# What does this PR do?
This PR adds the possibility to perform GTPQ quantization with transformers model using [optimum](https://github.com/huggingface/optimum)  library. The backend relies on [auto_gptq](https://github.com/PanQiWei/AutoGPTQ) library where we use `GTPQ` and `QuantLinear` class. 
Here's the related [PR](https://github.com/huggingface/optimum/pull/1216) on optimum side. This PR can only be merged after the optimum one. 

### Quantize model 
Unlike `bitsandbytes`, it is not feasible to quantize the weights right after loading them in order to reduce memory consumption. This is why, we need to first load the entire model and then quantize it (all done in `from_pretrained`)
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
model_name = "facebook/opt-125m"

tokenizer = AutoTokenizer.from_pretrained(model_name)
config = GPTQConfig(bits=4, dataset = "c4", tokenizer=tokenizer,  group_size=128, desc_act=False)
# works also with device_map (cpu offload works but not disk offload)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, quantization_config=config)
```

### Save hf model 
We save the `quantization_config` in `model.config`
```py
# move to the device you want to save model (needed if you used device_map before)
model.to(device)
quantized_model_folder = 'opt-125m-quantized_hf'
model.save_pretrained.(save_folder)
```

### Load quantized weights
If the `model.config` has a `quantization_config` key, we will replace the layers of the model and load the quantized weights.
```py
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(quantized_model_folder,
                                                                  device_map = "auto")
```

TODO:
- [ ] merge optimum PR first
- [x] doc
GPTQ integration. # What does this PR do?
This PR adds the possibility to perform GTPQ quantization with transformers model using [optimum](https://github.com/huggingface/optimum)  library. The backend relies on [auto_gptq](https://github.com/PanQiWei/AutoGPTQ) library where we use `GTPQ` and `QuantLinear` class. 
Here's the related [PR](https://github.com/huggingface/optimum/pull/1216) on optimum side. This PR can only be merged after the optimum one. 

### Quantize model 
Unlike `bitsandbytes`, it is not feasible to quantize the weights right after loading them in order to reduce memory consumption. This is why, we need to first load the entire model and then quantize it (all done in `from_pretrained`)
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
model_name = "facebook/opt-125m"

tokenizer = AutoTokenizer.from_pretrained(model_name)
config = GPTQConfig(bits=4, dataset = "c4", tokenizer=tokenizer,  group_size=128, desc_act=False)
# works also with device_map (cpu offload works but not disk offload)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, quantization_config=config)
```

### Save hf model 
We save the `quantization_config` in `model.config`
```py
# move to the device you want to save model (needed if you used device_map before)
model.to(device)
quantized_model_folder = 'opt-125m-quantized_hf'
model.save_pretrained.(save_folder)
```

### Load quantized weights
If the `model.config` has a `quantization_config` key, we will replace the layers of the model and load the quantized weights.
```py
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(quantized_model_folder,
                                                                  device_map = "auto")
```

TODO:
- [ ] merge optimum PR first
- [x] doc
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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.
"""
Speech processor class for Speech2Text
"""
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class Speech2TextProcessor(ProcessorMixin):
    r"""
    Constructs a Speech2Text processor which wraps a Speech2Text feature extractor and a Speech2Text tokenizer into a
    single processor.

    [`Speech2TextProcessor`] offers all the functionalities of [`Speech2TextFeatureExtractor`] and
    [`Speech2TextTokenizer`]. See the [`~Speech2TextProcessor.__call__`] and [`~Speech2TextProcessor.decode`] for more
    information.

    Args:
        feature_extractor (`Speech2TextFeatureExtractor`):
            An instance of [`Speech2TextFeatureExtractor`]. The feature extractor is a required input.
        tokenizer (`Speech2TextTokenizer`):
            An instance of [`Speech2TextTokenizer`]. The tokenizer is a required input.
    """
    feature_extractor_class = "Speech2TextFeatureExtractor"
    tokenizer_class = "Speech2TextTokenizer"

    def __init__(self, feature_extractor, tokenizer):
        super().__init__(feature_extractor, tokenizer)
        self.current_processor = self.feature_extractor
        self._in_target_context_manager = False

    def __call__(self, *args, **kwargs):
        """
        When used in normal mode, this method forwards all its arguments to Speech2TextFeatureExtractor's
        [`~Speech2TextFeatureExtractor.__call__`] and returns its output. If used in the context
        [`~Speech2TextProcessor.as_target_processor`] this method forwards all its arguments to Speech2TextTokenizer's
        [`~Speech2TextTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more
        information.
        """
        # For backward compatibility
        if self._in_target_context_manager:
            return self.current_processor(*args, **kwargs)

        if "raw_speech" in kwargs:
            warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.")
            audio = kwargs.pop("raw_speech")
        else:
            audio = kwargs.pop("audio", None)
        sampling_rate = kwargs.pop("sampling_rate", None)
        text = kwargs.pop("text", None)
        if len(args) > 0:
            audio = args[0]
            args = args[1:]

        if audio is None and text is None:
            raise ValueError("You need to specify either an `audio` or `text` input to process.")

        if audio is not None:
            inputs = self.feature_extractor(audio, *args, sampling_rate=sampling_rate, **kwargs)
        if text is not None:
            encodings = self.tokenizer(text, **kwargs)

        if text is None:
            return inputs
        elif audio is None:
            return encodings
        else:
            inputs["labels"] = encodings["input_ids"]
            return inputs

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Speech2TextTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Speech2TextTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
        to the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    @contextmanager
    def as_target_processor(self):
        """
        Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning
        Speech2Text.
        """
        warnings.warn(
            "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
            "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
            "your audio inputs, or in a separate call."
        )
        self._in_target_context_manager = True
        self.current_processor = self.tokenizer
        yield
        self.current_processor = self.feature_extractor
        self._in_target_context_manager = False

# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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.
"""
Speech processor class for Speech2Text
"""
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class Speech2TextProcessor(ProcessorMixin):
    r"""
    Constructs a Speech2Text processor which wraps a Speech2Text feature extractor and a Speech2Text tokenizer into a
    single processor.

    [`Speech2TextProcessor`] offers all the functionalities of [`Speech2TextFeatureExtractor`] and
    [`Speech2TextTokenizer`]. See the [`~Speech2TextProcessor.__call__`] and [`~Speech2TextProcessor.decode`] for more
    information.

    Args:
        feature_extractor (`Speech2TextFeatureExtractor`):
            An instance of [`Speech2TextFeatureExtractor`]. The feature extractor is a required input.
        tokenizer (`Speech2TextTokenizer`):
            An instance of [`Speech2TextTokenizer`]. The tokenizer is a required input.
    """
    feature_extractor_class = "Speech2TextFeatureExtractor"
    tokenizer_class = "Speech2TextTokenizer"

    def __init__(self, feature_extractor, tokenizer):
        super().__init__(feature_extractor, tokenizer)
        self.current_processor = self.feature_extractor
        self._in_target_context_manager = False

    def __call__(self, *args, **kwargs):
        """
        When used in normal mode, this method forwards all its arguments to Speech2TextFeatureExtractor's
        [`~Speech2TextFeatureExtractor.__call__`] and returns its output. If used in the context
        [`~Speech2TextProcessor.as_target_processor`] this method forwards all its arguments to Speech2TextTokenizer's
        [`~Speech2TextTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more
        information.
        """
        # For backward compatibility
        if self._in_target_context_manager:
            return self.current_processor(*args, **kwargs)

        if "raw_speech" in kwargs:
            warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.")
            audio = kwargs.pop("raw_speech")
        else:
            audio = kwargs.pop("audio", None)
        sampling_rate = kwargs.pop("sampling_rate", None)
        text = kwargs.pop("text", None)
        if len(args) > 0:
            audio = args[0]
            args = args[1:]

        if audio is None and text is None:
            raise ValueError("You need to specify either an `audio` or `text` input to process.")

        if audio is not None:
            inputs = self.feature_extractor(audio, *args, sampling_rate=sampling_rate, **kwargs)
        if text is not None:
            encodings = self.tokenizer(text, **kwargs)

        if text is None:
            return inputs
        elif audio is None:
            return encodings
        else:
            inputs["labels"] = encodings["input_ids"]
            return inputs

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Speech2TextTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Speech2TextTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
        to the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    @contextmanager
    def as_target_processor(self):
        """
        Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning
        Speech2Text.
        """
        warnings.warn(
            "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
            "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
            "your audio inputs, or in a separate call."
        )
        self._in_target_context_manager = True
        self.current_processor = self.tokenizer
        yield
        self.current_processor = self.feature_extractor
        self._in_target_context_manager = False
