

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 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				):
  _a		     =    [R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]




  @register_to_config
  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = None				,  lowerCAmelCase = 50_257				,  lowerCAmelCase = 1_024				,  lowerCAmelCase = 768				,  lowerCAmelCase = 12				,  lowerCAmelCase = 12				,  lowerCAmelCase = None				,  lowerCAmelCase = "gelu_new"				,  lowerCAmelCase = 0.1				,  lowerCAmelCase = 0.1				,  lowerCAmelCase = 0.1				,  lowerCAmelCase = 1e-5				,  lowerCAmelCase = 0.02				,  lowerCAmelCase = True				,  lowerCAmelCase = True				,  lowerCAmelCase = False				,  lowerCAmelCase = False				,  )    ->					Optional[Any]:
        '''simple docstring'''






        super().__init__()

        _lowercase      			=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.'''   )

        _lowercase      			=prefix_inner_dim
        _lowercase      			=prefix_hidden_dim

        _lowercase      			=(
            nn.Linear(self.prefix_inner_dim				,  self.prefix_hidden_dim   )
            if self.prefix_hidden_dim is not None
            else nn.Identity()
        )
        _lowercase      			=(
            nn.Linear(self.prefix_hidden_dim				,  lowerCAmelCase   ) if self.prefix_hidden_dim is not None else nn.Identity()
        )

        _lowercase      			=GPTaConfig(
            vocab_size=lowerCAmelCase				,  n_positions=lowerCAmelCase				,  n_embd=lowerCAmelCase				,  n_layer=lowerCAmelCase				,  n_head=lowerCAmelCase				,  n_inner=lowerCAmelCase				,  activation_function=lowerCAmelCase				,  resid_pdrop=lowerCAmelCase				,  embd_pdrop=lowerCAmelCase				,  attn_pdrop=lowerCAmelCase				,  layer_norm_epsilon=lowerCAmelCase				,  initializer_range=lowerCAmelCase				,  scale_attn_weights=lowerCAmelCase				,  use_cache=lowerCAmelCase				,  scale_attn_by_inverse_layer_idx=lowerCAmelCase				,  reorder_and_upcast_attn=lowerCAmelCase				,  )
        _lowercase      			=GPTaLMHeadModel(lowerCAmelCase   )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=self.transformer.transformer.wte(lowerCAmelCase   )
        _lowercase      			=self.encode_prefix(lowerCAmelCase   )
        _lowercase      			=self.decode_prefix(lowerCAmelCase   )
        _lowercase      			=torch.cat((prefix_embeds, embedding_text)				,  dim=1   )

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




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					torch.Tensor:
        '''simple docstring'''






        return torch.zeros(lowerCAmelCase				,  self.prefix_length				,  dtype=torch.intaa				,  device=lowerCAmelCase   )




  def     A__	(							self				,  lowerCAmelCase   )    ->					List[Any]:
        '''simple docstring'''






        return self.encode_prefix(lowerCAmelCase   )




  @torch.no_grad()
  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					str:
        '''simple docstring'''






        _lowercase      			=torch.split(lowerCAmelCase				,  1				,  dim=0   )
        _lowercase      			=[]
        _lowercase      			=[]
        for feature in features:
              _lowercase      			=self.decode_prefix(feature.to(lowerCAmelCase   )   )  # back to the clip feature
              # Only support beam search for now
              _lowercase			,			_lowercase      			=self.generate_beam(
                  input_embeds=lowerCAmelCase				,  device=lowerCAmelCase				,  eos_token_id=lowerCAmelCase   )
              generated_tokens.append(output_tokens[0]   )
              generated_seq_lengths.append(seq_lengths[0]   )
        _lowercase      			=torch.stack(lowerCAmelCase   )
        _lowercase      			=torch.stack(lowerCAmelCase   )
        return generated_tokens, generated_seq_lengths




  @torch.no_grad()
  def     A__	(							self				,  lowerCAmelCase=None				,  lowerCAmelCase=None				,  lowerCAmelCase=None				,  lowerCAmelCase = 5				,  lowerCAmelCase = 67				,  lowerCAmelCase = 1.0				,  lowerCAmelCase = None				,  )    ->					str:
        '''simple docstring'''






        _lowercase      			=eos_token_id
        _lowercase      			=None
        _lowercase      			=None
        _lowercase      			=torch.ones(lowerCAmelCase				,  device=lowerCAmelCase				,  dtype=torch.int   )
        _lowercase      			=torch.zeros(lowerCAmelCase				,  device=lowerCAmelCase				,  dtype=torch.bool   )

        if input_embeds is not None:
              _lowercase      			=input_embeds
        else:
              _lowercase      			=self.transformer.transformer.wte(lowerCAmelCase   )

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

              if scores is None:
                    _lowercase			,			_lowercase      			=logits.topk(lowerCAmelCase				,  -1   )
                    _lowercase      			=generated.expand(lowerCAmelCase				,  *generated.shape[1:]   )
                    _lowercase			,			_lowercase      			=next_tokens.permute(1				,  0   ), scores.squeeze(0   )
                    if tokens is None:
                          _lowercase      			=next_tokens
                    else:
                          _lowercase      			=tokens.expand(lowerCAmelCase				,  *tokens.shape[1:]   )
                          _lowercase      			=torch.cat((tokens, next_tokens)				,  dim=1   )
              else:
                    _lowercase      			=-float(np.inf   )
                    _lowercase      			=0
                    _lowercase      			=scores[:, None] + logits
                    seq_lengths[~is_stopped] += 1
                    _lowercase      			=scores_sum / seq_lengths[:, None]
                    _lowercase			,			_lowercase      			=scores_sum_average.view(-1   ).topk(lowerCAmelCase				,  -1   )
                    _lowercase      			=next_tokens // scores_sum.shape[1]
                    _lowercase      			=seq_lengths[next_tokens_source]
                    _lowercase      			=next_tokens % scores_sum.shape[1]
                    _lowercase      			=next_tokens.unsqueeze(1   )
                    _lowercase      			=tokens[next_tokens_source]
                    _lowercase      			=torch.cat((tokens, next_tokens)				,  dim=1   )
                    _lowercase      			=generated[next_tokens_source]
                    _lowercase      			=scores_sum_average * seq_lengths
                    _lowercase      			=is_stopped[next_tokens_source]

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

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





# Copyright 2021 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.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


lowercase_   							=  'pytorch_model.bin'
lowercase_   							=  'pytorch_model.bin.index.json'
lowercase_   							=  'adapter_config.json'
lowercase_   							=  'adapter_model.bin'
lowercase_   							=  'adapter_model.safetensors'
lowercase_   							=  'tf_model.h5'
lowercase_   							=  'tf_model.h5.index.json'
lowercase_   							=  'model.ckpt'
lowercase_   							=  'flax_model.msgpack'
lowercase_   							=  'flax_model.msgpack.index.json'
lowercase_   							=  'model.safetensors'
lowercase_   							=  'model.safetensors.index.json'
lowercase_   							=  'config.json'
lowercase_   							=  'preprocessor_config.json'
lowercase_   							=  FEATURE_EXTRACTOR_NAME
lowercase_   							=  'generation_config.json'
lowercase_   							=  'modelcard.json'

lowercase_   							=  '▁'
lowercase_   							=  SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

lowercase_   							=  [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
lowercase_   							=  [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
lowercase_   							=  [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def 	a (     A__      :  List[str]	)       -> str:





      """simple docstring"""



      if version.parse(A__	) < version.parse(A__	):
            if "dev" in min_version:
                  _lowercase      			=(
                      'This example requires a source install from HuggingFace Transformers (see '
                      '`https://huggingface.co/docs/transformers/installation#install-from-source`),'
                  )
            else:
                  _lowercase      			=F'''This example requires a minimum version of {min_version},'''
            error_message += F''' but the version found is {__version__}.\n'''
            raise ImportError(
                error_message
                + 'Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '
                'versions of HuggingFace Transformers.'	)


