




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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''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       :	Optional[int]       ,     a       :	nn.Module       ,     a       :	int			)			->					Optional[Any]:
												super().__init__()
												SCREAMING_SNAKE_CASE           =					module
												SCREAMING_SNAKE_CASE           =					nn.Sequential(
												    nn.Linear(module.in_features       ,     a       ,     bias=a			)       ,     nn.Linear(a       ,     module.out_features       ,     bias=a			)       ,     )
												SCREAMING_SNAKE_CASE           =					(2.0 / (5 * min(module.in_features       ,     module.out_features			))) ** 0.5
												nn.init.normal_(self.adapter[0].weight       ,     std=a			)
												nn.init.zeros_(self.adapter[1].weight			)
												self.adapter.to(module.weight.device			)







									def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	str       ,     *a       :	List[Any]       ,     **a       :	List[Any]			)			->					List[Any]:
												return self.module(a       ,     *a       ,     **a			) + self.adapter(a			)




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




							'''simple docstring'''



							a__						  =     '''bigscience/bloom-1b7'''

							# Constant values
							a__						  =     2.1_0_9_6_5_9_5_5_2_6_9_2_5_7_4

							a__						  =     '''Hello my name is'''
							a__						  =     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'''       )
							a__						  =     1_0





							def    _UpperCAmelCase     (					self       :	Dict			)			->					str:
										# Models and tokenizer
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained(self.model_name			)





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[int]:
										super().setUp()

										# Models and tokenizer
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(
										    self.model_name       ,     torch_dtype=torch.floataa       ,     device_map="""auto"""			)
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					int:
										del self.model_fpaa
										del self.model_abit

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





							def    _UpperCAmelCase     (					self       :	str			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.model_abit.config

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

										SCREAMING_SNAKE_CASE           =					config.to_dict()
										SCREAMING_SNAKE_CASE           =					config.to_diff_dict()

										SCREAMING_SNAKE_CASE           =					config.to_json_string()





							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[int]:
										from bitsandbytes.nn import Paramsabit

										SCREAMING_SNAKE_CASE           =					self.model_fpaa.get_memory_footprint()
										SCREAMING_SNAKE_CASE           =					self.model_abit.get_memory_footprint()

										self.assertAlmostEqual(mem_fpaa / mem_abit       ,     self.EXPECTED_RELATIVE_DIFFERENCE			)
										SCREAMING_SNAKE_CASE           =					get_some_linear_layer(self.model_abit			)
										self.assertTrue(linear.weight.__class__ == Paramsabit			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					List[Any]:
										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(a       ,     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       :	Dict			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			)
										SCREAMING_SNAKE_CASE           =					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=a			)       ,     self.EXPECTED_OUTPUTS			)





							def    _UpperCAmelCase     (					self       :	str			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					BitsAndBytesConfig()
										SCREAMING_SNAKE_CASE           =					True

										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(
										    self.model_name       ,     quantization_config=a       ,     device_map="""auto"""			)

										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			)
										SCREAMING_SNAKE_CASE           =					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=a			)       ,     self.EXPECTED_OUTPUTS			)





							def    _UpperCAmelCase     (					self       :	str			)			->					Optional[int]:
										with self.assertRaises(a			), tempfile.TemporaryDirectory() as tmpdirname:
													self.model_abit.save_pretrained(a			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					BitsAndBytesConfig()

										with self.assertRaises(a			):
													SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(
													    self.model_name       ,     quantization_config=a       ,     load_in_abit=a       ,     device_map="""auto"""       ,     bnb_abit_quant_type="""nf4"""       ,     )





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					int:
										with self.assertRaises(a			):
													# Tries with `str`
													self.model_abit.to("""cpu"""			)

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

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

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

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

										# Test if we did not break anything
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			)

										SCREAMING_SNAKE_CASE           =					self.model_fpaa.to(torch.floataa			)
										SCREAMING_SNAKE_CASE           =					self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0			)       ,     max_new_tokens=10			)

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

										# Check this does not throw an error
										SCREAMING_SNAKE_CASE           =					self.model_fpaa.half()

										# Check this does not throw an error
										SCREAMING_SNAKE_CASE           =					self.model_fpaa.float()







							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained("""t5-small"""       ,     load_in_abit=a       ,     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       :	Union[str, Any]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					"""t5-small"""
										SCREAMING_SNAKE_CASE           =					"""google/flan-t5-small"""  # flan-t5 uses dense-act instead of dense-relu-dense
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained(cls.model_name			)
										SCREAMING_SNAKE_CASE           =					"""Translate in German: Hello, my dog is cute"""





							def    _UpperCAmelCase     (					self       :	Any			)			->					List[str]:
										gc.collect()
										torch.cuda.empty_cache()





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					int:
										from transformers import TaForConditionalGeneration

										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration._keep_in_fpaa_modules
										SCREAMING_SNAKE_CASE           =					None

										# test with `t5-small`
										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			).to(0			)
										SCREAMING_SNAKE_CASE           =					model.generate(**a			)

										# test with `flan-t5-small`
										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			).to(0			)
										SCREAMING_SNAKE_CASE           =					model.generate(**a			)
										SCREAMING_SNAKE_CASE           =					modules







							def    _UpperCAmelCase     (					self       :	int			)			->					int:
										import bitsandbytes as bnb

										from transformers import TaForConditionalGeneration

										# test with `t5-small`
										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration.from_pretrained(self.model_name       ,     load_in_abit=a       ,     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			)			)

										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			).to(0			)
										SCREAMING_SNAKE_CASE           =					model.generate(**a			)

										# test with `flan-t5-small`
										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			).to(0			)
										SCREAMING_SNAKE_CASE           =					model.generate(**a			)





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					List[str]:
										super().setUp()
										# model_name
										SCREAMING_SNAKE_CASE           =					"""bigscience/bloom-560m"""
										SCREAMING_SNAKE_CASE           =					"""t5-small"""

										# Different types of model

										SCREAMING_SNAKE_CASE           =					AutoModel.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										# Sequence classification model
										SCREAMING_SNAKE_CASE           =					AutoModelForSequenceClassification.from_pretrained(
										    self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										# CausalLM model
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										# Seq2seq model
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained(
										    self.seq_to_seq_name       ,     load_in_abit=a       ,     device_map="""auto"""			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Tuple:
										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       :	Dict			)			->					List[Any]:
										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_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					str:
										super().setUp()





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Optional[Any]:
										del self.pipe

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







							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					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
										SCREAMING_SNAKE_CASE           =					self.pipe(self.input_text			)
										self.assertIn(pipeline_output[0]["""generated_text"""]       ,     self.EXPECTED_OUTPUTS			)





@require_torch_multi_gpu
class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Dict			)			->					Optional[int]:
										super().setUp()







							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(
										    self.model_name       ,     load_in_abit=a       ,     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
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			)

										# Second real batch
										SCREAMING_SNAKE_CASE           =					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=a			)       ,     self.EXPECTED_OUTPUTS			)





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	int			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					"""facebook/opt-350m"""
										super().setUp()







							def    _UpperCAmelCase     (					self       :	Any			)			->					Tuple:
										if version.parse(importlib.metadata.version("""bitsandbytes"""			)			) < version.parse("""0.37.0"""			):
													return

										# Step 1: freeze all parameters
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(self.model_name       ,     load_in_abit=a			)

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

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

        # Step 2: add adapters
										for _, module in model.named_modules():
													if "OPTAttention" in repr(type(a			)			):
																SCREAMING_SNAKE_CASE           =					LoRALayer(module.q_proj       ,     rank=16			)
																SCREAMING_SNAKE_CASE           =					LoRALayer(module.k_proj       ,     rank=16			)
																SCREAMING_SNAKE_CASE           =					LoRALayer(module.v_proj       ,     rank=16			)

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

										# Step 4: Check if the gradient is not None
										with torch.cuda.amp.autocast():
													SCREAMING_SNAKE_CASE           =					model.forward(**a			)
													out.logits.norm().backward()

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





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''gpt2-xl'''
							a__						  =     3.3_1_9_1_8_5_4_8_5_4_1_5_2_1_8_7












import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=True					,	SCREAMING_SNAKE_CASE="pt"   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					{"""add_prefix_space""": True} if isinstance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ) and not line.startswith(""" """   ) else {}
			SCREAMING_SNAKE_CASE           =					padding_side
			return tokenizer(
			    [line]					,	max_length=SCREAMING_SNAKE_CASE					,	padding="""max_length""" if pad_to_max_length else None					,	truncation=SCREAMING_SNAKE_CASE					,	return_tensors=SCREAMING_SNAKE_CASE					,	add_special_tokens=SCREAMING_SNAKE_CASE					,	**SCREAMING_SNAKE_CASE					,	)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=None					,	):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					input_ids.ne(SCREAMING_SNAKE_CASE   ).any(dim=0   )
			if attention_mask is None:
						return input_ids[:, keep_column_mask]
			else:
						return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def __init__(					self       :	Any       ,     a       :	int       ,     a       :	List[str]       ,     a       :	Dict       ,     a       :	List[Any]       ,     a       :	Optional[Any]="train"       ,     a       :	Tuple=None       ,     a       :	List[str]=None       ,     a       :	Optional[int]=None       ,     a       :	List[str]=""       ,     )			->					Optional[Any]:
										super().__init__()
										SCREAMING_SNAKE_CASE           =					Path(a			).joinpath(type_path + """.source"""			)
										SCREAMING_SNAKE_CASE           =					Path(a			).joinpath(type_path + """.target"""			)
										SCREAMING_SNAKE_CASE           =					self.get_char_lens(self.src_file			)
										SCREAMING_SNAKE_CASE           =					max_source_length
										SCREAMING_SNAKE_CASE           =					max_target_length
										assert min(self.src_lens			) > 0, f"""found empty line in {self.src_file}"""
										SCREAMING_SNAKE_CASE           =					tokenizer
										SCREAMING_SNAKE_CASE           =					prefix
										if n_obs is not None:
													SCREAMING_SNAKE_CASE           =					self.src_lens[:n_obs]
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					tgt_lang





							def __len__(					self       :	Union[str, Any]			)			->					Dict:
										return len(self.src_lens			)





							def __getitem__(					self       :	Any       ,     a       :	Optional[int]			)			->					Dict[str, torch.Tensor]:
										SCREAMING_SNAKE_CASE           =					index + 1  # linecache starts at 1
										SCREAMING_SNAKE_CASE           =					self.prefix + linecache.getline(str(self.src_file			)       ,     a			).rstrip("""\n"""			)
										SCREAMING_SNAKE_CASE           =					linecache.getline(str(self.tgt_file			)       ,     a			).rstrip("""\n"""			)
										assert source_line, f"""empty source line for index {index}"""
										assert tgt_line, f"""empty tgt line for index {index}"""

										# Need to add eos token manually for T5
										if isinstance(self.tokenizer       ,     a			):
													source_line += self.tokenizer.eos_token
													tgt_line += self.tokenizer.eos_token

										# Pad source and target to the right
										SCREAMING_SNAKE_CASE           =					(
										    self.tokenizer.question_encoder if isinstance(self.tokenizer       ,     a			) else self.tokenizer
										)
										SCREAMING_SNAKE_CASE           =					self.tokenizer.generator if isinstance(self.tokenizer       ,     a			) else self.tokenizer

										SCREAMING_SNAKE_CASE           =					encode_line(a       ,     a       ,     self.max_source_length       ,     """right"""			)
										SCREAMING_SNAKE_CASE           =					encode_line(a       ,     a       ,     self.max_target_length       ,     """right"""			)

										SCREAMING_SNAKE_CASE           =					source_inputs["""input_ids"""].squeeze()
										SCREAMING_SNAKE_CASE           =					target_inputs["""input_ids"""].squeeze()
										SCREAMING_SNAKE_CASE           =					source_inputs["""attention_mask"""].squeeze()
										return {
										    "input_ids": source_ids,
										    "attention_mask": src_mask,
										    "decoder_input_ids": target_ids,
										}





							@staticmethod
							def    _UpperCAmelCase     (					a       :	Optional[int]			)			->					List[Any]:
										return [len(a			) for x in Path(a			).open().readlines()]







							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	List[Any]			)			->					Dict[str, torch.Tensor]:
										SCREAMING_SNAKE_CASE           =					torch.stack([x["""input_ids"""] for x in batch]			)
										SCREAMING_SNAKE_CASE           =					torch.stack([x["""attention_mask"""] for x in batch]			)
										SCREAMING_SNAKE_CASE           =					torch.stack([x["""decoder_input_ids"""] for x in batch]			)
										SCREAMING_SNAKE_CASE           =					(
										    self.tokenizer.generator.pad_token_id
										    if isinstance(self.tokenizer       ,     a			)
										    else self.tokenizer.pad_token_id
										)
										SCREAMING_SNAKE_CASE           =					(
										    self.tokenizer.question_encoder.pad_token_id
										    if isinstance(self.tokenizer       ,     a			)
										    else self.tokenizer.pad_token_id
										)
										SCREAMING_SNAKE_CASE           =					trim_batch(a       ,     a			)
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					trim_batch(a       ,     a       ,     attention_mask=a			)
										SCREAMING_SNAKE_CASE           =					{
										    """input_ids""": source_ids,
										    """attention_mask""": source_mask,
										    """decoder_input_ids""": y,
										}
										return batch





__A						:							Tuple							=  getLogger(__name__)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return list(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					get_git_info()
			save_json(SCREAMING_SNAKE_CASE					,	os.path.join(SCREAMING_SNAKE_CASE					,	"""git_log.json"""   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=4					,	**SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			with open(SCREAMING_SNAKE_CASE					,	"""w"""   ) as f:
						json.dump(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	indent=SCREAMING_SNAKE_CASE					,	**SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			with open(SCREAMING_SNAKE_CASE   ) as f:
						return json.load(SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					{
			    """repo_id""": str(SCREAMING_SNAKE_CASE   ),
			    """repo_sha""": str(repo.head.object.hexsha   ),
			    """repo_branch""": str(repo.active_branch   ),
			    """hostname""": str(socket.gethostname()   ),
			}
			return repo_infos
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return list(map(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			with open(SCREAMING_SNAKE_CASE					,	"""wb"""   ) as f:
						return pickle.dump(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''

			def remove_articles(SCREAMING_SNAKE_CASE   ):
						return re.sub(r"""\b(a|an|the)\b"""					,	""" """					,	SCREAMING_SNAKE_CASE   )

			def white_space_fix(SCREAMING_SNAKE_CASE   ):
						return " ".join(text.split()   )

			def remove_punc(SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					set(string.punctuation   )
						return "".join(ch for ch in text if ch not in exclude   )

			def lower(SCREAMING_SNAKE_CASE   ):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE   )   )   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					normalize_answer(SCREAMING_SNAKE_CASE   ).split()
			SCREAMING_SNAKE_CASE           =					normalize_answer(SCREAMING_SNAKE_CASE   ).split()
			SCREAMING_SNAKE_CASE           =					Counter(SCREAMING_SNAKE_CASE   ) & Counter(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					sum(common.values()   )
			if num_same == 0:
						return 0
			SCREAMING_SNAKE_CASE           =					1.0 * num_same / len(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					1.0 * num_same / len(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					(2 * precision * recall) / (precision + recall)
			return fa
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return normalize_answer(SCREAMING_SNAKE_CASE   ) == normalize_answer(SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			assert len(SCREAMING_SNAKE_CASE   ) == len(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					0
			for hypo, pred in zip(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
						em += exact_match_score(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			if len(SCREAMING_SNAKE_CASE   ) > 0:
						em /= len(SCREAMING_SNAKE_CASE   )
			return {"em": em}
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return model_prefix.startswith("""rag"""   )




def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					{p: p for p in extra_params}
			# T5 models don't have `dropout` param, they have `dropout_rate` instead
			SCREAMING_SNAKE_CASE           =					"""dropout_rate"""
			for p in extra_params:
						if getattr(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
									if not hasattr(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ) and not hasattr(SCREAMING_SNAKE_CASE					,	equivalent_param[p]   ):
												logger.info("""config doesn't have a `{}` attribute""".format(SCREAMING_SNAKE_CASE   )   )
												delattr(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
												continue
									SCREAMING_SNAKE_CASE           =					p if hasattr(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ) else equivalent_param[p]
									setattr(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	getattr(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )   )
									delattr(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			return hparams, config






