




import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
 from cookiecutter.main import cookiecutter

 _lowerCamelCase      			=						True
except ImportError:
 _lowerCamelCase      			=						False

_lowerCamelCase      			=						logging.get_logger(__name__)  # pylint: disable=invalid-name




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Namespace				)  ->							List[str]:
    return AddNewModelCommand(args.testing	,					args.testing_file	,					path=args.path				)



class       a					(       _A      ):

     '''simple docstring'''





     @staticmethod
     def   lowerCamelCase_       (     __snake_case       :      ArgumentParser    ):
         UpperCAmelCase_										=					parser.add_parser('''add-new-model'''    )
         add_new_model_parser.add_argument('''--testing''' ,   action='''store_true''' ,   help='''If in testing mode.'''    )
         add_new_model_parser.add_argument('''--testing_file''' ,   type=__snake_case ,   help='''Configuration file on which to run.'''    )
         add_new_model_parser.add_argument(
             '''--path''' ,   type=__snake_case ,   help='''Path to cookiecutter. Should only be used for testing purposes.'''    )
         add_new_model_parser.set_defaults(func=__snake_case    )
     def __init__(     self       :      int ,   __snake_case       :      bool ,   __snake_case       :      str ,   __snake_case       :      Union[str, Any]=None ,   *__snake_case       :      Optional[Any]    ):
         UpperCAmelCase_										=					testing
         UpperCAmelCase_										=					testing_file
         UpperCAmelCase_										=					path




     def   lowerCamelCase_       (     self       :      Any    ):
         warnings.warn(
             '''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '''
             '''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '''
             '''checks, you should use `transformers-cli add-new-model-like` instead.'''    )
         if not _has_cookiecutter:
             raise ImportError(
                 '''Model creation dependencies are required to use the `add_new_model` command. Install them by running '''
                 '''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n'''    )
         # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
         UpperCAmelCase_										=					[directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:22]]
         if len(__snake_case    ) > 0:
             raise ValueError(
                 '''Several directories starting with `cookiecutter-template-` in current working directory. '''
                 '''Please clean your directory by removing all folders starting with `cookiecutter-template-` or '''
                 '''change your working directory.'''    )

         UpperCAmelCase_										=					(
             Path(__snake_case    ).parent.parent.parent.parent if self._path is None else Path(self._path    ).parent.parent
         )
         UpperCAmelCase_										=					path_to_transformer_root / '''templates''' / '''adding_a_new_model'''

         # Execute cookiecutter
         if not self._testing:
             cookiecutter(str(__snake_case    )    )
         else:
             with open(self._testing_file ,   '''r'''    ) as configuration_file:
                 UpperCAmelCase_										=					json.load(__snake_case    )

             cookiecutter(
                 str(path_to_cookiecutter if self._path is None else self._path    ) ,   no_input=__snake_case ,   extra_context=__snake_case ,   )

         UpperCAmelCase_										=					[directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:22]][0]

         # Retrieve configuration
         with open(directory + '''/configuration.json''' ,   '''r'''    ) as configuration_file:
             UpperCAmelCase_										=					json.load(__snake_case    )

         UpperCAmelCase_										=					configuration['''lowercase_modelname''']
         UpperCAmelCase_										=					configuration['''generate_tensorflow_pytorch_and_flax''']
         os.remove(F'{directory}/configuration.json'    )

         UpperCAmelCase_										=					'''PyTorch''' in generate_tensorflow_pytorch_and_flax
         UpperCAmelCase_										=					'''TensorFlow''' in generate_tensorflow_pytorch_and_flax
         UpperCAmelCase_										=					'''Flax''' in generate_tensorflow_pytorch_and_flax

         UpperCAmelCase_										=					F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
         os.makedirs(__snake_case ,   exist_ok=__snake_case    )
         os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}' ,   exist_ok=__snake_case    )

         # Tests require submodules as they have parent imports
         with open(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py' ,   '''w'''    ):
             pass

         shutil.move(
             F'{directory}/__init__.py' ,   F'{model_dir}/__init__.py' ,   )
         shutil.move(
             F'{directory}/configuration_{lowercase_model_name}.py' ,   F'{model_dir}/configuration_{lowercase_model_name}.py' ,   )

         def remove_copy_lines(__snake_case       :      Optional[Any]    ):
             with open(__snake_case ,   '''r'''    ) as f:
                 UpperCAmelCase_										=					f.readlines()
             with open(__snake_case ,   '''w'''    ) as f:
                 for line in lines:
                     if "# Copied from transformers." not in line:
                         f.write(__snake_case    )

         if output_pytorch:
             if not self._testing:
                 remove_copy_lines(F'{directory}/modeling_{lowercase_model_name}.py'    )

             shutil.move(
                 F'{directory}/modeling_{lowercase_model_name}.py' ,   F'{model_dir}/modeling_{lowercase_model_name}.py' ,   )

             shutil.move(
                 F'{directory}/test_modeling_{lowercase_model_name}.py' ,   F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py' ,   )
         else:
             os.remove(F'{directory}/modeling_{lowercase_model_name}.py'    )
             os.remove(F'{directory}/test_modeling_{lowercase_model_name}.py'    )

         if output_tensorflow:
             if not self._testing:
                 remove_copy_lines(F'{directory}/modeling_tf_{lowercase_model_name}.py'    )

             shutil.move(
                 F'{directory}/modeling_tf_{lowercase_model_name}.py' ,   F'{model_dir}/modeling_tf_{lowercase_model_name}.py' ,   )

             shutil.move(
                 F'{directory}/test_modeling_tf_{lowercase_model_name}.py' ,   F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py' ,   )
         else:
             os.remove(F'{directory}/modeling_tf_{lowercase_model_name}.py'    )
             os.remove(F'{directory}/test_modeling_tf_{lowercase_model_name}.py'    )

         if output_flax:
             if not self._testing:
                 remove_copy_lines(F'{directory}/modeling_flax_{lowercase_model_name}.py'    )

             shutil.move(
                 F'{directory}/modeling_flax_{lowercase_model_name}.py' ,   F'{model_dir}/modeling_flax_{lowercase_model_name}.py' ,   )

             shutil.move(
                 F'{directory}/test_modeling_flax_{lowercase_model_name}.py' ,   F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py' ,   )
         else:
             os.remove(F'{directory}/modeling_flax_{lowercase_model_name}.py'    )
             os.remove(F'{directory}/test_modeling_flax_{lowercase_model_name}.py'    )

         shutil.move(
             F'{directory}/{lowercase_model_name}.md' ,   F'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md' ,   )

         shutil.move(
             F'{directory}/tokenization_{lowercase_model_name}.py' ,   F'{model_dir}/tokenization_{lowercase_model_name}.py' ,   )

         shutil.move(
             F'{directory}/tokenization_fast_{lowercase_model_name}.py' ,   F'{model_dir}/tokenization_{lowercase_model_name}_fast.py' ,   )

         from os import fdopen, remove
         from shutil import copymode, move
         from tempfile import mkstemp

         def replace(__snake_case       :      str ,   __snake_case       :      str ,   __snake_case       :      List[str]    ):
             # Create temp file
             UpperCAmelCase_			,							UpperCAmelCase_										=					mkstemp()
             UpperCAmelCase_										=					False
             with fdopen(__snake_case ,   '''w'''    ) as new_file:
                 with open(__snake_case    ) as old_file:
                     for line in old_file:
                         new_file.write(__snake_case    )
                         if line_to_copy_below in line:
                             UpperCAmelCase_										=					True
                             for line_to_copy in lines_to_copy:
                                 new_file.write(__snake_case    )

             if not line_found:
                 raise ValueError(F'Line {line_to_copy_below} was not found in file.'    )

             # Copy the file permissions from the old file to the new file
             copymode(__snake_case ,   __snake_case    )
             # Remove original file
             remove(__snake_case    )
             # Move new file
             move(__snake_case ,   __snake_case    )

         def skip_units(__snake_case       :      List[str]    ):
             return (
                 ("generating PyTorch" in line and not output_pytorch)
                 or ("generating TensorFlow" in line and not output_tensorflow)
                 or ("generating Flax" in line and not output_flax)
             )

         def replace_in_files(__snake_case       :      Dict    ):
             with open(__snake_case    ) as datafile:
                 UpperCAmelCase_										=					[]
                 UpperCAmelCase_										=					False
                 UpperCAmelCase_										=					False
                 for line in datafile:
                     if "# To replace in: " in line and "##" not in line:
                         UpperCAmelCase_										=					line.split('''"'''    )[1]
                         UpperCAmelCase_										=					skip_units(__snake_case    )
                     elif "# Below: " in line and "##" not in line:
                         UpperCAmelCase_										=					line.split('''"'''    )[1]
                         UpperCAmelCase_										=					skip_units(__snake_case    )
                     elif "# End." in line and "##" not in line:
                         if not skip_file and not skip_snippet:
                             replace(__snake_case ,   __snake_case ,   __snake_case    )

                         UpperCAmelCase_										=					[]
                     elif "# Replace with" in line and "##" not in line:
                         UpperCAmelCase_										=					[]
                     elif "##" not in line:
                         lines_to_copy.append(__snake_case    )

             remove(__snake_case    )

         replace_in_files(F'{directory}/to_replace_{lowercase_model_name}.py'    )
         os.rmdir(__snake_case    )











import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_lowerCamelCase      			=						False



class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def   lowerCamelCase_       (     self       :      str    ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
     @property
     def   lowerCamelCase_       (     self       :      Any    ):
         return 12
     @property
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         return 12
     @property
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         return 32
     @property
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         torch.manual_seed(0    )
         UpperCAmelCase_										=					VQModel(
             block_out_channels=[32, 64] ,   in_channels=3 ,   out_channels=3 ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,   latent_channels=3 ,   num_vq_embeddings=self.num_embed ,   vq_embed_dim=3 ,   )
         return model
     @property
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )
         return tokenizer
     @property
     def   lowerCamelCase_       (     self       :      int    ):
         torch.manual_seed(0    )
         UpperCAmelCase_										=					CLIPTextConfig(
             bos_token_id=0 ,   eos_token_id=2 ,   hidden_size=self.text_embedder_hidden_size ,   intermediate_size=37 ,   layer_norm_eps=1E-05 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   pad_token_id=1 ,   vocab_size=10_00 ,   )
         return CLIPTextModel(__snake_case    )
     @property
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         torch.manual_seed(0    )

         UpperCAmelCase_										=					12
         UpperCAmelCase_										=					12

         UpperCAmelCase_										=					{
             '''attention_bias''': True,
             '''cross_attention_dim''': 32,
             '''attention_head_dim''': height * width,
             '''num_attention_heads''': 1,
             '''num_vector_embeds''': self.num_embed,
             '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
             '''norm_num_groups''': 32,
             '''sample_size''': width,
             '''activation_fn''': '''geglu-approximate''',
         }

         UpperCAmelCase_										=					TransformeraDModel(**__snake_case    )
         return model
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					'''cpu'''

         UpperCAmelCase_										=					self.dummy_vqvae
         UpperCAmelCase_										=					self.dummy_text_encoder
         UpperCAmelCase_										=					self.dummy_tokenizer
         UpperCAmelCase_										=					self.dummy_transformer
         UpperCAmelCase_										=					VQDiffusionScheduler(self.num_embed    )
         UpperCAmelCase_										=					LearnedClassifierFreeSamplingEmbeddings(learnable=__snake_case    )

         UpperCAmelCase_										=					VQDiffusionPipeline(
             vqvae=__snake_case ,   text_encoder=__snake_case ,   tokenizer=__snake_case ,   transformer=__snake_case ,   scheduler=__snake_case ,   learned_classifier_free_sampling_embeddings=__snake_case ,   )
         UpperCAmelCase_										=					pipe.to(__snake_case    )
         pipe.set_progress_bar_config(disable=__snake_case    )

         UpperCAmelCase_										=					'''teddy bear playing in the pool'''

         UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(0    )
         UpperCAmelCase_										=					pipe([prompt] ,   generator=__snake_case ,   num_inference_steps=2 ,   output_type='''np'''    )
         UpperCAmelCase_										=					output.images

         UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(0    )
         UpperCAmelCase_										=					pipe(
             [prompt] ,   generator=__snake_case ,   output_type='''np''' ,   return_dict=__snake_case ,   num_inference_steps=2    )[0]

         UpperCAmelCase_										=					image[0, -3:, -3:, -1]
         UpperCAmelCase_										=					image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         UpperCAmelCase_										=					np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2




     def   lowerCamelCase_       (     self       :      str    ):
         UpperCAmelCase_										=					'''cpu'''

         UpperCAmelCase_										=					self.dummy_vqvae
         UpperCAmelCase_										=					self.dummy_text_encoder
         UpperCAmelCase_										=					self.dummy_tokenizer
         UpperCAmelCase_										=					self.dummy_transformer
         UpperCAmelCase_										=					VQDiffusionScheduler(self.num_embed    )
         UpperCAmelCase_										=					LearnedClassifierFreeSamplingEmbeddings(
             learnable=__snake_case ,   hidden_size=self.text_embedder_hidden_size ,   length=tokenizer.model_max_length    )

         UpperCAmelCase_										=					VQDiffusionPipeline(
             vqvae=__snake_case ,   text_encoder=__snake_case ,   tokenizer=__snake_case ,   transformer=__snake_case ,   scheduler=__snake_case ,   learned_classifier_free_sampling_embeddings=__snake_case ,   )
         UpperCAmelCase_										=					pipe.to(__snake_case    )
         pipe.set_progress_bar_config(disable=__snake_case    )

         UpperCAmelCase_										=					'''teddy bear playing in the pool'''

         UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(0    )
         UpperCAmelCase_										=					pipe([prompt] ,   generator=__snake_case ,   num_inference_steps=2 ,   output_type='''np'''    )
         UpperCAmelCase_										=					output.images

         UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(0    )
         UpperCAmelCase_										=					pipe(
             [prompt] ,   generator=__snake_case ,   output_type='''np''' ,   return_dict=__snake_case ,   num_inference_steps=2    )[0]

         UpperCAmelCase_										=					image[0, -3:, -3:, -1]
         UpperCAmelCase_										=					image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         UpperCAmelCase_										=					np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 2.0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2



@slow
@require_torch_gpu
class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''    )

         UpperCAmelCase_										=					VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''    )
         UpperCAmelCase_										=					pipeline.to(__snake_case    )
         pipeline.set_progress_bar_config(disable=__snake_case    )

         # requires GPU generator for gumbel softmax
         # don't use GPU generator in tests though
         UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(0    )
         UpperCAmelCase_										=					pipeline(
             '''teddy bear playing in the pool''' ,   num_images_per_prompt=1 ,   generator=__snake_case ,   output_type='''np''' ,   )

         UpperCAmelCase_										=					output.images[0]

         assert image.shape == (2_56, 2_56, 3)
         assert np.abs(expected_image - image    ).max() < 2.0





