


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__lowerCamelCase        =     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__lowerCamelCase        =     '''    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
'''


class   a__	(						unittest.TestCase    ):

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Optional[Any]										=    tempfile.mkdtemp()
            os.makedirs(os.path.join(self.transformer_dir      ,							"""models/bert/"""    )    )
            a_    :     Optional[Any]										=    self.transformer_dir
            shutil.copy(
                os.path.join(lowerCamelCase_      ,							"""src/transformers/models/bert/modeling_bert.py"""    )      ,							os.path.join(self.transformer_dir      ,							"""models/bert/modeling_bert.py"""    )      ,							)

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Any										=    """src/transformers"""
            shutil.rmtree(self.transformer_dir    )

       def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Tuple=None    ):
            a_    :     Any										=    comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
            if overwrite_result is not None:
                 a_    :     Any										=    comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
            a_    :     Dict										=    black.Mode(target_versions={black.TargetVersion.PYaa}      ,							line_length=1_1_9    )
            a_    :     Optional[Any]										=    black.format_str(lowerCamelCase_      ,							mode=lowerCamelCase_    )
            a_    :     Optional[Any]										=    os.path.join(self.transformer_dir      ,							"""new_code.py"""    )
            with open(lowerCamelCase_      ,							"""w"""      ,							newline="""\n"""    ) as f:
                 f.write(lowerCamelCase_    )
            if overwrite_result is None:
                 self.assertTrue(len(check_copies.is_copy_consistent(lowerCamelCase_    )    ) == 0    )
            else:
                 check_copies.is_copy_consistent(f.name      ,							overwrite=lowerCamelCase_    )
                 with open(lowerCamelCase_      ,							"""r"""    ) as f:
                      self.assertTrue(f.read()      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Tuple										=    check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""    )
            self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            # Base copy consistency
            self.check_copy_consistency(
                """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"""      ,							"""BertLMPredictionHead"""      ,							REFERENCE_CODE + """\n"""      ,							)

            # With no empty line at the end
            self.check_copy_consistency(
                """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"""      ,							"""BertLMPredictionHead"""      ,							lowerCamelCase_      ,							)

            # Copy consistency with rename
            self.check_copy_consistency(
                """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"""      ,							"""TestModelLMPredictionHead"""      ,							re.sub("""Bert"""      ,							"""TestModel"""      ,							lowerCamelCase_    )      ,							)

            # Copy consistency with a really long name
            a_    :     int										=    """TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
            self.check_copy_consistency(
                F'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''      ,							F'''{long_class_name}LMPredictionHead'''      ,							re.sub("""Bert"""      ,							lowerCamelCase_      ,							lowerCamelCase_    )      ,							)

            # Copy consistency with overwrite
            self.check_copy_consistency(
                """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"""      ,							"""TestModelLMPredictionHead"""      ,							lowerCamelCase_      ,							overwrite_result=re.sub("""Bert"""      ,							"""TestModel"""      ,							lowerCamelCase_    )      ,							)

       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     Optional[Any]										=    check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

            a_    :     Tuple										=    (
                """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"""
                """ Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"""
                """ Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"""
                """ Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."""
                """ **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"""
                """ released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"""
                """ lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"""
                """ method has been applied to compress GPT2 into"""
                """ [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"""
                """ [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"""
                """ Multilingual BERT into"""
                """ [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"""
                """ version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"""
                """ (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"""
                """ as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"""
                """ Luong, Quoc V. Le, Christopher D. Manning."""
            )
            a_    :     Optional[Any]										=    (
                """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
                """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
                """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
                """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
            )
            a_    :     Any										=    (
                """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
                """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
                """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
                """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."""
                """ **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"""
                """ [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"""
                """ lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"""
                """ method has been applied to compress GPT2 into"""
                """ [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"""
                """ [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"""
                """ Multilingual BERT into"""
                """ [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"""
                """ version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"""
                """ Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"""
                """ than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"""
                """ Christopher D. Manning 发布。\n"""
            )

            a_			,			a_    :     Optional[int]										=    check_copies.convert_to_localized_md(
                lowerCamelCase_      ,							lowerCamelCase_      ,							localized_readme["""format_model_list"""]    )

            self.assertFalse(lowerCamelCase_    )
            self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )

            a_			,			a_    :     Tuple										=    check_copies.convert_to_localized_md(
                lowerCamelCase_      ,							lowerCamelCase_      ,							localized_readme["""format_model_list"""]    )

            # Check whether the number of models is equal to README.md after conversion.
            self.assertTrue(lowerCamelCase_    )

            a_    :     Optional[int]										=    (
                """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"""
                """ Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"""
                """ Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"""
                """ Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."""
            )
            a_    :     Union[str, Any]										=    (
                """1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"""
                """ the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
                """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
                """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
            )
            a_    :     str										=    (
                """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
                """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
                """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
                """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
            )

            a_			,			a_    :     Tuple										=    check_copies.convert_to_localized_md(
                lowerCamelCase_      ,							lowerCamelCase_      ,							localized_readme["""format_model_list"""]    )

            # Check if the model link is synchronized.
            self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )







import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     torch.FloatTensor
       lowerCamelCase__:     Optional[torch.FloatTensor]							    =     None



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase=0.9_99  ,  __UpperCamelCase="cosine"  ,  ):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(__UpperCamelCase ):
               return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(__UpperCamelCase ):
               return math.exp(t * -12.0 )

     else:
          raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )

     a_    :     List[Any]										=    []
     for i in range(__UpperCamelCase ):
          a_    :     str										=    i / num_diffusion_timesteps
          a_    :     str										=    (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(__UpperCamelCase ) / alpha_bar_fn(__UpperCamelCase )  ,  __UpperCamelCase ) )
     return torch.tensor(__UpperCamelCase  ,  dtype=torch.floataa )


class   a__	(						lowerCAmelCase_						,       lowerCAmelCase_    ):

       @register_to_config
       def __init__(   self      :     str      ,							lowerCamelCase_      :     int = 1_0_0_0      ,							lowerCamelCase_      :     str = "fixed_small_log"      ,							lowerCamelCase_      :     bool = True      ,							lowerCamelCase_      :     Optional[float] = 1.0      ,							lowerCamelCase_      :     str = "epsilon"      ,							lowerCamelCase_      :     str = "squaredcos_cap_v2"      ,							):
            if beta_schedule != "squaredcos_cap_v2":
                 raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""    )

            a_    :     Dict										=    betas_for_alpha_bar(lowerCamelCase_    )

            a_    :     List[Any]										=    1.0 - self.betas
            a_    :     List[str]										=    torch.cumprod(self.alphas      ,							dim=0    )
            a_    :     List[Any]										=    torch.tensor(1.0    )

            # standard deviation of the initial noise distribution
            a_    :     int										=    1.0

            # setable values
            a_    :     List[str]										=    None
            a_    :     Optional[int]										=    torch.from_numpy(np.arange(0      ,							lowerCamelCase_    )[::-1].copy()    )

            a_    :     List[Any]										=    variance_type

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     Optional[int] = None    ):
            return sample

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Union[str, torch.device] = None    ):
            a_    :     Tuple										=    num_inference_steps
            a_    :     Tuple										=    (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
            a_    :     Tuple										=    (np.arange(0      ,							lowerCamelCase_    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
            a_    :     Dict										=    torch.from_numpy(lowerCamelCase_    ).to(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     List[Any]=None      ,							lowerCamelCase_      :     Tuple=None      ,							lowerCamelCase_      :     str=None    ):
            if prev_timestep is None:
                 a_    :     List[Any]										=    t - 1

            a_    :     Any										=    self.alphas_cumprod[t]
            a_    :     Union[str, Any]										=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
            a_    :     List[Any]										=    1 - alpha_prod_t
            a_    :     int										=    1 - alpha_prod_t_prev

            if prev_timestep == t - 1:
                 a_    :     int										=    self.betas[t]
            else:
                 a_    :     Optional[int]										=    1 - alpha_prod_t / alpha_prod_t_prev

            # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
            # and sample from it to get previous sample
            # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
            a_    :     int										=    beta_prod_t_prev / beta_prod_t * beta

            if variance_type is None:
                 a_    :     Optional[Any]										=    self.config.variance_type

            # hacks - were probably added for training stability
            if variance_type == "fixed_small_log":
                 a_    :     Dict										=    torch.log(torch.clamp(lowerCamelCase_      ,							min=1E-20    )    )
                 a_    :     List[Any]										=    torch.exp(0.5 * variance    )
            elif variance_type == "learned_range":
                 # NOTE difference with DDPM scheduler
                 a_    :     Dict										=    variance.log()
                 a_    :     Optional[Any]										=    beta.log()

                 a_    :     str										=    (predicted_variance + 1) / 2
                 a_    :     Optional[int]										=    frac * max_log + (1 - frac) * min_log

            return variance

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     Optional[int] = None      ,							lowerCamelCase_      :     List[str]=None      ,							lowerCamelCase_      :     bool = True      ,							):
            a_    :     List[str]										=    timestep

            if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                 a_			,			a_    :     List[str]										=    torch.split(lowerCamelCase_      ,							sample.shape[1]      ,							dim=1    )
            else:
                 a_    :     Any										=    None

            # 1. compute alphas, betas
            if prev_timestep is None:
                 a_    :     Optional[Any]										=    t - 1

            a_    :     Tuple										=    self.alphas_cumprod[t]
            a_    :     Any										=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
            a_    :     int										=    1 - alpha_prod_t
            a_    :     int										=    1 - alpha_prod_t_prev

            if prev_timestep == t - 1:
                 a_    :     Optional[int]										=    self.betas[t]
                 a_    :     int										=    self.alphas[t]
            else:
                 a_    :     int										=    1 - alpha_prod_t / alpha_prod_t_prev
                 a_    :     Dict										=    1 - beta

            # 2. compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
            if self.config.prediction_type == "epsilon":
                 a_    :     Optional[int]										=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            elif self.config.prediction_type == "sample":
                 a_    :     Optional[Any]										=    model_output
            else:
                 raise ValueError(
                     F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
                     """ for the UnCLIPScheduler."""    )

            # 3. Clip "predicted x_0"
            if self.config.clip_sample:
                 a_    :     Dict										=    torch.clamp(
                     lowerCamelCase_      ,							-self.config.clip_sample_range      ,							self.config.clip_sample_range    )

            # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            a_    :     Dict										=    (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
            a_    :     Any										=    alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

            # 5. Compute predicted previous sample µ_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            a_    :     Optional[Any]										=    pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

            # 6. Add noise
            a_    :     Optional[int]										=    0
            if t > 0:
                 a_    :     Any										=    randn_tensor(
                     model_output.shape      ,							dtype=model_output.dtype      ,							generator=lowerCamelCase_      ,							device=model_output.device    )

                 a_    :     Tuple										=    self._get_variance(
                     lowerCamelCase_      ,							predicted_variance=lowerCamelCase_      ,							prev_timestep=lowerCamelCase_      ,							)

                 if self.variance_type == "fixed_small_log":
                      a_    :     str										=    variance
                 elif self.variance_type == "learned_range":
                      a_    :     Optional[Any]										=    (0.5 * variance).exp()
                 else:
                      raise ValueError(
                          F'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
                          """ for the UnCLIPScheduler."""    )

                 a_    :     List[str]										=    variance * variance_noise

            a_    :     List[Any]										=    pred_prev_sample + variance

            if not return_dict:
                 return (pred_prev_sample,)

            return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase_      ,							pred_original_sample=lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     torch.IntTensor      ,							):
            # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
            a_    :     Tuple										=    self.alphas_cumprod.to(device=original_samples.device      ,							dtype=original_samples.dtype    )
            a_    :     Union[str, Any]										=    timesteps.to(original_samples.device    )

            a_    :     List[str]										=    alphas_cumprod[timesteps] ** 0.5
            a_    :     Tuple										=    sqrt_alpha_prod.flatten()
            while len(sqrt_alpha_prod.shape    ) < len(original_samples.shape    ):
                 a_    :     Dict										=    sqrt_alpha_prod.unsqueeze(-1    )

            a_    :     int										=    (1 - alphas_cumprod[timesteps]) ** 0.5
            a_    :     Any										=    sqrt_one_minus_alpha_prod.flatten()
            while len(sqrt_one_minus_alpha_prod.shape    ) < len(original_samples.shape    ):
                 a_    :     Tuple										=    sqrt_one_minus_alpha_prod.unsqueeze(-1    )

            a_    :     List[str]										=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
            return noisy_samples



