






"""simple docstring"""
from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__=None   ,   snake_case__=None					):




	if attention_mask is None:
		A_		:						Dict	    =   tf.cast(tf.math.not_equal(__lowercase   ,   config.pad_token_id					)   ,   tf.inta					)
	return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	_A       :				int    =				OPTConfig
	_A       :				List[str]    =				{}
	_A       :				Any    =				"""gelu"""





	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=13     ,     lowerCAmelCase_=7     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=99     ,     lowerCAmelCase_=16     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_="gelu"     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=20     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=0     ,     lowerCAmelCase_=16     ,     lowerCAmelCase_=16     ,     ):
		A_		:						Tuple	    =   parent
		A_		:						Optional[Any]	    =   batch_size
		A_		:						Any	    =   seq_length
		A_		:						Optional[int]	    =   is_training
		A_		:						Optional[int]	    =   use_labels
		A_		:						Any	    =   vocab_size
		A_		:						Union[str, Any]	    =   hidden_size
		A_		:						Dict	    =   num_hidden_layers
		A_		:						Tuple	    =   num_attention_heads
		A_		:						Tuple	    =   intermediate_size
		A_		:						Tuple	    =   hidden_act
		A_		:						Optional[int]	    =   hidden_dropout_prob
		A_		:						int	    =   attention_probs_dropout_prob
		A_		:						Any	    =   max_position_embeddings
		A_		:						str	    =   eos_token_id
		A_		:						List[str]	    =   pad_token_id
		A_		:						Optional[int]	    =   bos_token_id
		A_		:						Any	    =   embed_dim
		A_		:						Optional[Any]	    =   word_embed_proj_dim
		A_		:						Optional[Any]	    =   False





	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   ids_tensor([self.batch_size, self.seq_length - 1]     ,     self.vocab_size							)
		A_		:						Optional[Any]	    =   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)     ,     1							)
		A_		:						Tuple	    =   tf.concat([input_ids, eos_tensor]     ,     axis=1							)

		A_		:						Tuple	    =   self.config_cls(
		    vocab_size=self.vocab_size     ,     hidden_size=self.hidden_size     ,     num_hidden_layers=self.num_hidden_layers     ,     num_attention_heads=self.num_attention_heads     ,     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_id=self.eos_token_id     ,     bos_token_id=self.bos_token_id     ,     pad_token_id=self.pad_token_id     ,     embed_dim=self.embed_dim     ,     word_embed_proj_dim=self.word_embed_proj_dim     ,     is_encoder_decoder=lowerCAmelCase_     ,     **self.config_updates     ,     )
		A_		:						Union[str, Any]	    =   prepare_opt_inputs_dict(lowerCAmelCase_     ,     lowerCAmelCase_							)
		return config, inputs_dict





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Optional[Any]	    =   TFOPTModel(config=lowerCAmelCase_							)
		A_		:						List[Any]	    =   inputs_dict['input_ids']

		A_		:						Optional[int]	    =   input_ids[:1, :]
		A_		:						Optional[Any]	    =   inputs_dict['attention_mask'][:1, :]
		A_		:						List[str]	    =   1

		# first forward pass
		A_		:						Any	    =   model(lowerCAmelCase_     ,     attention_mask=lowerCAmelCase_     ,     use_cache=lowerCAmelCase_							)

		A_		:						str	    =   outputs.to_tuple()

		# create hypothetical next token and extent to next_input_ids
		A_		:						Optional[Any]	    =   ids_tensor((self.batch_size, 3)     ,     config.vocab_size							)
		A_		:						Dict	    =   tf.cast(ids_tensor((self.batch_size, 3)     ,     2							)     ,     tf.inta							)

		# append to next input_ids and
		A_		:						List[str]	    =   tf.concat([input_ids, next_tokens]     ,     axis=-1							)
		A_		:						Tuple	    =   tf.concat([attention_mask, next_attn_mask]     ,     axis=-1							)

		A_		:						Tuple	    =   model(lowerCAmelCase_     ,     attention_mask=lowerCAmelCase_							)[0]
		A_		:						Any	    =   model(lowerCAmelCase_     ,     attention_mask=lowerCAmelCase_     ,     past_key_values=lowerCAmelCase_							)[0]

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

		# select random slice
		A_		:						Tuple	    =   int(ids_tensor((1,)     ,     output_from_past.shape[-1]							)							)
		A_		:						Union[str, Any]	    =   output_from_no_past[:, -3:, random_slice_idx]
		A_		:						int	    =   output_from_past[:, :, random_slice_idx]

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






@require_tf
class      SCREAMING_SNAKE_CASE  (				__A       ,       __A       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				List[str]    =				(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
	_A       :				Union[str, Any]    =				(TFOPTForCausalLM,) if is_tf_available() else ()
	_A       :				List[Any]    =				(
	    {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
	)
	_A       :				Union[str, Any]    =				False
	_A       :				Dict    =				False
	_A       :				List[Any]    =				False
	_A       :				int    =				10





	def    lowerCamelCase(self							):
		A_		:						List[str]	    =   TFOPTModelTester(self							)
		A_		:						Optional[Any]	    =   ConfigTester(self     ,     config_class=lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		self.config_tester.run_common_tests()





	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   self.model_tester.prepare_config_and_inputs_for_common()
		self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						Any	    =   self.model_tester.prepare_config_and_inputs_for_common()

		def _get_word_embedding_weight(lowerCAmelCase_     ,     lowerCAmelCase_							):
			if hasattr(lowerCAmelCase_     ,     """weight"""							):
				return embedding_layer.weight
			else:
				# Here we build the word embeddings weights if not exists.
				# And then we retry to get the attribute once built.
				model.build()
				if hasattr(lowerCAmelCase_     ,     """weight"""							):
					return embedding_layer.weight
				else:
					return None

		for model_class in self.all_model_classes:
			for size in [config.vocab_size - 10, config.vocab_size + 10]:
				# build the embeddings
				A_		:						Dict	    =   model_class(config=lowerCAmelCase_							)
				A_		:						str	    =   _get_word_embedding_weight(lowerCAmelCase_     ,     model.get_input_embeddings()							)
				A_		:						Any	    =   _get_word_embedding_weight(lowerCAmelCase_     ,     model.get_output_embeddings()							)

				# reshape the embeddings
				model.resize_token_embeddings(lowerCAmelCase_							)
				A_		:						Dict	    =   _get_word_embedding_weight(lowerCAmelCase_     ,     model.get_input_embeddings()							)
				A_		:						Tuple	    =   _get_word_embedding_weight(lowerCAmelCase_     ,     model.get_output_embeddings()							)

				# check that the resized embeddings size matches the desired size.
				A_		:						int	    =   size if size is not None else config.vocab_size

				self.assertEqual(new_input_embeddings.shape[0]     ,     lowerCAmelCase_							)

				# check that weights remain the same after resizing
				A_		:						Any	    =   True
				for pa, pa in zip(old_input_embeddings.value()     ,     new_input_embeddings.value()							):
					if tf.math.reduce_sum(tf.math.abs(pa - pa							)							) > 0:
						A_		:						int	    =   False
				self.assertTrue(lowerCAmelCase_							)

				if old_output_embeddings is not None and new_output_embeddings is not None:
					self.assertEqual(new_output_embeddings.shape[0]     ,     lowerCAmelCase_							)

					A_		:						Tuple	    =   True
					for pa, pa in zip(old_output_embeddings.value()     ,     new_output_embeddings.value()							):
						if tf.math.reduce_sum(tf.math.abs(pa - pa							)							) > 0:
							A_		:						Union[str, Any]	    =   False
					self.assertTrue(lowerCAmelCase_							)






def 			__UpperCamelCase	(		snake_case__					):
	return tf.constant(__lowercase   ,   dtype=tf.intaa					)



@require_tf
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	_A       :				str    =				99





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   tf.ones((4, 1)     ,     dtype=tf.intaa							) * 2
		A_		:						Optional[Any]	    =   tf.concat([ids_tensor((4, 6)     ,     self.vocab_size - 3							) + 3, eos_column_vector]     ,     axis=1							)
		A_		:						Dict	    =   input_ids.shape[0]
		A_		:						Union[str, Any]	    =   OPTConfig(
		    vocab_size=self.vocab_size     ,     hidden_size=24     ,     num_hidden_layers=2     ,     num_attention_heads=2     ,     ffn_dim=32     ,     max_position_embeddings=48     ,     eos_token_id=2     ,     pad_token_id=1     ,     bos_token_id=0     ,     )
		return config, input_ids, batch_size






@require_sentencepiece
@require_tf
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	@slow
	def    lowerCamelCase(self							):
		A_		:						Dict	    =   TFOPTModel.from_pretrained("""facebook/opt-350m"""							)
		A_		:						Optional[int]	    =   _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]							)
		A_		:						Any	    =   tf.not_equal(lowerCAmelCase_     ,     model.config.pad_token_id							)
		with tf.GradientTape():
			A_		:						Dict	    =   model(input_ids=lowerCAmelCase_     ,     attention_mask=lowerCAmelCase_							).last_hidden_state
		A_		:						List[Any]	    =   (1, 11, 512)
		self.assertEqual(output.shape     ,     lowerCAmelCase_							)
		A_		:						List[Any]	    =   tf.constant(
		    [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]							)
		self.assertTrue(np.allclose(output[:, :3, :3]     ,     lowerCAmelCase_     ,     atol=4e-3							)							)

		A_		:						Dict	    =   tf.function(lowerCAmelCase_     ,     jit_compile=lowerCAmelCase_							)
		A_		:						Optional[int]	    =   xla_generate(lowerCAmelCase_     ,     lowerCAmelCase_							)[0]
		self.assertTrue(np.allclose(output[:, :3, :3]     ,     lowerCAmelCase_     ,     atol=4e-2							)							)






@require_tf
@slow
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	def    lowerCamelCase(self							):
		super().setUp()
		A_		:						List[Any]	    =   'facebook/opt-350m'





	def    lowerCamelCase(self							):
		A_		:						str	    =   TFOPTForCausalLM.from_pretrained(self.path_model							)
		A_		:						Optional[Any]	    =   GPTaTokenizer.from_pretrained(self.path_model							)

		A_		:						Any	    =   [
		    'Today is a beautiful day and I want to',
		    'In the city of',
		    'Paris is the capital of France and',
		    'Computers and mobile phones have taken',
		]
		# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
		A_		:						Optional[int]	    =   tokenizer(lowerCAmelCase_     ,     return_tensors="""tf"""     ,     padding=lowerCAmelCase_     ,     add_special_tokens=lowerCAmelCase_							)
		A_		:						Optional[int]	    =   tf.math.reduce_mean(model(inputs.input_ids     ,     attention_mask=inputs.attention_mask							)[0]     ,     axis=-1							)
		A_		:						Optional[Any]	    =   tf.constant(
		    [
		        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
		        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
		        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
		        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
		    ]							)
		self.assertTrue(np.allclose(lowerCAmelCase_     ,     lowerCAmelCase_     ,     atol=1e-4							)							)

		A_		:						Union[str, Any]	    =   tf.function(lowerCAmelCase_     ,     jit_compile=lowerCAmelCase_							)
		A_		:						Any	    =   tf.math.reduce_mean(xla_generate(inputs.input_ids     ,     attention_mask=inputs.attention_mask							)[0]     ,     axis=-1							)
		self.assertTrue(np.allclose(lowerCAmelCase_     ,     lowerCAmelCase_     ,     atol=1e-4							)							)






@require_tf
@slow
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	@property
	def    lowerCamelCase(self							):
		return [
		    "Today is a beautiful day and I want",
		    "In the city of",
		    "Paris is the capital of France and",
		    "Computers and mobile phones have taken",
		]





	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   'facebook/opt-125m'

		A_		:						Optional[int]	    =   [
		    'Today is a beautiful day and I want to',
		    'In the city of New York, the city',
		    'Paris is the capital of France and the capital',
		    'Computers and mobile phones have taken over the',
		]

		A_		:						Dict	    =   []
		A_		:						List[Any]	    =   GPTaTokenizer.from_pretrained(lowerCAmelCase_							)
		A_		:						Tuple	    =   TFOPTForCausalLM.from_pretrained(lowerCAmelCase_							)

		for prompt in self.prompts:
			A_		:						Dict	    =   tokenizer(lowerCAmelCase_     ,     return_tensors="""tf"""							).input_ids

			A_		:						List[Any]	    =   model.generate(lowerCAmelCase_     ,     max_length=10							)

			A_		:						Optional[int]	    =   tokenizer.batch_decode(lowerCAmelCase_     ,     skip_special_tokens=lowerCAmelCase_							)
			predicted_outputs += generated_string

		self.assertListEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						List[str]	    =   'facebook/opt-350m'

		A_		:						Optional[int]	    =   GPTaTokenizer.from_pretrained(lowerCAmelCase_							)
		A_		:						Tuple	    =   TFOPTForCausalLM.from_pretrained(lowerCAmelCase_							)

		A_		:						Tuple	    =   'left'

		# use different length sentences to test batching
		A_		:						Any	    =   [
		    'Hello, my dog is a little',
		    'Today, I',
		]

		A_		:						Dict	    =   tokenizer(lowerCAmelCase_     ,     return_tensors="""tf"""     ,     padding=lowerCAmelCase_							)
		A_		:						List[str]	    =   inputs['input_ids']

		A_		:						Optional[int]	    =   model.generate(input_ids=lowerCAmelCase_     ,     attention_mask=inputs["""attention_mask"""]							)

		A_		:						Union[str, Any]	    =   tokenizer(sentences[0]     ,     return_tensors="""tf"""							).input_ids
		A_		:						Any	    =   model.generate(input_ids=lowerCAmelCase_							)

		A_		:						Any	    =   inputs_non_padded.shape[-1] - tf.math.reduce_sum(
		    tf.cast(inputs["""attention_mask"""][-1]     ,     tf.intaa							)							)
		A_		:						List[str]	    =   tokenizer(sentences[1]     ,     return_tensors="""tf"""							).input_ids
		A_		:						Optional[Any]	    =   model.generate(input_ids=lowerCAmelCase_     ,     max_length=model.config.max_length - num_paddings							)

		A_		:						Tuple	    =   tokenizer.batch_decode(lowerCAmelCase_     ,     skip_special_tokens=lowerCAmelCase_							)
		A_		:						List[str]	    =   tokenizer.decode(output_non_padded[0]     ,     skip_special_tokens=lowerCAmelCase_							)
		A_		:						str	    =   tokenizer.decode(output_padded[0]     ,     skip_special_tokens=lowerCAmelCase_							)

		A_		:						List[Any]	    =   [
		    'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
		    'Today, I was in the middle of a conversation with a friend about the',
		]
		self.assertListEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)
		self.assertListEqual(lowerCAmelCase_     ,     [non_padded_sentence, padded_sentence]							)





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   'facebook/opt-350m'

		A_		:						Any	    =   [
		    'Today is a beautiful day and I want to',
		    'In the city of San Francisco, the city',
		    'Paris is the capital of France and the capital',
		    'Computers and mobile phones have taken over the',
		]

		A_		:						List[Any]	    =   []
		A_		:						List[Any]	    =   GPTaTokenizer.from_pretrained(lowerCAmelCase_							)
		A_		:						int	    =   TFOPTForCausalLM.from_pretrained(lowerCAmelCase_							)

		for prompt in self.prompts:
			A_		:						int	    =   tokenizer(lowerCAmelCase_     ,     return_tensors="""tf"""							).input_ids

			A_		:						int	    =   model.generate(lowerCAmelCase_     ,     max_length=10							)

			A_		:						str	    =   tokenizer.batch_decode(lowerCAmelCase_     ,     skip_special_tokens=lowerCAmelCase_							)
			predicted_outputs += generated_string

		self.assertListEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)









"""simple docstring"""
import argparse
import json
from pathlib import Path

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

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__=False					):
	A_		:						Dict	    =   []

	# fmt: off
	# stem:
	rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""")					)
	rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""")					)

	rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""")					)
	rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""")					)

	# backbone
	rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""")					)
	rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""")					)
	rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""")					)

	for stage_idx in range(len(config.backbone_config.depths					)					):
		for layer_idx in range(config.backbone_config.depths[stage_idx]					):
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")					)

		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")					)
		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")					)
		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")					)

	# transformer encoder
	for i in range(config.num_hidden_layers					):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")					)
		rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")					)
		rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")					)
		rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")					)
		rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")					)
		rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")					)
		rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")					)
		rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")					)
		rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")					)
		rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")					)

	if base_model:
		# layernorm + pooler
		rename_keys.extend(
		    [
		        ("""norm.weight""", """layernorm.weight"""),
		        ("""norm.bias""", """layernorm.bias"""),
		        ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
		        ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
		    ]					)

		# if just the base model, we should remove "vit" from all keys that start with "vit"
		A_		:						Dict	    =   [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""					) else pair for pair in rename_keys]
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ("""norm.weight""", """vit.layernorm.weight"""),
		        ("""norm.bias""", """vit.layernorm.bias"""),
		        ("""head.weight""", """classifier.weight"""),
		        ("""head.bias""", """classifier.bias"""),
		    ]					)
	# fmt: on

	return rename_keys






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__=False					):
	for i in range(config.num_hidden_layers					):
		if base_model:
			A_		:						Any	    =   """"""
		else:
			A_		:						List[Any]	    =   """vit."""
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		A_		:						Tuple	    =   state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""					)
		A_		:						Union[str, Any]	    =   state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""					)
		# next, add query, keys and values (in that order) to the state dict
		A_		:						Dict	    =   in_proj_weight[
		    : config.hidden_size, :
		]
		A_		:						int	    =   in_proj_bias[: config.hidden_size]
		A_		:						Optional[int]	    =   in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		A_		:						Any	    =   in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		A_		:						List[str]	    =   in_proj_weight[
		    -config.hidden_size :, :
		]
		A_		:						Optional[Any]	    =   in_proj_bias[-config.hidden_size :]






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Tuple	    =   ["""head.weight""", """head.bias"""]
	for k in ignore_keys:
		state_dict.pop(snake_case__   ,   snake_case__					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Union[str, Any]	    =   dct.pop(snake_case__					)
	A_		:						List[Any]	    =   val






def 			__UpperCamelCase	(		):
	A_		:						Any	    =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
	A_		:						str	    =   Image.open(requests.get(snake_case__   ,   stream=snake_case__					).raw					)
	return im






@torch.no_grad()
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__=False					):
	A_		:						str	    =   BitConfig(
	    global_padding="""same"""   ,   layer_type="""bottleneck"""   ,   depths=(3, 4, 9)   ,   out_features=["""stage3"""]   ,   embedding_dynamic_padding=snake_case__   ,   )
	A_		:						int	    =   ViTHybridConfig(backbone_config=snake_case__   ,   image_size=384   ,   num_labels=1_000					)
	A_		:						Any	    =   False

	# load original model from timm
	A_		:						str	    =   timm.create_model(snake_case__   ,   pretrained=snake_case__					)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	A_		:						Optional[Any]	    =   timm_model.state_dict()
	if base_model:
		remove_classification_head_(snake_case__					)
	A_		:						Dict	    =   create_rename_keys(snake_case__   ,   snake_case__					)
	for src, dest in rename_keys:
		rename_key(snake_case__   ,   snake_case__   ,   snake_case__					)
	read_in_q_k_v(snake_case__   ,   snake_case__   ,   snake_case__					)

	A_		:						Any	    =   """huggingface/label-files"""
	A_		:						Any	    =   """imagenet-1k-id2label.json"""
	A_		:						Any	    =   json.load(open(hf_hub_download(snake_case__   ,   snake_case__   ,   repo_type="""dataset"""					)   ,   """r"""					)					)
	A_		:						Optional[int]	    =   {int(snake_case__					): v for k, v in idalabel.items()}
	A_		:						Optional[int]	    =   idalabel
	A_		:						Dict	    =   {v: k for k, v in idalabel.items()}

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		A_		:						int	    =   ViTHybridModel(snake_case__					).eval()
	else:
		A_		:						Union[str, Any]	    =   ViTHybridForImageClassification(snake_case__					).eval()
	model.load_state_dict(snake_case__					)

	# create image processor
	A_		:						Any	    =   create_transform(**resolve_data_config({}   ,   model=snake_case__					)					)
	A_		:						List[Any]	    =   transform.transforms

	A_		:						int	    =   {
	    """bilinear""": PILImageResampling.BILINEAR,
	    """bicubic""": PILImageResampling.BICUBIC,
	    """nearest""": PILImageResampling.NEAREST,
	}

	A_		:						Optional[int]	    =   ViTHybridImageProcessor(
	    do_resize=snake_case__   ,   size={"""shortest_edge""": timm_transforms[0].size}   ,   resample=pillow_resamplings[timm_transforms[0].interpolation.value]   ,   do_center_crop=snake_case__   ,   crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}   ,   do_normalize=snake_case__   ,   image_mean=timm_transforms[-1].mean.tolist()   ,   image_std=timm_transforms[-1].std.tolist()   ,   )

	A_		:						Tuple	    =   prepare_img()
	A_		:						List[str]	    =   transform(snake_case__					).unsqueeze(0					)
	A_		:						int	    =   processor(snake_case__   ,   return_tensors="""pt"""					).pixel_values

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

	# verify logits
	with torch.no_grad():
		A_		:						List[str]	    =   model(snake_case__					)
		A_		:						Any	    =   outputs.logits

	print("""Predicted class:"""   ,   logits.argmax(-1					).item()					)
	if base_model:
		A_		:						int	    =   timm_model.forward_features(snake_case__					)
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(snake_case__   ,   outputs.pooler_output   ,   atol=1E-3					)
	else:
		A_		:						Optional[int]	    =   timm_model(snake_case__					)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(snake_case__   ,   outputs.logits   ,   atol=1E-3					)
	print("""Looks ok!"""					)

	if pytorch_dump_folder_path is not None:
		Path(snake_case__					).mkdir(exist_ok=snake_case__					)
		print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""					)
		model.save_pretrained(snake_case__					)
		print(F"""Saving processor to {pytorch_dump_folder_path}"""					)
		processor.save_pretrained(snake_case__					)

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


if __name__ == "__main__":
							_lowerCAmelCase         =							argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--vit_name",
							    default="vit_base_r50_s16_384",
							    type=str,
							    help="Name of the hybrid ViT 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 upload the model to the HuggingFace hub."
							)

							_lowerCAmelCase         =							parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)

