

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 		UpperCamelCase      (lowercase_:				int      )  ->   Optional[int]:
	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 					_a		(nn.Module       ):


						'''simple docstring'''






						def __init__(				self					, A__					, A__    ):
							super().__init__()
							A__							:					Optional[int]       =	module
							A__							:					List[Any]       =	nn.Sequential(
							    nn.Linear(module.in_features					, A__					, bias=A__    )					, nn.Linear(A__					, module.out_features					, bias=A__    )					, )
							A__							:					List[Any]       =	(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 						__A    (				self					, A__					, *A__					, **A__    ):
							return self.module(A__					, *A__					, **A__    ) + self.adapter(A__    )



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


					'''simple docstring'''






					UpperCAmelCase__:     int													= '''bigscience/bloom-1b7'''

					# Constant values
					UpperCAmelCase__:     List[Any]													= 2.1_09_65_95_52_69_25_74

					UpperCAmelCase__:     str													= '''Hello my name is'''
					UpperCAmelCase__:     List[Any]													= 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'''       )
					UpperCAmelCase__:     List[str]													= 10






					def 						__A    (				self    ):
						# Models and tokenizer
						A__							:					Any       =	AutoTokenizer.from_pretrained(self.model_name    )



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						super().setUp()

						# Models and tokenizer
						A__							:					Any       =	AutoModelForCausalLM.from_pretrained(
						    self.model_name					, torch_dtype=torch.floataa					, device_map="""auto"""    )
						A__							:					Dict       =	AutoModelForCausalLM.from_pretrained(self.model_name					, load_in_abit=A__					, device_map="""auto"""    )






					def 						__A    (				self    ):
						del self.model_fpaa
						del self.model_abit

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






					def 						__A    (				self    ):
						A__							:					int       =	self.model_abit.config

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

						A__							:					Optional[int]       =	config.to_dict()
						A__							:					str       =	config.to_diff_dict()

						A__							:					List[str]       =	config.to_json_string()






					def 						__A    (				self    ):
						from bitsandbytes.nn import Paramsabit

						A__							:					Any       =	self.model_fpaa.get_memory_footprint()
						A__							:					Union[str, Any]       =	self.model_abit.get_memory_footprint()

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






					def 						__A    (				self    ):
						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 						__A    (				self    ):
						A__							:					str       =	self.tokenizer(self.input_text					, return_tensors="""pt"""    )
						A__							:					Tuple       =	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 						__A    (				self    ):
						A__							:					Tuple       =	BitsAndBytesConfig()
						A__							:					Dict       =	True

						A__							:					List[str]       =	AutoModelForCausalLM.from_pretrained(
						    self.model_name					, quantization_config=A__					, device_map="""auto"""    )

						A__							:					str       =	self.tokenizer(self.input_text					, return_tensors="""pt"""    )
						A__							:					Dict       =	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 						__A    (				self    ):
						with self.assertRaises(A__    ), tempfile.TemporaryDirectory() as tmpdirname:
							self.model_abit.save_pretrained(A__    )






					def 						__A    (				self    ):
						A__							:					Dict       =	BitsAndBytesConfig()

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






					def 						__A    (				self    ):
						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__							:					Optional[int]       =	self.tokenizer(self.input_text					, return_tensors="""pt"""    )

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

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

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

						# Check this does not throw an error
						A__							:					List[str]       =	self.model_fpaa.float()






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	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 					_a		(unittest.TestCase       ):


					'''simple docstring'''






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






					def 						__A    (				self    ):
						gc.collect()
						torch.cuda.empty_cache()






					def 						__A    (				self    ):
						from transformers import TaForConditionalGeneration

						A__							:					Tuple       =	TaForConditionalGeneration._keep_in_fpaa_modules
						A__							:					Optional[Any]       =	None

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

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






					def 						__A    (				self    ):
						import bitsandbytes as bnb

						from transformers import TaForConditionalGeneration

						# test with `t5-small`
						A__							:					Dict       =	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__							:					Tuple       =	self.tokenizer(self.input_text					, return_tensors="""pt"""    ).to(0    )
						A__							:					List[Any]       =	model.generate(**A__    )

						# test with `flan-t5-small`
						A__							:					str       =	TaForConditionalGeneration.from_pretrained(
						    self.dense_act_model_name					, load_in_abit=A__					, device_map="""auto"""    )
						A__							:					Union[str, Any]       =	self.tokenizer(self.input_text					, return_tensors="""pt"""    ).to(0    )
						A__							:					Tuple       =	model.generate(**A__    )



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						super().setUp()
						# model_name
						A__							:					Any       =	"""bigscience/bloom-560m"""
						A__							:					int       =	"""t5-small"""

						# Different types of model

						A__							:					Union[str, Any]       =	AutoModel.from_pretrained(self.model_name					, load_in_abit=A__					, device_map="""auto"""    )
						# Sequence classification model
						A__							:					Dict       =	AutoModelForSequenceClassification.from_pretrained(
						    self.model_name					, load_in_abit=A__					, device_map="""auto"""    )
						# CausalLM model
						A__							:					str       =	AutoModelForCausalLM.from_pretrained(self.model_name					, load_in_abit=A__					, device_map="""auto"""    )
						# Seq2seq model
						A__							:					List[Any]       =	AutoModelForSeqaSeqLM.from_pretrained(
						    self.seq_to_seq_name					, load_in_abit=A__					, device_map="""auto"""    )






					def 						__A    (				self    ):
						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 						__A    (				self    ):
						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 					_a		(__magic_name__       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						super().setUp()






					def 						__A    (				self    ):
						del self.pipe

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






					def 						__A    (				self    ):
						A__							:					str       =	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__							:					str       =	self.pipe(self.input_text    )
						self.assertIn(pipeline_output[0]["""generated_text"""]					, self.EXPECTED_OUTPUTS    )



@require_torch_multi_gpu
class 					_a		(__magic_name__       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						super().setUp()






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	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__							:					Dict       =	self.tokenizer(self.input_text					, return_tensors="""pt"""    )

						# Second real batch
						A__							:					Optional[int]       =	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 					_a		(__magic_name__       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						A__							:					str       =	"""facebook/opt-350m"""
						super().setUp()






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

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

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

        # Step 3: dummy batch
						A__							:					List[Any]       =	self.tokenizer("""Test batch """					, return_tensors="""pt"""    ).to(0    )

						# Step 4: Check if the gradient is not None
						with torch.cuda.amp.autocast():
							A__							:					List[str]       =	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 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     Optional[int]													= '''gpt2-xl'''
					UpperCAmelCase__:     str													= 3.31_91_85_48_54_15_21_87






from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 					_a		(__magic_name__	,     __magic_name__	,     __magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     str													= [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']






					@register_to_config
					def __init__(				self					, A__					, A__					, A__ = None					, A__ = 5_0257					, A__ = 1024					, A__ = 768					, A__ = 12					, A__ = 12					, A__ = None					, A__ = "gelu_new"					, A__ = 0.1					, A__ = 0.1					, A__ = 0.1					, A__ = 1e-5					, A__ = 0.0_2					, A__ = True					, A__ = True					, A__ = False					, A__ = False					, ):
						super().__init__()

						A__							:					Union[str, Any]       =	prefix_length

						if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
							raise ValueError(
							    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
							    F""" `n_embd`: {n_embd} are not equal."""    )

						A__							:					str       =	prefix_inner_dim
						A__							:					Optional[Any]       =	prefix_hidden_dim

						A__							:					Tuple       =	(
						    nn.Linear(self.prefix_inner_dim					, self.prefix_hidden_dim    )
						    if self.prefix_hidden_dim is not None
						    else nn.Identity()
						)
						A__							:					int       =	(
						    nn.Linear(self.prefix_hidden_dim					, A__    ) if self.prefix_hidden_dim is not None else nn.Identity()
						)

						A__							:					Tuple       =	GPTaConfig(
						    vocab_size=A__					, n_positions=A__					, n_embd=A__					, n_layer=A__					, n_head=A__					, n_inner=A__					, activation_function=A__					, resid_pdrop=A__					, embd_pdrop=A__					, attn_pdrop=A__					, layer_norm_epsilon=A__					, initializer_range=A__					, scale_attn_weights=A__					, use_cache=A__					, scale_attn_by_inverse_layer_idx=A__					, reorder_and_upcast_attn=A__					, )
						A__							:					int       =	GPTaLMHeadModel(A__    )






					def 						__A    (				self					, A__					, A__					, A__ = None					, A__ = None					, ):
						A__							:					List[str]       =	self.transformer.transformer.wte(A__    )
						A__							:					int       =	self.encode_prefix(A__    )
						A__							:					int       =	self.decode_prefix(A__    )
						A__							:					Optional[Any]       =	torch.cat((prefix_embeds, embedding_text)					, dim=1    )

						if labels is not None:
							A__							:					Any       =	self.get_dummy_token(input_ids.shape[0]					, input_ids.device    )
							A__							:					List[Any]       =	torch.cat((dummy_token, input_ids)					, dim=1    )
						A__							:					List[str]       =	self.transformer(inputs_embeds=A__					, labels=A__					, attention_mask=A__    )
						if self.prefix_hidden_dim is not None:
							return out, hidden
						else:
							return out






					def 						__A    (				self					, A__					, A__    ):
						return torch.zeros(A__					, self.prefix_length					, dtype=torch.intaa					, device=A__    )






					def 						__A    (				self					, A__    ):
						return self.encode_prefix(A__    )






					@torch.no_grad()
					def 						__A    (				self					, A__					, A__					, A__    ):
						A__							:					List[Any]       =	torch.split(A__					, 1					, dim=0    )
						A__							:					Optional[int]       =	[]
						A__							:					str       =	[]
						for feature in features:
							A__							:					Dict       =	self.decode_prefix(feature.to(A__    )    )  # back to the clip feature
							# Only support beam search for now
							A__     ,     A__							:					Union[str, Any]       =	self.generate_beam(
							    input_embeds=A__					, device=A__					, eos_token_id=A__    )
							generated_tokens.append(output_tokens[0]    )
							generated_seq_lengths.append(seq_lengths[0]    )
						A__							:					int       =	torch.stack(A__    )
						A__							:					List[Any]       =	torch.stack(A__    )
						return generated_tokens, generated_seq_lengths






					@torch.no_grad()
					def 						__A    (				self					, A__=None					, A__=None					, A__=None					, A__ = 5					, A__ = 67					, A__ = 1.0					, A__ = None					, ):
						A__							:					Any       =	eos_token_id
						A__							:					Any       =	None
						A__							:					Optional[int]       =	None
						A__							:					Optional[Any]       =	torch.ones(A__					, device=A__					, dtype=torch.int    )
						A__							:					Any       =	torch.zeros(A__					, device=A__					, dtype=torch.bool    )

						if input_embeds is not None:
							A__							:					Dict       =	input_embeds
						else:
							A__							:					str       =	self.transformer.transformer.wte(A__    )

						for i in range(A__    ):
							A__							:					Dict       =	self.transformer(inputs_embeds=A__    )
							A__							:					str       =	outputs.logits
							A__							:					Union[str, Any]       =	logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
							A__							:					Any       =	logits.softmax(-1    ).log()

							if scores is None:
								A__     ,     A__							:					Optional[int]       =	logits.topk(A__					, -1    )
								A__							:					List[Any]       =	generated.expand(A__					, *generated.shape[1:]    )
								A__     ,     A__							:					List[Any]       =	next_tokens.permute(1					, 0    ), scores.squeeze(0    )
								if tokens is None:
									A__							:					Optional[Any]       =	next_tokens
								else:
									A__							:					List[Any]       =	tokens.expand(A__					, *tokens.shape[1:]    )
									A__							:					int       =	torch.cat((tokens, next_tokens)					, dim=1    )
							else:
								A__							:					Optional[int]       =	-float(np.inf    )
								A__							:					List[Any]       =	0
								A__							:					str       =	scores[:, None] + logits
								seq_lengths[~is_stopped] += 1
								A__							:					Dict       =	scores_sum / seq_lengths[:, None]
								A__     ,     A__							:					List[Any]       =	scores_sum_average.view(-1    ).topk(A__					, -1    )
								A__							:					Tuple       =	next_tokens // scores_sum.shape[1]
								A__							:					Optional[Any]       =	seq_lengths[next_tokens_source]
								A__							:					List[str]       =	next_tokens % scores_sum.shape[1]
								A__							:					Optional[int]       =	next_tokens.unsqueeze(1    )
								A__							:					int       =	tokens[next_tokens_source]
								A__							:					List[Any]       =	torch.cat((tokens, next_tokens)					, dim=1    )
								A__							:					str       =	generated[next_tokens_source]
								A__							:					Optional[Any]       =	scores_sum_average * seq_lengths
								A__							:					Union[str, Any]       =	is_stopped[next_tokens_source]

							A__							:					str       =	self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]					, 1					, -1    )
							A__							:					Optional[int]       =	torch.cat((generated, next_token_embed)					, dim=1    )
							A__							:					List[str]       =	is_stopped + next_tokens.eq(A__    ).squeeze()
							if is_stopped.all():
								break

						A__							:					Dict       =	scores / seq_lengths
						A__							:					Dict       =	scores.argsort(descending=A__    )
						# tokens tensors are already padded to max_seq_length
						A__							:					Union[str, Any]       =	[tokens[i] for i in order]
						A__							:					Any       =	torch.stack(A__					, dim=0    )
						A__							:					Dict       =	torch.tensor([seq_lengths[i] for i in order]					, dtype=seq_lengths.dtype    )
						return output_texts, seq_lengths



