
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
    import torch


lowerCamelCase_						     =					logging.get_logger(__name__)




@dataclass
class      __A:

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=6.0	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_="fp4"	,      SCREAMING_SNAKE_CASE_=False	,      **SCREAMING_SNAKE_CASE_	,      ):
       UpperCamelCase__				=    load_in_abit
       UpperCamelCase__				=    load_in_abit
       UpperCamelCase__				=    llm_inta_threshold
       UpperCamelCase__				=    llm_inta_skip_modules
       UpperCamelCase__				=    llm_inta_enable_fpaa_cpu_offload
       UpperCamelCase__				=    llm_inta_has_fpaa_weight
       UpperCamelCase__				=    bnb_abit_quant_type
       UpperCamelCase__				=    bnb_abit_use_double_quant

       if bnb_abit_compute_dtype is None:
            UpperCamelCase__				=    torch.floataa
       elif isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    getattr(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
       elif isinstance(SCREAMING_SNAKE_CASE_	,      torch.dtype						):
            UpperCamelCase__				=    bnb_abit_compute_dtype
       else:
            raise ValueError("""bnb_4bit_compute_dtype must be a string or a torch.dtype"""						)

       self.post_init()







  def 	UpperCAmelCase_ (self						):
       if not isinstance(self.llm_inta_threshold	,      SCREAMING_SNAKE_CASE_						):
            raise ValueError("""llm_int8_threshold must be a float"""						)

       if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules	,      SCREAMING_SNAKE_CASE_						):
            raise ValueError("""llm_int8_skip_modules must be a list of strings"""						)
       if not isinstance(self.llm_inta_enable_fpaa_cpu_offload	,      SCREAMING_SNAKE_CASE_						):
            raise ValueError("""llm_int8_enable_fp32_cpu_offload must be a boolean"""						)

       if not isinstance(self.llm_inta_has_fpaa_weight	,      SCREAMING_SNAKE_CASE_						):
            raise ValueError("""llm_int8_has_fp16_weight must be a boolean"""						)

       if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype	,      torch.dtype						):
            raise ValueError("""bnb_4bit_compute_dtype must be torch.dtype"""						)

       if not isinstance(self.bnb_abit_quant_type	,      SCREAMING_SNAKE_CASE_						):
            raise ValueError("""bnb_4bit_quant_type must be a string"""						)

       if not isinstance(self.bnb_abit_use_double_quant	,      SCREAMING_SNAKE_CASE_						):
            raise ValueError("""bnb_4bit_use_double_quant must be a boolean"""						)

       if self.load_in_abit and not version.parse(importlib.metadata.version("""bitsandbytes"""						)						) >= version.parse(
           """0.39.0"""						):
            raise ValueError(
                """4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version"""						)







  def 	UpperCAmelCase_ (self						):
       return self.load_in_abit or self.load_in_abit







  def 	UpperCAmelCase_ (self						):
       if self.load_in_abit:
            return "llm_int8"
       elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
            return "fp4"
       elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
            return "nf4"
       else:
            return None







  @classmethod
  def 	UpperCAmelCase_ (cls	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    cls(**SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    []
       for key, value in kwargs.items():
            if hasattr(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
                 setattr(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
                 to_remove.append(SCREAMING_SNAKE_CASE_						)
       for key in to_remove:
            kwargs.pop(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       if return_unused_kwargs:
            return config, kwargs
       else:
            return config







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       with open(SCREAMING_SNAKE_CASE_	,      """w"""	,      encoding="""utf-8"""						) as writer:
            UpperCamelCase__				=    self.to_dict()
            UpperCamelCase__				=    json.dumps(SCREAMING_SNAKE_CASE_	,      indent=2	,      sort_keys=SCREAMING_SNAKE_CASE_						) + """\n"""

            writer.write(SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    copy.deepcopy(self.__dict__						)
       UpperCamelCase__				=    str(output["""bnb_4bit_compute_dtype"""]						).split("""."""						)[1]

       return output







  def __repr__(self						):
       return F"{self.__class__.__name__} {self.to_json_string()}"







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_ = True						):
       if use_diff is True:
            UpperCamelCase__				=    self.to_diff_dict()
       else:
            UpperCamelCase__				=    self.to_dict()
       return json.dumps(SCREAMING_SNAKE_CASE_	,      indent=2	,      sort_keys=SCREAMING_SNAKE_CASE_						) + "\n"







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.to_dict()

       # get the default config dict
       UpperCamelCase__				=    BitsAndBytesConfig().to_dict()

       UpperCamelCase__				=    {}

       # only serialize values that differ from the default config
       for key, value in config_dict.items():
            if value != default_config_dict[key]:
                 UpperCamelCase__				=    value

       return serializable_config_dict

import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
    import torch

    from transformers import AutoModelForCausalLM




@require_torch
class      __A(    unittest.TestCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.decode(greedy_ids[0]						)

       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)
       # The greedy text should be printed to stdout, except for the final "\n" in the streamer
       UpperCamelCase__				=    cs.out[:-1]

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.decode(greedy_ids[0]						)

       UpperCamelCase__				=    TextIteratorStreamer(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
       UpperCamelCase__				=    Thread(target=model.generate	,      kwargs=SCREAMING_SNAKE_CASE_						)
       thread.start()
       UpperCamelCase__				=    """"""
       for new_text in streamer:
            streamer_text += new_text

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    greedy_ids[:, input_ids.shape[1] :]
       UpperCamelCase__				=    tokenizer.decode(new_greedy_ids[0]						)

       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_	,      skip_prompt=SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)
       # The greedy text should be printed to stdout, except for the final "\n" in the streamer
       UpperCamelCase__				=    cs.out[:-1]

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
       # with actual models -- the dummy models' tokenizers are not aligned with their models, and
       # `skip_special_tokens=True` has no effect on them
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""distilgpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""distilgpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    torch.ones((1, 5)	,      device=SCREAMING_SNAKE_CASE_						).long() * model.config.bos_token_id
       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_	,      skip_special_tokens=SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=1	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)

       # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
       # re-tokenized, must only contain one token
       UpperCamelCase__				=    cs.out[:-1]  # Remove the final "\n"
       UpperCamelCase__				=    tokenizer(SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						)
       self.assertEqual(streamer_text_tokenized.input_ids.shape	,      (1, 1)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    TextIteratorStreamer(SCREAMING_SNAKE_CASE_	,      timeout=0.001						)
       UpperCamelCase__				=    {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
       UpperCamelCase__				=    Thread(target=model.generate	,      kwargs=SCREAMING_SNAKE_CASE_						)
       thread.start()

       # The streamer will timeout after 0.001 seconds, so an exception will be raised
       with self.assertRaises(SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    """"""
            for new_text in streamer:
                 streamer_text += new_text





