






import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       List[Any]							       = KandinskyVaaPipeline
				_UpperCamelCase					:       int							       = [
				    "image_embeds",
				    "negative_image_embeds",
				]
				_UpperCamelCase					:       Optional[int]							       = ["image_embeds", "negative_image_embeds"]
				_UpperCamelCase					:       Optional[Any]							       = [
				    "generator",
				    "height",
				    "width",
				    "latents",
				    "guidance_scale",
				    "num_inference_steps",
				    "return_dict",
				    "guidance_scale",
				    "num_images_per_prompt",
				    "output_type",
				    "return_dict",
				]
				_UpperCamelCase					:       Dict							       = False


				@property
				def 						__a					(					self      ):
											return 3_2


				@property
				def 						__a					(					self      ):
											return 3_2


				@property
				def 						__a					(					self      ):
											return self.time_input_dim


				@property
				def 						__a					(					self      ):
											return self.time_input_dim * 4


				@property
				def 						__a					(					self      ):
											return 1_0_0


				@property
				def 						__a					(					self      ):
											torch.manual_seed(0      )

											_lowercase							:   Tuple 							=   {
											    'in_channels': 4,
											    # Out channels is double in channels because predicts mean and variance
											    'out_channels': 8,
											    'addition_embed_type': 'image',
											    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
											    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
											    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
											    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
											    'layers_per_block': 1,
											    'encoder_hid_dim': self.text_embedder_hidden_size,
											    'encoder_hid_dim_type': 'image_proj',
											    'cross_attention_dim': self.cross_attention_dim,
											    'attention_head_dim': 4,
											    'resnet_time_scale_shift': 'scale_shift',
											    'class_embed_type': None,
											}

											_lowercase							:   Union[str, Any] 							=   UNetaDConditionModel(**_lowerCAmelCase      )
											return model


				@property
				def 						__a					(					self      ):
											return {
											    "block_out_channels": [3_2, 6_4],
											    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
											    "in_channels": 3,
											    "latent_channels": 4,
											    "layers_per_block": 1,
											    "norm_num_groups": 8,
											    "norm_type": "spatial",
											    "num_vq_embeddings": 1_2,
											    "out_channels": 3,
											    "up_block_types": [
											        "AttnUpDecoderBlock2D",
											        "UpDecoderBlock2D",
											    ],
											    "vq_embed_dim": 4,
											}


				@property
				def 						__a					(					self      ):
											torch.manual_seed(0      )
											_lowercase							:   Union[str, Any] 							=   VQModel(**self.dummy_movq_kwargs      )
											return model


				def 						__a					(					self      ):
											_lowercase							:   List[str] 							=   self.dummy_unet
											_lowercase							:   Optional[int] 							=   self.dummy_movq

											_lowercase							:   Tuple 							=   DDIMScheduler(
											    num_train_timesteps=1_0_0_0 ,						beta_schedule='linear' ,						beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						clip_sample=_lowerCAmelCase ,						set_alpha_to_one=_lowerCAmelCase ,						steps_offset=1 ,						prediction_type='epsilon' ,						thresholding=_lowerCAmelCase ,						)

											_lowercase							:   str 							=   {
											    'unet': unet,
											    'scheduler': scheduler,
											    'movq': movq,
											}
											return components


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=0      ):
											_lowercase							:   Optional[int] 							=   floats_tensor((1, self.text_embedder_hidden_size) ,						rng=random.Random(_lowerCAmelCase      )      ).to(_lowerCAmelCase      )
											_lowercase							:   Tuple 							=   floats_tensor((1, self.text_embedder_hidden_size) ,						rng=random.Random(seed + 1      )      ).to(
											    _lowerCAmelCase      )
											if str(_lowerCAmelCase      ).startswith('mps'      ):
																		_lowercase							:   Any 							=   torch.manual_seed(_lowerCAmelCase      )
											else:
																		_lowercase							:   Tuple 							=   torch.Generator(device=_lowerCAmelCase      ).manual_seed(_lowerCAmelCase      )
											_lowercase							:   Optional[int] 							=   {
											    'image_embeds': image_embeds,
											    'negative_image_embeds': negative_image_embeds,
											    'generator': generator,
											    'height': 6_4,
											    'width': 6_4,
											    'guidance_scale': 4.0,
											    'num_inference_steps': 2,
											    'output_type': 'np',
											}
											return inputs


				def 						__a					(					self      ):
											_lowercase							:   Union[str, Any] 							=   'cpu'

											_lowercase							:   Any 							=   self.get_dummy_components()

											_lowercase							:   Any 							=   self.pipeline_class(**_lowerCAmelCase      )
											_lowercase							:   str 							=   pipe.to(_lowerCAmelCase      )

											pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

											_lowercase							:   Tuple 							=   pipe(**self.get_dummy_inputs(_lowerCAmelCase      )      )
											_lowercase							:   Dict 							=   output.images

											_lowercase							:   str 							=   pipe(
											    **self.get_dummy_inputs(_lowerCAmelCase      ) ,						return_dict=_lowerCAmelCase ,						)[0]

											_lowercase							:   str 							=   image[0, -3:, -3:, -1]
											_lowercase							:   List[Any] 							=   image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)

											_lowercase							:   Any 							=   np.array(
											    [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]      )

											assert (
											    np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
											), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

											assert (
											    np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2
											), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




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


				def 						__a					(					self      ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


				def 						__a					(					self      ):
											_lowercase							:   List[str] 							=   load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'      )

											_lowercase							:   Optional[Any] 							=   KandinskyVaaPriorPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-2-prior' ,						torch_dtype=torch.floataa      )
											pipe_prior.to(_lowerCAmelCase      )

											_lowercase							:   int 							=   KandinskyVaaPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-2-decoder' ,						torch_dtype=torch.floataa      )
											_lowercase							:   List[Any] 							=   pipeline.to(_lowerCAmelCase      )
											pipeline.set_progress_bar_config(disable=_lowerCAmelCase      )

											_lowercase							:   List[Any] 							=   'red cat, 4k photo'

											_lowercase							:   List[str] 							=   torch.Generator(device='cuda'      ).manual_seed(0      )
											_lowercase							,							_lowercase							:   Union[str, Any] 							=   pipe_prior(
											    _lowerCAmelCase ,						generator=_lowerCAmelCase ,						num_inference_steps=5 ,						negative_prompt='' ,						).to_tuple()

											_lowercase							:   Any 							=   torch.Generator(device='cuda'      ).manual_seed(0      )
											_lowercase							:   Union[str, Any] 							=   pipeline(
											    image_embeds=_lowerCAmelCase ,						negative_image_embeds=_lowerCAmelCase ,						generator=_lowerCAmelCase ,						num_inference_steps=1_0_0 ,						output_type='np' ,						)

											_lowercase							:   Optional[int] 							=   output.images[0]

											assert image.shape == (5_1_2, 5_1_2, 3)

											assert_mean_pixel_difference(_lowerCAmelCase ,						_lowerCAmelCase      )






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()

_snake_case      = {
    '''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  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase=False						,     _lowercase=True			)       ->      List[Any]:
     if model_type not in MODEL_CLASSES:
          raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()			)}.'			)

     UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					MODEL_CLASSES[model_type]

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

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

     if compare_with_pt_model:
          UpperCamelCase						    =					tf_model(tf_model.dummy_inputs						,     training=_lowercase			)  # build the network

          UpperCamelCase						    =					torch.load(_lowercase						,     map_location='cpu'			)
          UpperCamelCase						    =					pt_model_class.from_pretrained(
              pretrained_model_name_or_path=_lowercase						,     config=_lowercase						,     state_dict=_lowercase			)

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

          UpperCamelCase						    =					pto[0].numpy()
          UpperCamelCase						    =					tfo[0].numpy()
          UpperCamelCase						    =					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(_lowercase						,     save_format='h5'			)

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase=None						,     _lowercase=None						,     _lowercase=False						,     _lowercase=False						,     _lowercase=False						,     _lowercase=False						,     )       ->      int:
     if args_model_type is None:
          UpperCamelCase						    =					list(MODEL_CLASSES.keys()			)
     else:
          UpperCamelCase						    =					[args_model_type]

     for j, model_type in enumerate(_lowercase						,     start=1			):
          print('=' * 100			)
          print(F' Converting model type {j}/{len(_lowercase			)}: {model_type}'			)
          print('=' * 100			)
          if model_type not in MODEL_CLASSES:
               raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()			)}.'			)

          UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					MODEL_CLASSES[model_type]

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

          for i, (model_shortcut_name, config_shortcut_name) in enumerate(
              zip(_lowercase						,     _lowercase			)						,     start=1			):
               print('-' * 100			)
               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
                    UpperCamelCase						    =					model_shortcut_name
               elif only_convert_finetuned_models:
                    print(F'    Skipping not finetuned checkpoint {model_shortcut_name}'			)
                    continue
               print(
                   F'    Converting checkpoint {i}/{len(_lowercase			)}: {model_shortcut_name} - model_type {model_type}'			)
               print('-' * 100			)

               if config_shortcut_name in aws_config_map:
                    UpperCamelCase						    =					cached_file(_lowercase						,     _lowercase						,     force_download=not use_cached_models			)
               else:
                    UpperCamelCase						    =					config_shortcut_name

               if model_shortcut_name in aws_model_maps:
                    UpperCamelCase						    =					cached_file(_lowercase						,     _lowercase						,     force_download=not use_cached_models			)
               else:
                    UpperCamelCase						    =					model_shortcut_name

               if os.path.isfile(_lowercase			):
                    UpperCamelCase						    =					'converted_model'

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


if __name__ == "__main__":
 _snake_case      = 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.''')
 _snake_case      = 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,
 )


