



import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase=False				,		_lowerCAmelCase=False				,		_lowerCAmelCase="data2vec."):
					UpperCamelCase_											=				"backbone." if is_semantic else ""

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

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        (f"""{prefix}cls_token""", f"""{hf_prefix}embeddings.cls_token"""),
					        (f"""{prefix}patch_embed.proj.weight""", f"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
					        (f"""{prefix}patch_embed.proj.bias""", f"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
					    ])

					if has_lm_head:
										# mask token + shared relative position bias + layernorm
										rename_keys.extend(
										    [
										        ("mask_token", f"""{hf_prefix}embeddings.mask_token"""),
										        (
										            "rel_pos_bias.relative_position_bias_table",
										            f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
										        ),
										        (
										            "rel_pos_bias.relative_position_index",
										            f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
										        ),
										        ("norm.weight", "layernorm.weight"),
										        ("norm.bias", "layernorm.bias"),
										    ])
					elif is_semantic:
										# semantic segmentation classification heads
										rename_keys.extend(
										    [
										        ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
										        ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
										        ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
										        ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
										    ])
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ("fc_norm.weight", f"""{hf_prefix}pooler.layernorm.weight"""),
										        ("fc_norm.bias", f"""{hf_prefix}pooler.layernorm.bias"""),
										        ("head.weight", "classifier.weight"),
										        ("head.bias", "classifier.bias"),
										    ])

					return rename_keys



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase=False				,		_lowerCAmelCase=False				,		_lowerCAmelCase="data2vec_vision."):






					for i in range(config.num_hidden_layers):
										UpperCamelCase_											=				"backbone." if is_semantic else ""
										# queries, keys and values
										UpperCamelCase_											=				state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight""")
										UpperCamelCase_											=				state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias""")
										UpperCamelCase_											=				state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias""")

										UpperCamelCase_											=				in_proj_weight[
										    : config.hidden_size, :
										]
										UpperCamelCase_											=				q_bias
										UpperCamelCase_											=				in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										UpperCamelCase_											=				in_proj_weight[
										    -config.hidden_size :, :
										]
										UpperCamelCase_											=				v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										UpperCamelCase_											=				state_dict.pop(f"""{prefix}blocks.{i}.gamma_1""")
										UpperCamelCase_											=				state_dict.pop(f"""{prefix}blocks.{i}.gamma_2""")

										UpperCamelCase_											=				gamma_a
										UpperCamelCase_											=				gamma_a

										# relative_position bias table + index
										if not has_lm_head:
															# each layer has its own relative position bias
															UpperCamelCase_											=				state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table""")
															UpperCamelCase_											=				state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index""")

															UpperCamelCase_											=				table
															UpperCamelCase_											=				index



def 				_lowerCAmelCase      ():
					UpperCamelCase_											=				argparse.ArgumentParser(
					    "Convert Data2VecVision to HF for image classification and pretraining"				,		add_help=snake_case__)
					parser.add_argument("--hf_checkpoint_name"				,		type=snake_case__)
					parser.add_argument("--input_size"				,		default=2_24				,		type=snake_case__				,		help="images input size")
					parser.add_argument("--beit_checkpoint"				,		default=""				,		help="beit checkpoint")

					return parser.parse_args()



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase):






					def load_state_dict(_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase=""				,		_lowerCAmelCase="relative_position_index"):
										UpperCamelCase_											=				[]
										UpperCamelCase_											=				[]
										UpperCamelCase_											=				[]
										# copy state_dict so _load_from_state_dict can modify it
										UpperCamelCase_											=				getattr(snake_case__				,		"_metadata"				,		snake_case__)
										UpperCamelCase_											=				state_dict.copy()
										if metadata is not None:
															UpperCamelCase_											=				metadata

										def load(_lowerCAmelCase				,		_lowerCAmelCase=""):
															UpperCamelCase_											=				{} if metadata is None else metadata.get(prefix[:-1]				,		{})
															module._load_from_state_dict(
															    snake_case__				,		snake_case__				,		snake_case__				,		snake_case__				,		snake_case__				,		snake_case__				,		snake_case__)
															for name, child in module._modules.items():
																				if child is not None:
																									load(snake_case__				,		prefix + name + ".")

										load(snake_case__				,		prefix=snake_case__)

										UpperCamelCase_											=				[]
										UpperCamelCase_											=				[]
										for key in missing_keys:
															UpperCamelCase_											=				True
															for ignore_key in ignore_missing.split("|"):
																				if ignore_key in key:
																									UpperCamelCase_											=				False
																									break
															if keep_flag:
																				warn_missing_keys.append(snake_case__)
															else:
																				ignore_missing_keys.append(snake_case__)

										UpperCamelCase_											=				warn_missing_keys

										if len(snake_case__) > 0:
															print(
															    "Weights of {} not initialized from pretrained model: {}".format(
															        model.__class__.__name__				,		snake_case__))
										if len(snake_case__) > 0:
															print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__				,		snake_case__))
										if len(snake_case__) > 0:
															print(
															    "Ignored weights of {} not initialized from pretrained model: {}".format(
															        model.__class__.__name__				,		snake_case__))
										if len(snake_case__) > 0:
															print("\n".join(snake_case__))

					UpperCamelCase_											=				{
					    "pretrained": False,
					    "use_shared_rel_pos_bias": True,
					    "use_abs_pos_emb": False,
					    "init_values": 0.1,
					}

					if is_finetuned:
										model_kwargs.update(
										    {
										        "num_classes": 10_00,
										        "use_mean_pooling": True,
										        "init_scale": 0.001,
										        "use_rel_pos_bias": True,
										    })

					UpperCamelCase_											=				create_model(
					    "beit_large_patch16_224" if is_large else "beit_base_patch16_224"				,		**snake_case__				,		)
					UpperCamelCase_											=				model.patch_embed.patch_size
					UpperCamelCase_											=				(args.input_size // patch_size[0], args.input_size // patch_size[1])
					UpperCamelCase_											=				torch.load(args.beit_checkpoint				,		map_location="cpu")

					print(f"""Load ckpt from {args.beit_checkpoint}""")
					UpperCamelCase_											=				None
					for model_key in ("model", "module"):
										if model_key in checkpoint:
															UpperCamelCase_											=				checkpoint[model_key]
															print(f"""Load state_dict by model_key = {model_key}""")
															break

					UpperCamelCase_											=				list(checkpoint_model.keys())
					for key in all_keys:
										if "relative_position_index" in key:
															checkpoint_model.pop(snake_case__)

										if "relative_position_bias_table" in key:
															UpperCamelCase_											=				checkpoint_model[key]
															UpperCamelCase_					,      UpperCamelCase_											=				rel_pos_bias.size()
															UpperCamelCase_					,      UpperCamelCase_											=				model.state_dict()[key].size()
															UpperCamelCase_											=				model.patch_embed.patch_shape
															if dst_patch_shape[0] != dst_patch_shape[1]:
																				raise NotImplementedError()

					load_state_dict(snake_case__				,		snake_case__				,		prefix="")

					return model



def 				_lowerCAmelCase      ():
					UpperCamelCase_											=				get_args()

					UpperCamelCase_											=				"ft1k" in args.hf_checkpoint_name
					UpperCamelCase_											=				"large" in args.hf_checkpoint_name

					if is_finetuned:
										# To convert Beit's data2vec_vision to HF you need to copy
										# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
										# into this folder.
										import modeling_finetune  # noqa: F401
					else:
										# To convert Beit's data2vec_vision to HF you need to copy
										# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
										# into this folder
										# IMPORTANT: Note that for now we've only converted the down-stream
										# model and not the full pretrained model. This means for the integration
										# test you need to add a `return x` after the following line:
										# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
										# to make the integration test pass.
										import modeling_cyclical  # noqa: F401

					# 1. Create model config
					UpperCamelCase_											=				DataaVecVisionConfig()
					if is_finetuned:
										UpperCamelCase_											=				True
										UpperCamelCase_											=				False
										UpperCamelCase_											=				True
										UpperCamelCase_											=				10_00

										UpperCamelCase_											=				"huggingface/label-files"
										UpperCamelCase_											=				"imagenet-1k-id2label.json"
										UpperCamelCase_											=				json.load(open(hf_hub_download(snake_case__				,		snake_case__				,		repo_type="dataset")				,		"r"))
										UpperCamelCase_											=				{int(snake_case__): v for k, v in idalabel.items()}
										UpperCamelCase_											=				idalabel
										UpperCamelCase_											=				{v: k for k, v in idalabel.items()}
					else:
										UpperCamelCase_											=				False
										UpperCamelCase_											=				True
										UpperCamelCase_											=				False

					if is_large:
										UpperCamelCase_											=				10_24
										UpperCamelCase_											=				40_96
										UpperCamelCase_											=				24
										UpperCamelCase_											=				16

					# 2. Load Beit model
					UpperCamelCase_											=				load_beit_model(snake_case__				,		snake_case__				,		snake_case__)
					orig_model.eval()

					# 3. Forward Beit model
					UpperCamelCase_											=				BeitImageProcessor(size=config.image_size				,		do_center_crop=snake_case__)
					UpperCamelCase_											=				Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png")
					UpperCamelCase_											=				image_processor(images=snake_case__				,		return_tensors="pt")
					UpperCamelCase_											=				encoding["pixel_values"]

					UpperCamelCase_											=				(pixel_values,) if is_finetuned else (pixel_values, None)
					with torch.no_grad():
										UpperCamelCase_											=				orig_model(*snake_case__)

					# 4. Load HF Data2VecVision model
					if is_finetuned:
										UpperCamelCase_											=				DataaVecVisionForImageClassification(snake_case__)
										hf_model.eval()
										UpperCamelCase_											=				False
										UpperCamelCase_											=				"data2vec_vision."
					else:
										UpperCamelCase_											=				DataaVecVisionModel(snake_case__)
										hf_model.eval()
										UpperCamelCase_											=				True
										UpperCamelCase_											=				""

					UpperCamelCase_											=				create_rename_keys(snake_case__				,		hf_prefix=snake_case__				,		has_lm_head=snake_case__)
					UpperCamelCase_											=				orig_model.state_dict()
					for src, dest in rename_keys:
										UpperCamelCase_											=				state_dict.pop(snake_case__)
										UpperCamelCase_											=				val

					read_in_q_k_v(snake_case__				,		snake_case__				,		hf_prefix=snake_case__				,		has_lm_head=snake_case__)
					UpperCamelCase_					,      UpperCamelCase_											=				hf_model.load_state_dict(snake_case__				,		strict=snake_case__)
					print("HF missing"				,		snake_case__)
					print("HF unexpected_keys"				,		snake_case__)

					# 5. Forward HF Data2VecVision model
					with torch.no_grad():
										UpperCamelCase_											=				hf_model(snake_case__)

					UpperCamelCase_											=				hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

					# 6. Compare
					UpperCamelCase_											=				torch.max(torch.abs(hf_output - orig_model_output)).item()

					print(f"""max_absolute_diff = {max_absolute_diff}""")
					UpperCamelCase_											=				torch.allclose(snake_case__				,		snake_case__				,		atol=1e-3)
					print("Do both models output the same tensors?"				,		"🔥" if success else "💩")
					if not success:
										raise Exception("Something went wRoNg")

					# 7. Save
					print(f"""Saving to {args.hf_checkpoint_name}""")
					hf_model.save_pretrained(args.hf_checkpoint_name)
					image_processor.save_pretrained(args.hf_checkpoint_name)


if __name__ == "__main__":
	main()
	# Run the following to convert checkpoints
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./pretrained_base.pt \
	#          --hf_checkpoint_name "./data2vec-vision-base"
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./finetuned_base.pt \
	#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./pretrained_large.pt \
	#          --hf_checkpoint_name "./data2vec-vision-large"
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./finetuned_large.pt \
	#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax
	import jax.numpy as jnp

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
	import torch


class  _lowercase				:
				'''simple docstring'''







				def __init__(	self       ,	snake_case__       ,	snake_case__=14       ,	snake_case__=7       ,	snake_case__=True       ,	snake_case__=True       ,	snake_case__=False       ,	snake_case__=True       ,	snake_case__=99       ,	snake_case__=32       ,	snake_case__=5       ,	snake_case__=4       ,	snake_case__=37       ,	snake_case__="gelu"       ,	snake_case__=0.1       ,	snake_case__=0.1       ,	snake_case__=512       ,	snake_case__=0.02       ,	):

									'''simple docstring'''


									UpperCamelCase_											=				parent
									UpperCamelCase_											=				batch_size
									UpperCamelCase_											=				seq_length
									UpperCamelCase_											=				is_training
									UpperCamelCase_											=				use_input_mask
									UpperCamelCase_											=				use_token_type_ids
									UpperCamelCase_											=				use_labels
									UpperCamelCase_											=				vocab_size
									UpperCamelCase_											=				hidden_size
									UpperCamelCase_											=				num_hidden_layers
									UpperCamelCase_											=				num_attention_heads
									UpperCamelCase_											=				intermediate_size
									UpperCamelCase_											=				hidden_act
									UpperCamelCase_											=				hidden_dropout_prob
									UpperCamelCase_											=				attention_probs_dropout_prob
									UpperCamelCase_											=				max_position_embeddings
									UpperCamelCase_											=				initializer_range
									UpperCamelCase_											=				None
									UpperCamelCase_											=				vocab_size - 1
									UpperCamelCase_											=				vocab_size - 1
									UpperCamelCase_											=				vocab_size - 1

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

									UpperCamelCase_											=				None
									if self.use_input_mask:
														UpperCamelCase_											=				random_attention_mask([self.batch_size, self.seq_length]      )

									UpperCamelCase_											=				GPTaConfig(
									    vocab_size=self.vocab_size       ,	n_embd=self.hidden_size       ,	n_layer=self.num_hidden_layers       ,	n_head=self.num_attention_heads       ,	n_positions=self.max_position_embeddings       ,	use_cache=snake_case__       ,	bos_token_id=self.bos_token_id       ,	eos_token_id=self.eos_token_id       ,	pad_token_id=self.pad_token_id       ,	)

									return (config, input_ids, input_mask)

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.prepare_config_and_inputs()
									UpperCamelCase_					,      UpperCamelCase_					,      UpperCamelCase_											=				config_and_inputs
									UpperCamelCase_											=				{"input_ids": input_ids, "attention_mask": attention_mask}
									return config, inputs_dict

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_					,      UpperCamelCase_					,      UpperCamelCase_											=				self.prepare_config_and_inputs()

									UpperCamelCase_											=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
									UpperCamelCase_											=				ids_tensor([self.batch_size, self.seq_length]       ,	vocab_size=2      )

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				20
									UpperCamelCase_											=				model_class_name(snake_case__      )

									UpperCamelCase_											=				model.init_cache(input_ids.shape[0]       ,	snake_case__      )
									UpperCamelCase_											=				jnp.ones((input_ids.shape[0], max_decoder_length)       ,	dtype="i4"      )

									UpperCamelCase_											=				jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1      )[None, :]       ,	(input_ids.shape[0], input_ids.shape[-1] - 1)      )
									UpperCamelCase_											=				model(
									    input_ids[:, :-1]       ,	attention_mask=snake_case__       ,	past_key_values=snake_case__       ,	position_ids=snake_case__       ,	)

									UpperCamelCase_											=				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,	dtype="i4"      )
									UpperCamelCase_											=				model(
									    input_ids[:, -1:]       ,	attention_mask=snake_case__       ,	past_key_values=outputs_cache.past_key_values       ,	position_ids=snake_case__       ,	)

									UpperCamelCase_											=				model(snake_case__      )

									UpperCamelCase_											=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
									self.parent.assertTrue(diff < 1e-3       ,	msg=F"""Max diff is {diff}"""      )

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				20
									UpperCamelCase_											=				model_class_name(snake_case__      )

									UpperCamelCase_											=				jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])      )]       ,	axis=-1       ,	)

									UpperCamelCase_											=				model.init_cache(input_ids.shape[0]       ,	snake_case__      )
									UpperCamelCase_											=				jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1      )[None, :]       ,	(input_ids.shape[0], input_ids.shape[-1] - 1)      )

									UpperCamelCase_											=				model(
									    input_ids[:, :-1]       ,	attention_mask=snake_case__       ,	past_key_values=snake_case__       ,	position_ids=snake_case__       ,	)
									UpperCamelCase_											=				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,	dtype="i4"      )
									UpperCamelCase_											=				model(
									    input_ids[:, -1:]       ,	past_key_values=outputs_cache.past_key_values       ,	attention_mask=snake_case__       ,	position_ids=snake_case__       ,	)

									UpperCamelCase_											=				model(snake_case__       ,	attention_mask=snake_case__      )

									UpperCamelCase_											=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
									self.parent.assertTrue(diff < 1e-3       ,	msg=F"""Max diff is {diff}"""      )




@require_flax
class  _lowercase				(a_ ,				a_ ,				unittest.TestCase ):
				'''simple docstring'''







				lowercase__				  =  (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
				lowercase__				  =  (FlaxGPTaLMHeadModel,) if is_flax_available() else ()

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				FlaxGPTaModelTester(self      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for model_class_name in self.all_model_classes:
														UpperCamelCase_					,      UpperCamelCase_					,      UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward(snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for model_class_name in self.all_model_classes:
														UpperCamelCase_					,      UpperCamelCase_					,      UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward_with_attn_mask(
														    snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__      )

				@slow
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				GPTaTokenizer.from_pretrained("gpt2"       ,	pad_token="</s>"       ,	padding_side="left"      )
									UpperCamelCase_											=				tokenizer(["Hello this is a long string", "Hey"]       ,	return_tensors="np"       ,	padding=snake_case__       ,	truncation=snake_case__      )

									UpperCamelCase_											=				FlaxGPTaLMHeadModel.from_pretrained("gpt2"      )
									UpperCamelCase_											=				False
									UpperCamelCase_											=				model.config.eos_token_id

									UpperCamelCase_											=				jax.jit(model.generate      )

									UpperCamelCase_											=				jit_generate(inputs["input_ids"]       ,	attention_mask=inputs["attention_mask"]      ).sequences

									UpperCamelCase_											=				tokenizer.batch_decode(snake_case__       ,	skip_special_tokens=snake_case__      )

									UpperCamelCase_											=				[
									    "Hello this is a long string of words. I'm going to start with the first one.\n",
									    "Hey, I'm not sure if I'm going to be able to do",
									]

									self.assertListEqual(snake_case__       ,	snake_case__      )

				@is_pt_flax_cross_test
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_					,      UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__      ):
																			# prepare inputs
																			UpperCamelCase_											=				self._prepare_for_class(snake_case__       ,	snake_case__      )
																			UpperCamelCase_											=				{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			UpperCamelCase_											=				model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			UpperCamelCase_											=				getattr(snake_case__       ,	snake_case__      )

																			UpperCamelCase_					,      UpperCamelCase_											=				pt_inputs["input_ids"].shape
																			UpperCamelCase_											=				np.random.randint(0       ,	seq_length - 1       ,	size=(batch_size,)      )
																			for batch_idx, start_index in enumerate(snake_case__      ):
																								UpperCamelCase_											=				0
																								UpperCamelCase_											=				1
																								UpperCamelCase_											=				0
																								UpperCamelCase_											=				1
																			UpperCamelCase_											=				pt_model_class(snake_case__      ).eval()
																			UpperCamelCase_											=				model_class(snake_case__       ,	dtype=jnp.floataa      )

																			UpperCamelCase_											=				convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,	snake_case__      )
																			UpperCamelCase_											=				fx_state

																			with torch.no_grad():
																								UpperCamelCase_											=				pt_model(**snake_case__      ).to_tuple()

																			UpperCamelCase_											=				fx_model(**snake_case__      ).to_tuple()
																			self.assertEqual(len(snake_case__      )       ,	len(snake_case__      )       ,	"Output lengths differ between Flax and PyTorch"      )
																			for fx_output, pt_output in zip(snake_case__       ,	snake_case__      ):
																								self.assert_almost_equals(fx_output[:, -1]       ,	pt_output[:, -1].numpy()       ,	4e-2      )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																								pt_model.save_pretrained(snake_case__      )
																								UpperCamelCase_											=				model_class.from_pretrained(snake_case__       ,	from_pt=snake_case__      )

																			UpperCamelCase_											=				fx_model_loaded(**snake_case__      ).to_tuple()
																			self.assertEqual(
																			    len(snake_case__      )       ,	len(snake_case__      )       ,	"Output lengths differ between Flax and PyTorch"      )
																			for fx_output_loaded, pt_output in zip(snake_case__       ,	snake_case__      ):
																								self.assert_almost_equals(fx_output_loaded[:, -1]       ,	pt_output[:, -1].numpy()       ,	4e-2      )

				@is_pt_flax_cross_test
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_					,      UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__      ):
																			# prepare inputs
																			UpperCamelCase_											=				self._prepare_for_class(snake_case__       ,	snake_case__      )
																			UpperCamelCase_											=				{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			UpperCamelCase_											=				model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			UpperCamelCase_											=				getattr(snake_case__       ,	snake_case__      )

																			UpperCamelCase_											=				pt_model_class(snake_case__      ).eval()
																			UpperCamelCase_											=				model_class(snake_case__       ,	dtype=jnp.floataa      )

																			UpperCamelCase_											=				load_flax_weights_in_pytorch_model(snake_case__       ,	fx_model.params      )
																			UpperCamelCase_					,      UpperCamelCase_											=				pt_inputs["input_ids"].shape
																			UpperCamelCase_											=				np.random.randint(0       ,	seq_length - 1       ,	size=(batch_size,)      )
																			for batch_idx, start_index in enumerate(snake_case__      ):
																								UpperCamelCase_											=				0
																								UpperCamelCase_											=				1
																								UpperCamelCase_											=				0
																								UpperCamelCase_											=				1

																			# make sure weights are tied in PyTorch
																			pt_model.tie_weights()

																			with torch.no_grad():
																								UpperCamelCase_											=				pt_model(**snake_case__      ).to_tuple()

																			UpperCamelCase_											=				fx_model(**snake_case__      ).to_tuple()
																			self.assertEqual(len(snake_case__      )       ,	len(snake_case__      )       ,	"Output lengths differ between Flax and PyTorch"      )
																			for fx_output, pt_output in zip(snake_case__       ,	snake_case__      ):
																								self.assert_almost_equals(fx_output[:, -1]       ,	pt_output[:, -1].numpy()       ,	4e-2      )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																								fx_model.save_pretrained(snake_case__      )
																								UpperCamelCase_											=				pt_model_class.from_pretrained(snake_case__       ,	from_flax=snake_case__      )

																			with torch.no_grad():
																								UpperCamelCase_											=				pt_model_loaded(**snake_case__      ).to_tuple()

																			self.assertEqual(
																			    len(snake_case__      )       ,	len(snake_case__      )       ,	"Output lengths differ between Flax and PyTorch"      )
																			for fx_output, pt_output in zip(snake_case__       ,	snake_case__      ):
																								self.assert_almost_equals(fx_output[:, -1]       ,	pt_output[:, -1].numpy()       ,	4e-2      )

				@slow
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for model_class_name in self.all_model_classes:
														UpperCamelCase_											=				model_class_name.from_pretrained("gpt2"       ,	from_pt=snake_case__      )
														UpperCamelCase_											=				model(np.ones((1, 1)      )      )
														self.assertIsNotNone(snake_case__      )
