
'''simple docstring'''




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class  A				(							__snake_case    ,			__snake_case    ,			__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		StableDiffusionInpaintPipeline
     __magic_name__						=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     __magic_name__						=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __magic_name__						=		frozenset(
         []						)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     __magic_name__						=		frozenset([]						)





     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         torch.manual_seed(0	)
         A       :   List[Any]					=						UNetaDConditionModel(
             block_out_channels=(32, 64)						,			layers_per_block=2						,			sample_size=32						,			in_channels=9						,			out_channels=4						,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,			cross_attention_dim=32						,			attention_head_dim=(2, 4)						,			use_linear_projection=__SCREAMING_SNAKE_CASE						,			)
         A       :   List[str]					=						PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE	)
         torch.manual_seed(0	)
         A       :   Tuple					=						AutoencoderKL(
             block_out_channels=[32, 64]						,			in_channels=3						,			out_channels=3						,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,			latent_channels=4						,			sample_size=128						,			)
         torch.manual_seed(0	)
         A       :   List[str]					=						CLIPTextConfig(
             bos_token_id=0						,			eos_token_id=2						,			hidden_size=32						,			intermediate_size=37						,			layer_norm_eps=1e-05						,			num_attention_heads=4						,			num_hidden_layers=5						,			pad_token_id=1						,			vocab_size=1000						,			hidden_act='''gelu'''						,			projection_dim=512						,			)
         A       :   List[str]					=						CLIPTextModel(__SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

         A       :   Tuple					=						{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=0	) ->				int:






         """simple docstring"""






         A       :   int					=						floats_tensor((1, 3, 32, 32)						,			rng=random.Random(__SCREAMING_SNAKE_CASE	)	).to(__SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						image.cpu().permute(0						,			2						,			3						,			1	)[0]
         A       :   Dict					=						Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE	)	).convert('''RGB'''	).resize((64, 64)	)
         A       :   List[Any]					=						Image.fromarray(np.uinta(image + 4	)	).convert('''RGB'''	).resize((64, 64)	)
         if str(__SCREAMING_SNAKE_CASE	).startswith('''mps'''	):
             A       :   Tuple					=						torch.manual_seed(__SCREAMING_SNAKE_CASE	)
         else:
             A       :   Dict					=						torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(__SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': init_image,
             '''mask_image''': mask_image,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }
         return inputs





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         A       :   Dict					=						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         A       :   Optional[Any]					=						self.get_dummy_components()
         A       :   int					=						StableDiffusionInpaintPipeline(**__SCREAMING_SNAKE_CASE	)
         A       :   Any					=						sd_pipe.to(__SCREAMING_SNAKE_CASE	)
         sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

         A       :   Any					=						self.get_dummy_inputs(__SCREAMING_SNAKE_CASE	)
         A       :   Optional[Any]					=						sd_pipe(**__SCREAMING_SNAKE_CASE	).images
         A       :   Dict					=						image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         A       :   Tuple					=						np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]	)

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





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         super().test_inference_batch_single_identical(expected_max_diff=3e-3	)





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





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         A       :   Any					=						load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-inpaint/init_image.png'''	)
         A       :   Dict					=						load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''	)
         A       :   List[str]					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
             '''/yellow_cat_sitting_on_a_park_bench.npy'''	)

         A       :   List[str]					=						'''stabilityai/stable-diffusion-2-inpainting'''
         A       :   int					=						StableDiffusionInpaintPipeline.from_pretrained(__SCREAMING_SNAKE_CASE						,			safety_checker=__SCREAMING_SNAKE_CASE	)
         pipe.to(__SCREAMING_SNAKE_CASE	)
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)
         pipe.enable_attention_slicing()

         A       :   Union[str, Any]					=						'''Face of a yellow cat, high resolution, sitting on a park bench'''

         A       :   Optional[Any]					=						torch.manual_seed(0	)
         A       :   str					=						pipe(
             prompt=__SCREAMING_SNAKE_CASE						,			image=__SCREAMING_SNAKE_CASE						,			mask_image=__SCREAMING_SNAKE_CASE						,			generator=__SCREAMING_SNAKE_CASE						,			output_type='''np'''						,			)
         A       :   Optional[Any]					=						output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image	).max() < 9e-3





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   List[str]					=						load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-inpaint/init_image.png'''	)
         A       :   Dict					=						load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''	)
         A       :   int					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
             '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''	)

         A       :   Any					=						'''stabilityai/stable-diffusion-2-inpainting'''
         A       :   List[Any]					=						StableDiffusionInpaintPipeline.from_pretrained(
             __SCREAMING_SNAKE_CASE						,			torch_dtype=torch.floataa						,			safety_checker=__SCREAMING_SNAKE_CASE						,			)
         pipe.to(__SCREAMING_SNAKE_CASE	)
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)
         pipe.enable_attention_slicing()

         A       :   Any					=						'''Face of a yellow cat, high resolution, sitting on a park bench'''

         A       :   Any					=						torch.manual_seed(0	)
         A       :   Optional[int]					=						pipe(
             prompt=__SCREAMING_SNAKE_CASE						,			image=__SCREAMING_SNAKE_CASE						,			mask_image=__SCREAMING_SNAKE_CASE						,			generator=__SCREAMING_SNAKE_CASE						,			output_type='''np'''						,			)
         A       :   Optional[int]					=						output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image	).max() < 5e-1





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         A       :   str					=						load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-inpaint/init_image.png'''	)
         A       :   List[str]					=						load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''	)

         A       :   Dict					=						'''stabilityai/stable-diffusion-2-inpainting'''
         A       :   List[Any]					=						PNDMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE						,			subfolder='''scheduler'''	)
         A       :   Dict					=						StableDiffusionInpaintPipeline.from_pretrained(
             __SCREAMING_SNAKE_CASE						,			safety_checker=__SCREAMING_SNAKE_CASE						,			scheduler=__SCREAMING_SNAKE_CASE						,			torch_dtype=torch.floataa						,			)
         pipe.to(__SCREAMING_SNAKE_CASE	)
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)
         pipe.enable_attention_slicing(1	)
         pipe.enable_sequential_cpu_offload()

         A       :   Dict					=						'''Face of a yellow cat, high resolution, sitting on a park bench'''

         A       :   Optional[Any]					=						torch.manual_seed(0	)
         A       :   Optional[Any]					=						pipe(
             prompt=__SCREAMING_SNAKE_CASE						,			image=__SCREAMING_SNAKE_CASE						,			mask_image=__SCREAMING_SNAKE_CASE						,			generator=__SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			output_type='''np'''						,			)

         A       :   List[str]					=						torch.cuda.max_memory_allocated()
         # make sure that less than 2.65 GB is allocated
         assert mem_bytes < 2.65 * 10**9







'''simple docstring'''



import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
  import numpy as np
  import torch

  from . import (
      AlbertForPreTraining,
      BartForConditionalGeneration,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      CamembertForMaskedLM,
      CTRLLMHeadModel,
      DistilBertForMaskedLM,
      DistilBertForQuestionAnswering,
      DPRContextEncoder,
      DPRQuestionEncoder,
      DPRReader,
      ElectraForPreTraining,
      FlaubertWithLMHeadModel,
      GPTaLMHeadModel,
      LayoutLMForMaskedLM,
      LxmertForPreTraining,
      LxmertVisualFeatureEncoder,
      OpenAIGPTLMHeadModel,
      RobertaForMaskedLM,
      RobertaForSequenceClassification,
      TaForConditionalGeneration,
      TransfoXLLMHeadModel,
      XLMRobertaForMaskedLM,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
  )


logging.set_verbosity_info()

UpperCAmelCase :       Tuple			   =     {
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}







def    a__       (  a__      ,    a__      ,    a__      ,    a__      ,    a__=False      ,    a__=True						):




     """simple docstring"""


     if model_type not in MODEL_CLASSES:
          raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()						)}.'						)

     __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      MODEL_CLASSES[model_type]

     # Initialise TF model
     if config_file in aws_config_map:
          __SCREAMING_SNAKE_CASE     =      cached_file(a__      ,    a__      ,    force_download=not use_cached_models						)
     __SCREAMING_SNAKE_CASE     =      config_class.from_json_file(a__						)
     __SCREAMING_SNAKE_CASE     =      True
     __SCREAMING_SNAKE_CASE     =      True
     print(F'Building TensorFlow model from configuration: {config}'						)
     __SCREAMING_SNAKE_CASE     =      model_class(a__						)

     # Load weights from tf checkpoint
     if pytorch_checkpoint_path in aws_config_map.keys():
          __SCREAMING_SNAKE_CASE     =      cached_file(
              a__      ,    a__      ,    force_download=not use_cached_models						)
     # Load PyTorch checkpoint in tf2 model:
     __SCREAMING_SNAKE_CASE     =      load_pytorch_checkpoint_in_tfa_model(a__      ,    a__						)

     if compare_with_pt_model:
          __SCREAMING_SNAKE_CASE     =      tf_model(tf_model.dummy_inputs      ,    training=a__						)  # build the network

          __SCREAMING_SNAKE_CASE     =      torch.load(a__      ,    map_location="""cpu"""						)
          __SCREAMING_SNAKE_CASE     =      pt_model_class.from_pretrained(
              pretrained_model_name_or_path=a__      ,    config=a__      ,    state_dict=a__						)

          with torch.no_grad():
               __SCREAMING_SNAKE_CASE     =      pt_model(**pt_model.dummy_inputs						)

          __SCREAMING_SNAKE_CASE     =      pto[0].numpy()
          __SCREAMING_SNAKE_CASE     =      tfo[0].numpy()
          __SCREAMING_SNAKE_CASE     =      np.amax(np.abs(np_pt - np_tf						)						)
          print(F'Max absolute difference between models outputs {diff}'						)
          assert diff <= 2E-2, F'Error, model absolute difference is >2e-2: {diff}'

     # Save pytorch-model
     print(F'Save TensorFlow model to {tf_dump_path}'						)
     tf_model.save_weights(a__      ,    save_format="""h5"""						)







def    a__       (  a__      ,    a__      ,    a__=None      ,    a__=None      ,    a__=False      ,    a__=False      ,    a__=False      ,    a__=False      ,    ):




     """simple docstring"""


     if args_model_type is None:
          __SCREAMING_SNAKE_CASE     =      list(MODEL_CLASSES.keys()						)
     else:
          __SCREAMING_SNAKE_CASE     =      [args_model_type]

     for j, model_type in enumerate(a__      ,    start=1						):
          print("""=""" * 1_00						)
          print(F' Converting model type {j}/{len(a__						)}: {model_type}'						)
          print("""=""" * 1_00						)
          if model_type not in MODEL_CLASSES:
               raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()						)}.'						)

          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      MODEL_CLASSES[model_type]

          if model_shortcut_names_or_path is None:
               __SCREAMING_SNAKE_CASE     =      list(aws_model_maps.keys()						)
          if config_shortcut_names_or_path is None:
               __SCREAMING_SNAKE_CASE     =      model_shortcut_names_or_path

          for i, (model_shortcut_name, config_shortcut_name) in enumerate(
              zip(a__      ,    a__						)      ,    start=1						):
               print("""-""" * 1_00						)
               if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                    if not only_convert_finetuned_models:
                         print(F'    Skipping finetuned checkpoint {model_shortcut_name}'						)
                         continue
                    __SCREAMING_SNAKE_CASE     =      model_shortcut_name
               elif only_convert_finetuned_models:
                    print(F'    Skipping not finetuned checkpoint {model_shortcut_name}'						)
                    continue
               print(
                   F'    Converting checkpoint {i}/{len(a__						)}: {model_shortcut_name} - model_type {model_type}'						)
               print("""-""" * 1_00						)

               if config_shortcut_name in aws_config_map:
                    __SCREAMING_SNAKE_CASE     =      cached_file(a__      ,    a__      ,    force_download=not use_cached_models						)
               else:
                    __SCREAMING_SNAKE_CASE     =      config_shortcut_name

               if model_shortcut_name in aws_model_maps:
                    __SCREAMING_SNAKE_CASE     =      cached_file(a__      ,    a__      ,    force_download=not use_cached_models						)
               else:
                    __SCREAMING_SNAKE_CASE     =      model_shortcut_name

               if os.path.isfile(a__						):
                    __SCREAMING_SNAKE_CASE     =      """converted_model"""

               convert_pt_checkpoint_to_tf(
                   model_type=a__      ,    pytorch_checkpoint_path=a__      ,    config_file=a__      ,    tf_dump_path=os.path.join(a__      ,    model_shortcut_name + """-tf_model.h5"""						)      ,    compare_with_pt_model=a__      ,    )
               if remove_cached_files:
                    os.remove(a__						)
                    os.remove(a__						)


if __name__ == "__main__":
  UpperCAmelCase :       List[str]			   =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
  )
  parser.add_argument(
      '--model_type',
      default=None,
      type=str,
      help=(
          f"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
          'convert all the models from AWS.'
      ),
  )
  parser.add_argument(
      '--pytorch_checkpoint_path',
      default=None,
      type=str,
      help=(
          'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
          'If not given, will download and convert all the checkpoints from AWS.'
      ),
  )
  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      help=(
          'The config json file corresponding to the pre-trained model. \n'
          'This specifies the model architecture. If not given and '
          '--pytorch_checkpoint_path is not given or is a shortcut name '
          'use the configuration associated to the shortcut name on the AWS'
      ),
  )
  parser.add_argument(
      '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
  )
  parser.add_argument(
      '--use_cached_models',
      action='store_true',
      help='Use cached models if possible instead of updating to latest checkpoint versions.',
  )
  parser.add_argument(
      '--remove_cached_files',
      action='store_true',
      help='Remove pytorch models after conversion (save memory when converting in batches).',
  )
  parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
  UpperCAmelCase :       List[Any]			   =     parser.parse_args()

  # if args.pytorch_checkpoint_path is not None:
  #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
  #                                 args.pytorch_checkpoint_path,
  #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
  #                                 args.tf_dump_path,
  #                                 compare_with_pt_model=args.compare_with_pt_model,
  #                                 use_cached_models=args.use_cached_models)
  # else:
  convert_all_pt_checkpoints_to_tf(
      args.model_type.lower() if args.model_type is not None else None,
      args.tf_dump_path,
      model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
      if args.pytorch_checkpoint_path is not None
      else None,
      config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
      compare_with_pt_model=args.compare_with_pt_model,
      use_cached_models=args.use_cached_models,
      remove_cached_files=args.remove_cached_files,
      only_convert_finetuned_models=args.only_convert_finetuned_models,
  )






