





'''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  _lowerCamelCase				( lowercase						:      Union[str, Any]						)	->	List[Any]:
				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 				__SCREAMING_SNAKE_CASE  (nn.Module      ):






									"""simple docstring"""







									def __init__(  self     :					str					,				__a     :					List[Any]					,				__a     :					List[Any]					):
													super().__init__()
													_a								= module
													_a								= nn.Sequential(
													    nn.Linear(module.in_features					,				A_					,				bias=A_					)					,				nn.Linear(A_					,				module.out_features					,				bias=A_					)					,				)
													_a								= (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     :					List[Any]					,				*__a     :					List[Any]					,				**__a     :					str					):
													return self.module(A_					,				*A_					,				**A_					) + self.adapter(A_					)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				__a            ='bigscience/bloom-1b7'

				# Constant values
				__a            =2.1_09_65_95_52_69_25_74

				__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            =10




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= AutoTokenizer.from_pretrained(self.model_name					)


class 				__SCREAMING_SNAKE_CASE  (_UpperCAmelCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Tuple					):
								super().setUp()

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




				def   UpperCamelCase__   (  self     :					int					):
								del self.model_fpaa
								del self.model_abit

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




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= self.model_abit.config

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

								_a								= config.to_dict()
								_a								= config.to_diff_dict()

								_a								= config.to_json_string()




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								from bitsandbytes.nn import Paramsabit

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

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




				def   UpperCamelCase__   (  self     :					str					):
								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					):
								_a								= self.tokenizer(self.input_text					,				return_tensors="pt"					)
								_a								= 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					):
								_a								= BitsAndBytesConfig()
								_a								= True

								_a								= AutoModelForCausalLM.from_pretrained(
								    self.model_name					,				quantization_config=A_					,				device_map="auto"					)

								_a								= self.tokenizer(self.input_text					,				return_tensors="pt"					)
								_a								= 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     :					Tuple					):





								with self.assertRaises(A_					), tempfile.TemporaryDirectory() as tmpdirname:
												self.model_abit.save_pretrained(A_					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= BitsAndBytesConfig()

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




				def   UpperCamelCase__   (  self     :					List[Any]					):





								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
								_a								= self.tokenizer(self.input_text					,				return_tensors="pt"					)

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

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

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

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




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= 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 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				@classmethod
				def   UpperCamelCase__   (  cls     :					List[str]					):
								_a								= '''t5-small'''
								_a								= '''google/flan-t5-small'''  # flan-t5 uses dense-act instead of dense-relu-dense
								_a								= AutoTokenizer.from_pretrained(cls.model_name					)
								_a								= '''Translate in German: Hello, my dog is cute'''




				def   UpperCamelCase__   (  self     :					List[Any]					):
								gc.collect()
								torch.cuda.empty_cache()




				def   UpperCamelCase__   (  self     :					List[str]					):
								from transformers import TaForConditionalGeneration

								_a								= TaForConditionalGeneration._keep_in_fpaa_modules
								_a								= None

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

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




				def   UpperCamelCase__   (  self     :					Any					):
								import bitsandbytes as bnb

								from transformers import TaForConditionalGeneration

								# test with `t5-small`
								_a								= 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					)					)

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

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


class 				__SCREAMING_SNAKE_CASE  (_UpperCAmelCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Tuple					):
								super().setUp()
								# model_name
								_a								= '''bigscience/bloom-560m'''
								_a								= '''t5-small'''

								# Different types of model

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




				def   UpperCamelCase__   (  self     :					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     :					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 				__SCREAMING_SNAKE_CASE  (_UpperCAmelCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								super().setUp()




				def   UpperCamelCase__   (  self     :					int					):
								del self.pipe

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




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= 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
								_a								= self.pipe(self.input_text					)
								self.assertIn(pipeline_output[0]["generated_text"]					,				self.EXPECTED_OUTPUTS					)


@require_torch_multi_gpu
class 				__SCREAMING_SNAKE_CASE  (_UpperCAmelCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Tuple					):
								super().setUp()




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= 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
								_a								= self.tokenizer(self.input_text					,				return_tensors="pt"					)

								# Second real batch
								_a								= 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 				__SCREAMING_SNAKE_CASE  (_UpperCAmelCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= '''facebook/opt-350m'''
								super().setUp()




				def   UpperCamelCase__   (  self     :					int					):





								if version.parse(importlib.metadata.version("bitsandbytes"					)					) < version.parse("0.37.0"					):
												return

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

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

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

								# Step 4: Check if the gradient is not None
								with torch.cuda.amp.autocast():
												_a								= 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 				__SCREAMING_SNAKE_CASE  (_UpperCAmelCase      ):






				"""simple docstring"""







				__a            ='gpt2-xl'
				__a            =3.31_91_85_48_54_15_21_87



def 		_lowercase (     lowercase__						):
      if not all(x.isalpha() for x in string						):
            raise ValueError('''String must only contain alphabetic characters.'''						)

      __lowerCAmelCase					:						int					=					sorted(string.lower()						)
      return len(lowercase__						) == len(set(lowercase__						)						)


if __name__ == "__main__":
 _UpperCamelCase 			=		input("Enter a string ").strip()

 _UpperCamelCase 			=		is_isogram(input_str)
 print(F"{input_str} is {'an' if isogram else 'not an'} isogram.")


