"      ).to(0      )
												lowercase__    :				Any=	model.generate(**snake_case__      )

												# test with `flan-t5-small`
												lowercase__    :				Optional[Any]=	TaForConditionalGeneration.from_pretrained(
												    self.dense_act_model_name	,  load_in_abit=snake_case__	,  device_map="auto"      )
												lowercase__    :				Dict=	self.tokenizer(self.input_text	,  return_tensors="pt"      ).to(0      )
												lowercase__    :				int=	model.generate(**snake_case__      )
												lowercase__    :				int=	modules




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												import bitsandbytes as bnb

												from transformers import TaForConditionalGeneration

												# test with `t5-small`
												lowercase__    :				int=	TaForConditionalGeneration.from_pretrained(self.model_name	,  load_in_abit=snake_case__	,  device_map="auto"      )

												# there was a bug with decoders - this test checks that it is fixed
												self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q	,  bnb.nn.Linearabit      )      )

												lowercase__    :				str=	self.tokenizer(self.input_text	,  return_tensors="pt"      ).to(0      )
												lowercase__    :				Tuple=	model.generate(**snake_case__      )

												# test with `flan-t5-small`
												lowercase__    :				Optional[int]=	TaForConditionalGeneration.from_pretrained(
												    self.dense_act_model_name	,  load_in_abit=snake_case__	,  device_map="auto"      )
												lowercase__    :				Tuple=	self.tokenizer(self.input_text	,  return_tensors="pt"      ).to(0      )
												lowercase__    :				Dict=	model.generate(**snake_case__      )


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												super().setUp()
												# model_name
												lowercase__    :				Tuple=	"bigscience/bloom-560m"
												lowercase__    :				List[Any]=	"t5-small"

												# Different types of model

												lowercase__    :				Tuple=	AutoModel.from_pretrained(self.model_name	,  load_in_abit=snake_case__	,  device_map="auto"      )
												# Sequence classification model
												lowercase__    :				Any=	AutoModelForSequenceClassification.from_pretrained(
												    self.model_name	,  load_in_abit=snake_case__	,  device_map="auto"      )
												# CausalLM model
												lowercase__    :				Optional[int]=	AutoModelForCausalLM.from_pretrained(self.model_name	,  load_in_abit=snake_case__	,  device_map="auto"      )
												# Seq2seq model
												lowercase__    :				Dict=	AutoModelForSeqaSeqLM.from_pretrained(
												    self.seq_to_seq_name	,  load_in_abit=snake_case__	,  device_map="auto"      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												del self.base_model
												del self.sequence_model
												del self.model_abit
												del self.seq_to_seq_model

												gc.collect()
												torch.cuda.empty_cache()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												from bitsandbytes.nn import Paramsabit

												self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit      )

												# Other heads should be nn.Parameter
												self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter      )
												self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter      )
												self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter      )


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												super().setUp()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												del self.pipe

												gc.collect()
												torch.cuda.empty_cache()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				int=	pipeline(
												    "text-generation"	,  model=self.model_name	,  model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.floataa}	,  max_new_tokens=self.MAX_NEW_TOKENS	,  )

												# Real second forward pass
												lowercase__    :				Optional[Any]=	self.pipe(self.input_text      )
												self.assertIn(pipeline_output[0]["generated_text"]	,  self.EXPECTED_OUTPUTS      )


@require_torch_multi_gpu
class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												super().setUp()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	AutoModelForCausalLM.from_pretrained(
												    self.model_name	,  load_in_abit=snake_case__	,  device_map="balanced"      )

												# Check correct device map
												self.assertEqual(set(model_parallel.hf_device_map.values()      )	,  {0, 1}      )

												# Check that inference pass works on the model
												lowercase__    :				Optional[Any]=	self.tokenizer(self.input_text	,  return_tensors="pt"      )

												# Second real batch
												lowercase__    :				Union[str, Any]=	model_parallel.generate(input_ids=encoded_input["input_ids"].to(0      )	,  max_new_tokens=10      )
												self.assertIn(self.tokenizer.decode(output_parallel[0]	,  skip_special_tokens=snake_case__      )	,  self.EXPECTED_OUTPUTS      )


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Any=	"facebook/opt-350m"
												super().setUp()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												if version.parse(importlib.metadata.version("bitsandbytes"      )      ) < version.parse("0.37.0"      ):
																		return

												# Step 1: freeze all parameters
												lowercase__    :				str=	AutoModelForCausalLM.from_pretrained(self.model_name	,  load_in_abit=snake_case__      )

												self.assertEqual(set(model.hf_device_map.values()      )	,  {torch.cuda.current_device()}      )

												for param in model.parameters():
																		lowercase__    :				List[Any]=	False  # freeze the model - train adapters later
																		if param.ndim == 1:
																								# cast the small parameters (e.g. layernorm) to fp32 for stability
																								lowercase__    :				Optional[int]=	param.data.to(torch.floataa      )

        # Step 2: add adapters
												for _, module in model.named_modules():
																		if "OPTAttention" in repr(type(snake_case__      )      ):
																								lowercase__    :				Any=	LoRALayer(module.q_proj	,  rank=16      )
																								lowercase__    :				Union[str, Any]=	LoRALayer(module.k_proj	,  rank=16      )
																								lowercase__    :				str=	LoRALayer(module.v_proj	,  rank=16      )

        # Step 3: dummy batch
												lowercase__    :				int=	self.tokenizer("Test batch "	,  return_tensors="pt"      ).to(0      )

												# Step 4: Check if the gradient is not None
												with torch.cuda.amp.autocast():
																		lowercase__    :				Optional[int]=	model.forward(**snake_case__      )
																		out.logits.norm().backward()

												for module in model.modules():
																		if isinstance(snake_case__	,  snake_case__      ):
																								self.assertTrue(module.adapter[1].weight.grad is not None      )
																								self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0      )
																		elif isinstance(snake_case__	,  nn.Embedding      ):
																								self.assertTrue(module.weight.grad is None      )


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase    	= "gpt2-xl"
						__lowerCamelCase    	= 3.31_91_85_48_54_15_21_87














"""simple docstring"""







import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


a			:    Any				 =		get_logger(__name__)


a			:    Any				 =		r"""
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

"""


class    __UpperCAmelCase:




						"""simple docstring"""




						@add_start_docstrings(snake_case__      )
						def __call__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )


class    __UpperCAmelCase:




						"""simple docstring"""




						@add_start_docstrings(snake_case__      )
						def __call__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						@add_start_docstrings(snake_case__      )
						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  **snake_case__      ):
												'''simple docstring'''
												for processor in self:
																		lowercase__    :				Optional[Any]=	inspect.signature(processor.__call__      ).parameters
																		if len(snake_case__      ) > 3:
																								if not all(arg in kwargs for arg in list(function_args.keys()      )[2:]      ):
																														raise ValueError(
																														    F'''Make sure that all the required parameters: {list(function_args.keys()      )} for '''
																														    F'''{processor.__class__} are passed to the logits processor.'''      )
																								lowercase__    :				Union[str, Any]=	processor(snake_case__	,  snake_case__	,  snake_case__	,  **snake_case__      )
																		else:
																								lowercase__    :				Dict=	processor(snake_case__	,  snake_case__	,  snake_case__      )
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) or not (temperature > 0):
																		raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}'''      )

												lowercase__    :				Any=	temperature




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				int=	scores / self.temperature
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__ = -float("Inf"      )	,  snake_case__ = 1      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) or (top_p < 0 or top_p > 1.0):
																		raise ValueError(F'''`top_p` has to be a float > 0 and < 1, but is {top_p}'''      )
												if not isinstance(snake_case__	,  snake_case__      ) or (min_tokens_to_keep < 1):
																		raise ValueError(F'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'''      )

												lowercase__    :				int=	top_p
												lowercase__    :				Optional[int]=	filter_value
												lowercase__    :				Tuple=	min_tokens_to_keep




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				Dict=	lax.top_k(snake_case__	,  scores.shape[-1]      )

												lowercase__    :				Optional[int]=	jnp.full_like(snake_case__	,  self.filter_value      )
												lowercase__    :				Union[str, Any]=	jax.nn.softmax(snake_case__	,  axis=-1      ).cumsum(axis=-1      )
												lowercase__    :				str=	cumulative_probs < self.top_p

												# include the token that is higher than top_p as well
												lowercase__    :				str=	jnp.roll(snake_case__	,  1      )
												score_mask |= score_mask.at[:, 0].set(snake_case__      )

												# min tokens to keep
												lowercase__    :				Optional[int]=	score_mask.at[:, : self.min_tokens_to_keep].set(snake_case__      )

												lowercase__    :				str=	jnp.where(snake_case__	,  snake_case__	,  snake_case__      )
												lowercase__    :				str=	jax.lax.sort_key_val(snake_case__	,  snake_case__      )[-1]

												return next_scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__ = -float("Inf"      )	,  snake_case__ = 1      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) or top_k <= 0:
																		raise ValueError(F'''`top_k` has to be a strictly positive integer, but is {top_k}'''      )

												lowercase__    :				List[Any]=	max(snake_case__	,  snake_case__      )
												lowercase__    :				Dict=	filter_value




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				Optional[Any]=	scores.shape
												lowercase__    :				int=	jnp.full(batch_size * vocab_size	,  self.filter_value      )

												lowercase__    :				Dict=	min(self.top_k	,  scores.shape[-1]      )  # Safety check
												lowercase__,     lowercase__    :				List[Any]=	lax.top_k(snake_case__	,  snake_case__      )
												lowercase__    :				Optional[int]=	jnp.broadcast_to((jnp.arange(snake_case__      ) * vocab_size)[:, None]	,  (batch_size, topk)      ).flatten()
												lowercase__    :				str=	topk_scores.flatten()
												lowercase__    :				Any=	topk_indices.flatten() + shift

												lowercase__    :				Optional[Any]=	next_scores_flat.at[topk_indices_flat].set(snake_case__      )
												lowercase__    :				str=	next_scores_flat.reshape(snake_case__	,  snake_case__      )
												return next_scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	bos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	jnp.full(scores.shape	,  -float("inf"      )      )

												lowercase__    :				int=	1 - jnp.bool_(cur_len - 1      )

												lowercase__    :				int=	jnp.where(snake_case__	,  new_scores.at[:, self.bos_token_id].set(0      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	max_length
												lowercase__    :				str=	eos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	jnp.full(scores.shape	,  -float("inf"      )      )

												lowercase__    :				Any=	1 - jnp.bool_(cur_len - self.max_length + 1      )

												lowercase__    :				Optional[int]=	jnp.where(snake_case__	,  new_scores.at[:, self.eos_token_id].set(0      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) or min_length < 0:
																		raise ValueError(F'''`min_length` has to be a positive integer, but is {min_length}'''      )

												if not isinstance(snake_case__	,  snake_case__      ) or eos_token_id < 0:
																		raise ValueError(F'''`eos_token_id` has to be a positive integer, but is {eos_token_id}'''      )

												lowercase__    :				List[str]=	min_length
												lowercase__    :				Dict=	eos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												# create boolean flag to decide if min length penalty should be applied
												lowercase__    :				Tuple=	1 - jnp.clip(cur_len - self.min_length	,  0	,  1      )

												lowercase__    :				Dict=	jnp.where(snake_case__	,  scores.at[:, self.eos_token_id].set(-float("inf"      )      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	list(snake_case__      )
												lowercase__    :				List[Any]=	begin_index




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				str=	1 - jnp.bool_(cur_len - self.begin_index      )

												lowercase__    :				str=	jnp.where(snake_case__	,  scores.at[:, self.begin_suppress_tokens].set(-float("inf"      )      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	list(snake_case__      )




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	scores.at[..., self.suppress_tokens].set(-float("inf"      )      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				int=	dict(snake_case__      )
												# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
												# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
												# Indexes without forced tokens will have a negative value.
												lowercase__    :				List[Any]=	jnp.ones((max(force_token_map.keys()      ) + 1)	,  dtype=jnp.intaa      ) * -1
												for index, token in force_token_map.items():
																		if token is not None:
																								lowercase__    :				List[Any]=	force_token_array.at[index].set(snake_case__      )
												lowercase__    :				int=	jnp.intaa(snake_case__      )




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												def _force_token(snake_case__      ):
																		lowercase__    :				Dict=	scores.shape[0]
																		lowercase__    :				Any=	self.force_token_array[generation_idx]

																		lowercase__    :				List[Any]=	jnp.ones_like(snake_case__	,  dtype=scores.dtype      ) * -float("inf"      )
																		lowercase__    :				List[Any]=	jnp.zeros((batch_size, 1)	,  dtype=scores.dtype      )
																		lowercase__    :				List[str]=	lax.dynamic_update_slice(snake_case__	,  snake_case__	,  (0, current_token)      )
																		return new_scores

												lowercase__    :				Dict=	lax.cond(
												    cur_len >= self.force_token_array.shape[0]	,  lambda: scores	,  lambda: lax.cond(
												        self.force_token_array[cur_len] >= 0	,  lambda: _force_token(snake_case__      )	,  lambda: scores	,  )	,  )
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				str=	generate_config.eos_token_id
												lowercase__    :				Optional[int]=	generate_config.no_timestamps_token_id
												lowercase__    :				Dict=	generate_config.no_timestamps_token_id + 1

												lowercase__    :				List[Any]=	decoder_input_length + 1

												if generate_config.is_multilingual:
																		# room for language token and task token
																		self.begin_index += 2
												if hasattr(snake_case__	,  "max_initial_timestamp_index"      ):
																		lowercase__    :				int=	generate_config.max_initial_timestamp_index
												else:
																		lowercase__    :				Dict=	model_config.vocab_size
												if self.max_initial_timestamp_index is None:
																		lowercase__    :				str=	model_config.vocab_size




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												# suppress <|notimestamps|> which is handled by without_timestamps
												lowercase__    :				int=	scores.at[:, self.no_timestamps_token_id].set(-float("inf"      )      )

												def handle_pairs(snake_case__	,  snake_case__      ):
																		lowercase__    :				Union[str, Any]=	jnp.where((cur_len - self.begin_index) >= 1	,  snake_case__	,  snake_case__      )
																		lowercase__    :				Tuple=	jnp.where(
																		    input_ids_k[cur_len - 1] >= self.timestamp_begin	,  True and last_was_timestamp	,  snake_case__	,  )

																		lowercase__    :				int=	jnp.where((cur_len - self.begin_index) < 2	,  snake_case__	,  snake_case__      )
																		lowercase__    :				Optional[int]=	jnp.where(
																		    input_ids_k[cur_len - 2] >= self.timestamp_begin	,  snake_case__	,  snake_case__	,  )

																		return jnp.where(
																		    snake_case__	,  jnp.where(
																		        penultimate_was_timestamp > 0	,  scores_k.at[self.timestamp_begin :].set(-float("inf"      )      )	,  scores_k.at[: self.eos_token_id].set(-float("inf"      )      )	,  )	,  snake_case__	,  )

												lowercase__    :				List[str]=	jax.vmap(snake_case__      )(snake_case__	,  snake_case__      )

												lowercase__    :				str=	jnp.where(cur_len == self.begin_index	,  snake_case__	,  snake_case__      )
												lowercase__    :				List[Any]=	jnp.where(
												    self.max_initial_timestamp_index is not None	,  True and apply_max_initial_timestamp	,  snake_case__	,  )

												lowercase__    :				Any=	self.timestamp_begin + self.max_initial_timestamp_index

												lowercase__    :				str=	jnp.where(
												    snake_case__	,  scores.at[:, last_allowed + 1 :].set(-float("inf"      )      )	,  snake_case__	,  )

												# if sum of probability over timestamps is above any other token, sample timestamp
												lowercase__    :				str=	jax.nn.log_softmax(snake_case__	,  axis=-1      )

												def handle_cumulative_probs(snake_case__	,  snake_case__      ):
																		lowercase__    :				Dict=	jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]	,  axis=-1      )
																		lowercase__    :				Union[str, Any]=	jnp.max(logprobs_k[: self.timestamp_begin]      )
																		return jnp.where(
																		    timestamp_logprob > max_text_token_logprob	,  scores_k.at[: self.timestamp_begin].set(-float("inf"      )      )	,  snake_case__	,  )

												lowercase__    :				Optional[int]=	jax.vmap(snake_case__      )(snake_case__	,  snake_case__      )

												return scores






