
'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel






@require_tf
class  A  :
	snake_case__   :str   =  BlenderbotSmallConfig
	snake_case__   :Dict   =  {}
	snake_case__   :Union[str, Any]   =  'gelu'





	def __init__(       self      :							Any   ,				__magic_name__      :							int   ,				__magic_name__      :							int=13   ,				__magic_name__      :							int=7   ,				__magic_name__      :							Any=True   ,				__magic_name__      :							Tuple=False   ,				__magic_name__      :							Union[str, Any]=99   ,				__magic_name__      :							Union[str, Any]=32   ,				__magic_name__      :							Tuple=2   ,				__magic_name__      :							List[str]=4   ,				__magic_name__      :							List[Any]=37   ,				__magic_name__      :							Optional[Any]=0.1   ,				__magic_name__      :							Optional[Any]=0.1   ,				__magic_name__      :							Union[str, Any]=20   ,				__magic_name__      :							Tuple=2   ,				__magic_name__      :							List[str]=1   ,				__magic_name__      :							Union[str, Any]=0   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				parent
				lowerCAmelCase__										=				batch_size
				lowerCAmelCase__										=				seq_length
				lowerCAmelCase__										=				is_training
				lowerCAmelCase__										=				use_labels
				lowerCAmelCase__										=				vocab_size
				lowerCAmelCase__										=				hidden_size
				lowerCAmelCase__										=				num_hidden_layers
				lowerCAmelCase__										=				num_attention_heads
				lowerCAmelCase__										=				intermediate_size

				lowerCAmelCase__										=				hidden_dropout_prob
				lowerCAmelCase__										=				attention_probs_dropout_prob
				lowerCAmelCase__										=				max_position_embeddings
				lowerCAmelCase__										=				eos_token_id
				lowerCAmelCase__										=				pad_token_id
				lowerCAmelCase__										=				bos_token_id





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				ids_tensor([self.batch_size, self.seq_length - 1]   ,				self.vocab_size				)
				lowerCAmelCase__										=				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)   ,				1				)
				lowerCAmelCase__										=				tf.concat([input_ids, eos_tensor]   ,				axis=1				)

				lowerCAmelCase__										=				ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size				)

				lowerCAmelCase__										=				self.config_cls(
				    vocab_size=self.vocab_size   ,				d_model=self.hidden_size   ,				encoder_layers=self.num_hidden_layers   ,				decoder_layers=self.num_hidden_layers   ,				encoder_attention_heads=self.num_attention_heads   ,				decoder_attention_heads=self.num_attention_heads   ,				encoder_ffn_dim=self.intermediate_size   ,				decoder_ffn_dim=self.intermediate_size   ,				dropout=self.hidden_dropout_prob   ,				attention_dropout=self.attention_probs_dropout_prob   ,				max_position_embeddings=self.max_position_embeddings   ,				eos_token_ids=[2]   ,				bos_token_id=self.bos_token_id   ,				pad_token_id=self.pad_token_id   ,				decoder_start_token_id=self.pad_token_id   ,				**self.config_updates   ,				)
				lowerCAmelCase__										=				prepare_blenderbot_small_inputs_dict(__magic_name__   ,				__magic_name__   ,				__magic_name__				)
				return config, inputs_dict







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]   ,				__magic_name__      :							Dict   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				TFBlenderbotSmallModel(config=__magic_name__				).get_decoder()
				lowerCAmelCase__										=				inputs_dict["input_ids"]

				lowerCAmelCase__										=				input_ids[:1, :]
				lowerCAmelCase__										=				inputs_dict["attention_mask"][:1, :]
				lowerCAmelCase__										=				inputs_dict["head_mask"]
				lowerCAmelCase__										=				1

				# first forward pass
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__   ,				head_mask=__magic_name__   ,				use_cache=__magic_name__				)

				lowerCAmelCase__						,lowerCAmelCase__										=				outputs.to_tuple()

				# create hypothetical next token and extent to next_input_ids
				lowerCAmelCase__										=				ids_tensor((self.batch_size, 3)   ,				config.vocab_size				)
				lowerCAmelCase__										=				tf.cast(ids_tensor((self.batch_size, 3)   ,				2				)   ,				tf.inta				)

				# append to next input_ids and
				lowerCAmelCase__										=				tf.concat([input_ids, next_tokens]   ,				axis=-1				)
				lowerCAmelCase__										=				tf.concat([attention_mask, next_attn_mask]   ,				axis=-1				)

				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__				)[0]
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__   ,				past_key_values=__magic_name__				)[0]

				self.parent.assertEqual(next_tokens.shape[1]   ,				output_from_past.shape[1]				)

				# select random slice
				lowerCAmelCase__										=				int(ids_tensor((1,)   ,				output_from_past.shape[-1]				)				)
				lowerCAmelCase__										=				output_from_no_past[:, -3:, random_slice_idx]
				lowerCAmelCase__										=				output_from_past[:, :, random_slice_idx]

				# test that outputs are equal for slice
				tf.debugging.assert_near(__magic_name__   ,				__magic_name__   ,				rtol=1E-3				)







def 	A  (   UpperCamelCase_       :  Optional[Any]    ,						UpperCamelCase_       :  Union[str, Any]    ,						UpperCamelCase_       :  List[str]    ,						UpperCamelCase_       :  str=None    ,						UpperCamelCase_       :  List[str]=None    ,						UpperCamelCase_       :  Dict=None    ,						UpperCamelCase_       :  List[str]=None    ,						UpperCamelCase_       :  List[str]=None    ,						)       ->							List[Any]:







			'''simple docstring'''



			if attention_mask is None:
						lowerCAmelCase__										=				tf.cast(tf.math.not_equal(UpperCamelCase_    ,						config.pad_token_id   )    ,						tf.inta   )
			if decoder_attention_mask is None:
						lowerCAmelCase__										=				tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape    ,						dtype=tf.inta   ),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]    ,						config.pad_token_id   )    ,						tf.inta   ),
						    ]    ,						axis=-1    ,						)
			if head_mask is None:
						lowerCAmelCase__										=				tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
			if decoder_head_mask is None:
						lowerCAmelCase__										=				tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
			if cross_attn_head_mask is None:
						lowerCAmelCase__										=				tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}






@require_tf
class  A  (						SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :List[Any]   =  (
	    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
	)
	snake_case__   :Dict   =  (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
	snake_case__   :int   =  (
	    {
	        'conversational': TFBlenderbotSmallForConditionalGeneration,
	        'feature-extraction': TFBlenderbotSmallModel,
	        'summarization': TFBlenderbotSmallForConditionalGeneration,
	        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
	        'translation': TFBlenderbotSmallForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	snake_case__   :Optional[Any]   =  True
	snake_case__   :Any   =  False
	snake_case__   :int   =  False





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				TFBlenderbotSmallModelTester(self				)
				lowerCAmelCase__										=				ConfigTester(self   ,				config_class=__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				self.config_tester.run_common_tests()







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*__magic_name__				)






@require_tokenizers
@require_tf
class  A  (						unittest.TestCase     ):
	snake_case__   :Any   =  [
	    'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
	    '  i\'m going to throw up.\nand why is that?'
	]
	snake_case__   :Dict   =  'facebook/blenderbot_small-90M'





	@cached_property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"				)





	@cached_property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
				return model







	@slow
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.tokenizer(self.src_text   ,				return_tensors="tf"				)
				lowerCAmelCase__										=				self.model.generate(
				    model_inputs.input_ids   ,				attention_mask=model_inputs.attention_mask   ,				num_beams=2   ,				use_cache=__magic_name__   ,				)
				lowerCAmelCase__										=				self.tokenizer.batch_decode(generated_ids.numpy()   ,				skip_special_tokens=__magic_name__				)[0]
				assert generated_words in (
				    "i don't know. i just feel like i'm going to throw up. it's not fun.",
				    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
				    "i'm not sure. i just feel like i've been in a bad situation.",
				)










import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	int:
    A__					=       '''huggingface/label-files'''
    A__					=       '''imagenet-1k-id2label.json'''
    A__					=       json.load(open(hf_hub_download(UpperCamelCase_			,							UpperCamelCase_			,							repo_type='''dataset'''						)			,							'''r'''						)						)
    A__					=       {int(UpperCamelCase_						): v for k, v in idalabel.items()}
    A__					=       {v: k for k, v in idalabel.items()}

    A__					=       '''std_conv''' if '''bit''' in model_name else False

    # note that when using BiT as backbone for ViT-hybrid checkpoints,
    # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
    # config.conv_layer = "std_conv_same"
    A__					=       BitConfig(
        conv_layer=UpperCamelCase_			,							num_labels=1_0_0_0			,							idalabel=UpperCamelCase_			,							labelaid=UpperCamelCase_			,							)

    return config



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[Any]						)->	Optional[int]:
    if "stem.conv" in name:
        A__					=       name.replace('''stem.conv'''			,							'''bit.embedder.convolution'''						)
    if "blocks" in name:
        A__					=       name.replace('''blocks'''			,							'''layers'''						)
    if "head.fc" in name:
        A__					=       name.replace('''head.fc'''			,							'''classifier.1'''						)
    if name.startswith('''norm'''						):
        A__					=       '''bit.''' + name
    if "bit" not in name and "classifier" not in name:
        A__					=       '''bit.encoder.''' + name

    return name



def 		lowerCAmelCase__     (       )->	str:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							Tuple			,							UpperCamelCase_					:							List[Any]=False						)->	Optional[int]:
    A__					=       get_config(UpperCamelCase_						)

    # load original model from timm
    A__					=       create_model(UpperCamelCase_			,							pretrained=UpperCamelCase_						)
    timm_model.eval()

    # load state_dict of original model
    A__					=       timm_model.state_dict()
    for key in state_dict.copy().keys():
        A__					=       state_dict.pop(UpperCamelCase_						)
        A__					=       val.squeeze() if '''head''' in key else val

    # load HuggingFace model
    A__					=       BitForImageClassification(UpperCamelCase_						)
    model.eval()
    model.load_state_dict(UpperCamelCase_						)

    # create image processor
    A__					=       create_transform(**resolve_data_config({}			,							model=UpperCamelCase_						)						)
    A__					=       transform.transforms

    A__					=       {
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    A__					=       BitImageProcessor(
        do_resize=UpperCamelCase_			,							size={'''shortest_edge''': timm_transforms[0].size}			,							resample=pillow_resamplings[timm_transforms[0].interpolation.value]			,							do_center_crop=UpperCamelCase_			,							crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}			,							do_normalize=UpperCamelCase_			,							image_mean=timm_transforms[-1].mean.tolist()			,							image_std=timm_transforms[-1].std.tolist()			,							)

    A__					=       prepare_img()
    A__					=       transform(UpperCamelCase_						).unsqueeze(0						)
    A__					=       processor(UpperCamelCase_			,							return_tensors='''pt'''						).pixel_values

    # verify pixel values
    assert torch.allclose(UpperCamelCase_			,							UpperCamelCase_						)

    # verify logits
    with torch.no_grad():
        A__					=       model(UpperCamelCase_						)
        A__					=       outputs.logits

    print('''Logits:'''			,							logits[0, :3]						)
    print('''Predicted class:'''			,							model.config.idalabel[logits.argmax(-1						).item()]						)
    A__					=       timm_model(UpperCamelCase_						)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(UpperCamelCase_			,							outputs.logits			,							atol=1E-3						)
    print('''Looks ok!'''						)

    if pytorch_dump_folder_path is not None:
        Path(UpperCamelCase_						).mkdir(exist_ok=UpperCamelCase_						)
        print(f"Saving model {model_name} and processor to {pytorch_dump_folder_path}"						)
        model.save_pretrained(UpperCamelCase_						)
        processor.save_pretrained(UpperCamelCase_						)

    if push_to_hub:
        print(f"Pushing model {model_name} and processor to the hub"						)
        model.push_to_hub(f"ybelkada/{model_name}"						)
        processor.push_to_hub(f"ybelkada/{model_name}"						)


if __name__ == "__main__":
   _lowercase    	=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="resnetv2_50x1_bitm",
       type=str,
       help="Name of the BiT timm model you'd like to convert.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
   )
   parser.add_argument(
       "--push_to_hub",
       action="store_true",
       help="Whether to push the model to the hub.",
   )

   _lowercase    	=		parser.parse_args()
   convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
