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 		snake_case__  (		lowercase       ,							lowercase=False       ,							lowercase=False       ,							lowercase="data2vec."				):

   '''simple docstring'''





   lowerCAmelCase_:       List[str]         =  '''backbone.''' if is_semantic else ''''''

   lowerCAmelCase_:       int         =  []
   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 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase=False       ,							lowercase=False       ,							lowercase="data2vec_vision."				):

   '''simple docstring'''





   for i in range(config.num_hidden_layers				):
      lowerCAmelCase_:       str         =  '''backbone.''' if is_semantic else ''''''
      # queries, keys and values
      lowerCAmelCase_:       Union[str, Any]         =  state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''				)
      lowerCAmelCase_:       Tuple         =  state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''				)
      lowerCAmelCase_:       Dict         =  state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''				)

      lowerCAmelCase_:       List[str]         =  in_proj_weight[
          : config.hidden_size, :
      ]
      lowerCAmelCase_:       Union[str, Any]         =  q_bias
      lowerCAmelCase_:       Dict         =  in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      lowerCAmelCase_:       Union[str, Any]         =  in_proj_weight[
          -config.hidden_size :, :
      ]
      lowerCAmelCase_:       Union[str, Any]         =  v_bias

      # gamma_1 and gamma_2
      # we call them lambda because otherwise they are renamed when using .from_pretrained
      lowerCAmelCase_:       List[Any]         =  state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''				)
      lowerCAmelCase_:       str         =  state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''				)

      lowerCAmelCase_:       Optional[Any]         =  gamma_a
      lowerCAmelCase_:       Optional[Any]         =  gamma_a

      # relative_position bias table + index
      if not has_lm_head:
         # each layer has its own relative position bias
         lowerCAmelCase_:       Optional[int]         =  state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''				)
         lowerCAmelCase_:       Optional[Any]         =  state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''				)

         lowerCAmelCase_:       Union[str, Any]         =  table
         lowerCAmelCase_:       List[str]         =  index
def 		snake_case__  (		):

   '''simple docstring'''





   lowerCAmelCase_:       Optional[Any]         =  argparse.ArgumentParser(
       "Convert Data2VecVision to HF for image classification and pretraining"       ,							add_help=UpperCAmelCase_				)
   parser.add_argument("--hf_checkpoint_name"       ,							type=UpperCAmelCase_				)
   parser.add_argument("--input_size"       ,							default=224       ,							type=UpperCAmelCase_       ,							help="images input size"				)
   parser.add_argument("--beit_checkpoint"       ,							default=""       ,							help="beit checkpoint"				)

   return parser.parse_args()
def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase				):

   '''simple docstring'''





   def load_state_dict(lowercase       ,							lowercase       ,							lowercase=""       ,							lowercase="relative_position_index"				):
      lowerCAmelCase_:       List[str]         =  []
      lowerCAmelCase_:       List[str]         =  []
      lowerCAmelCase_:       int         =  []
      # copy state_dict so _load_from_state_dict can modify it
      lowerCAmelCase_:       Union[str, Any]         =  getattr(UpperCAmelCase_       ,							"_metadata"       ,							UpperCAmelCase_				)
      lowerCAmelCase_:       List[str]         =  state_dict.copy()
      if metadata is not None:
         lowerCAmelCase_:       Optional[Any]         =  metadata

      def load(lowercase       ,							lowercase=""				):
         lowerCAmelCase_:       Union[str, Any]         =  {} if metadata is None else metadata.get(prefix[:-1]       ,							{}				)
         module._load_from_state_dict(
             UpperCAmelCase_       ,							UpperCAmelCase_       ,							UpperCAmelCase_       ,							UpperCAmelCase_       ,							UpperCAmelCase_       ,							UpperCAmelCase_       ,							UpperCAmelCase_				)
         for name, child in module._modules.items():
            if child is not None:
               load(UpperCAmelCase_       ,							prefix + name + "."				)

      load(UpperCAmelCase_       ,							prefix=UpperCAmelCase_				)

      lowerCAmelCase_:       List[str]         =  []
      lowerCAmelCase_:       Union[str, Any]         =  []
      for key in missing_keys:
         lowerCAmelCase_:       Union[str, Any]         =  True
         for ignore_key in ignore_missing.split("|"				):
            if ignore_key in key:
               lowerCAmelCase_:       int         =  False
               break
         if keep_flag:
            warn_missing_keys.append(UpperCAmelCase_				)
         else:
            ignore_missing_keys.append(UpperCAmelCase_				)

      lowerCAmelCase_:       List[Any]         =  warn_missing_keys

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

   lowerCAmelCase_:       int         =  {
       '''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": 1000,
              "use_mean_pooling": True,
              "init_scale": 0.001,
              "use_rel_pos_bias": True,
          }				)

   lowerCAmelCase_:       List[Any]         =  create_model(
       "beit_large_patch16_224" if is_large else "beit_base_patch16_224"       ,							**UpperCAmelCase_       ,							)
   lowerCAmelCase_:       Dict         =  model.patch_embed.patch_size
   lowerCAmelCase_:       Optional[int]         =  (args.input_size // patch_size[0], args.input_size // patch_size[1])
   lowerCAmelCase_:       Union[str, Any]         =  torch.load(args.beit_checkpoint       ,							map_location="cpu"				)

   print(F'''Load ckpt from {args.beit_checkpoint}'''				)
   lowerCAmelCase_:       List[Any]         =  None
   for model_key in ("model", "module"):
      if model_key in checkpoint:
         lowerCAmelCase_:       Union[str, Any]         =  checkpoint[model_key]
         print(F'''Load state_dict by model_key = {model_key}'''				)
         break

   lowerCAmelCase_:       int         =  list(checkpoint_model.keys()				)
   for key in all_keys:
      if "relative_position_index" in key:
         checkpoint_model.pop(UpperCAmelCase_				)

      if "relative_position_bias_table" in key:
         lowerCAmelCase_:       List[str]         =  checkpoint_model[key]
         lowerCAmelCase_:       Optional[Any]         =  rel_pos_bias.size()
         lowerCAmelCase_:       Union[str, Any]         =  model.state_dict()[key].size()
         lowerCAmelCase_:       int         =  model.patch_embed.patch_shape
         if dst_patch_shape[0] != dst_patch_shape[1]:
            raise NotImplementedError()

   load_state_dict(UpperCAmelCase_       ,							UpperCAmelCase_       ,							prefix=""				)

   return model
def 		snake_case__  (		):

   '''simple docstring'''





   lowerCAmelCase_:       Optional[Any]         =  get_args()

   lowerCAmelCase_:       Optional[int]         =  '''ft1k''' in args.hf_checkpoint_name
   lowerCAmelCase_:       Any         =  '''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
   lowerCAmelCase_:       List[Any]         =  DataaVecVisionConfig()
   if is_finetuned:
      lowerCAmelCase_:       Optional[Any]         =  True
      lowerCAmelCase_:       Optional[int]         =  False
      lowerCAmelCase_:       List[str]         =  True
      lowerCAmelCase_:       str         =  1000

      lowerCAmelCase_:       Union[str, Any]         =  '''huggingface/label-files'''
      lowerCAmelCase_:       Tuple         =  '''imagenet-1k-id2label.json'''
      lowerCAmelCase_:       List[Any]         =  json.load(open(hf_hub_download(UpperCAmelCase_       ,							UpperCAmelCase_       ,							repo_type="dataset"				)       ,							"r"				)				)
      lowerCAmelCase_:       List[Any]         =  {int(UpperCAmelCase_				): v for k, v in idalabel.items()}
      lowerCAmelCase_:       int         =  idalabel
      lowerCAmelCase_:       int         =  {v: k for k, v in idalabel.items()}
   else:
      lowerCAmelCase_:       Optional[int]         =  False
      lowerCAmelCase_:       Tuple         =  True
      lowerCAmelCase_:       Optional[Any]         =  False

   if is_large:
      lowerCAmelCase_:       Tuple         =  1024
      lowerCAmelCase_:       Optional[int]         =  4096
      lowerCAmelCase_:       Optional[int]         =  24
      lowerCAmelCase_:       Any         =  16

   # 2. Load Beit model
   lowerCAmelCase_:       Tuple         =  load_beit_model(UpperCAmelCase_       ,							UpperCAmelCase_       ,							UpperCAmelCase_				)
   orig_model.eval()

   # 3. Forward Beit model
   lowerCAmelCase_:       Optional[int]         =  BeitImageProcessor(size=config.image_size       ,							do_center_crop=UpperCAmelCase_				)
   lowerCAmelCase_:       str         =  Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png"				)
   lowerCAmelCase_:       Dict         =  image_processor(images=UpperCAmelCase_       ,							return_tensors="pt"				)
   lowerCAmelCase_:       Optional[Any]         =  encoding['''pixel_values''']

   lowerCAmelCase_:       Optional[Any]         =  (pixel_values,) if is_finetuned else (pixel_values, None)
   with torch.no_grad():
      lowerCAmelCase_:       Any         =  orig_model(*UpperCAmelCase_				)

   # 4. Load HF Data2VecVision model
   if is_finetuned:
      lowerCAmelCase_:       Dict         =  DataaVecVisionForImageClassification(UpperCAmelCase_				)
      hf_model.eval()
      lowerCAmelCase_:       List[str]         =  False
      lowerCAmelCase_:       int         =  '''data2vec_vision.'''
   else:
      lowerCAmelCase_:       List[str]         =  DataaVecVisionModel(UpperCAmelCase_				)
      hf_model.eval()
      lowerCAmelCase_:       Optional[int]         =  True
      lowerCAmelCase_:       int         =  ''''''

   lowerCAmelCase_:       Optional[int]         =  create_rename_keys(UpperCAmelCase_       ,							hf_prefix=UpperCAmelCase_       ,							has_lm_head=UpperCAmelCase_				)
   lowerCAmelCase_:       Union[str, Any]         =  orig_model.state_dict()
   for src, dest in rename_keys:
      lowerCAmelCase_:       str         =  state_dict.pop(UpperCAmelCase_				)
      lowerCAmelCase_:       str         =  val

   read_in_q_k_v(UpperCAmelCase_       ,							UpperCAmelCase_       ,							hf_prefix=UpperCAmelCase_       ,							has_lm_head=UpperCAmelCase_				)
   lowerCAmelCase_:       Dict         =  hf_model.load_state_dict(UpperCAmelCase_       ,							strict=UpperCAmelCase_				)
   print("HF missing"       ,							UpperCAmelCase_				)
   print("HF unexpected_keys"       ,							UpperCAmelCase_				)

   # 5. Forward HF Data2VecVision model
   with torch.no_grad():
      lowerCAmelCase_:       List[Any]         =  hf_model(UpperCAmelCase_				)

   lowerCAmelCase_:       List[Any]         =  hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

   # 6. Compare
   lowerCAmelCase_:       Any         =  torch.max(torch.abs(hf_output - orig_model_output				)				).item()

   print(F'''max_absolute_diff = {max_absolute_diff}'''				)
   lowerCAmelCase_:       Optional[int]         =  torch.allclose(UpperCAmelCase_       ,							UpperCAmelCase_       ,							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					,	lowerCamelCase__					,	lowerCamelCase__=14					,	lowerCamelCase__=7					,	lowerCamelCase__=True					,	lowerCamelCase__=True					,	lowerCamelCase__=False					,	lowerCamelCase__=True					,	lowerCamelCase__=99					,	lowerCamelCase__=32					,	lowerCamelCase__=5					,	lowerCamelCase__=4					,	lowerCamelCase__=37					,	lowerCamelCase__="gelu"					,	lowerCamelCase__=0.1					,	lowerCamelCase__=0.1					,	lowerCamelCase__=512					,	lowerCamelCase__=0.0_2					,	):
       lowerCAmelCase_:       int         =  parent
       lowerCAmelCase_:       Tuple         =  batch_size
       lowerCAmelCase_:       int         =  seq_length
       lowerCAmelCase_:       Any         =  is_training
       lowerCAmelCase_:       Any         =  use_input_mask
       lowerCAmelCase_:       Optional[Any]         =  use_token_type_ids
       lowerCAmelCase_:       str         =  use_labels
       lowerCAmelCase_:       Tuple         =  vocab_size
       lowerCAmelCase_:       List[Any]         =  hidden_size
       lowerCAmelCase_:       Optional[int]         =  num_hidden_layers
       lowerCAmelCase_:       List[str]         =  num_attention_heads
       lowerCAmelCase_:       int         =  intermediate_size
       lowerCAmelCase_:       Tuple         =  hidden_act
       lowerCAmelCase_:       Dict         =  hidden_dropout_prob
       lowerCAmelCase_:       Dict         =  attention_probs_dropout_prob
       lowerCAmelCase_:       Any         =  max_position_embeddings
       lowerCAmelCase_:       Dict         =  initializer_range
       lowerCAmelCase_:       List[Any]         =  None
       lowerCAmelCase_:       List[Any]         =  vocab_size - 1
       lowerCAmelCase_:       Union[str, Any]         =  vocab_size - 1
       lowerCAmelCase_:       int         =  vocab_size - 1




    def     _a						(						self   ):
       lowerCAmelCase_:       Tuple         =  ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size   )

       lowerCAmelCase_:       Any         =  None
       if self.use_input_mask:
          lowerCAmelCase_:       Union[str, Any]         =  random_attention_mask([self.batch_size, self.seq_length]   )

       lowerCAmelCase_:       Union[str, Any]         =  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=lowerCamelCase__					,	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     _a						(						self   ):
       lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()
       lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  config_and_inputs
       lowerCAmelCase_:       List[str]         =  {"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict




    def     _a						(						self   ):
       lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()

       lowerCAmelCase_:       List[str]         =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
       lowerCAmelCase_:       Tuple         =  ids_tensor([self.batch_size, self.seq_length]					,	vocab_size=2   )

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




    def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
       lowerCAmelCase_:       Optional[Any]         =  20
       lowerCAmelCase_:       Union[str, Any]         =  model_class_name(lowerCamelCase__   )

       lowerCAmelCase_:       List[str]         =  model.init_cache(input_ids.shape[0]					,	lowerCamelCase__   )
       lowerCAmelCase_:       Any         =  jnp.ones((input_ids.shape[0], max_decoder_length)					,	dtype="i4"   )

       lowerCAmelCase_:       Union[str, Any]         =  jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )
       lowerCAmelCase_:       Optional[Any]         =  model(
           input_ids[:, :-1]					,	attention_mask=lowerCamelCase__					,	past_key_values=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)

       lowerCAmelCase_:       Any         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
       lowerCAmelCase_:       Optional[int]         =  model(
           input_ids[:, -1:]					,	attention_mask=lowerCamelCase__					,	past_key_values=outputs_cache.past_key_values					,	position_ids=lowerCamelCase__					,	)

       lowerCAmelCase_:       List[Any]         =  model(lowerCamelCase__   )

       lowerCAmelCase_:       List[Any]         =  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     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
       lowerCAmelCase_:       Union[str, Any]         =  20
       lowerCAmelCase_:       List[str]         =  model_class_name(lowerCamelCase__   )

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

       lowerCAmelCase_:       str         =  model.init_cache(input_ids.shape[0]					,	lowerCamelCase__   )
       lowerCAmelCase_:       List[Any]         =  jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )

       lowerCAmelCase_:       List[Any]         =  model(
           input_ids[:, :-1]					,	attention_mask=lowerCamelCase__					,	past_key_values=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)
       lowerCAmelCase_:       Dict         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
       lowerCAmelCase_:       Dict         =  model(
           input_ids[:, -1:]					,	past_key_values=outputs_cache.past_key_values					,	attention_mask=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)

       lowerCAmelCase_:       Union[str, Any]         =  model(lowerCamelCase__					,	attention_mask=lowerCamelCase__   )

       lowerCAmelCase_:       List[str]         =  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						(    UpperCAmelCase__   ,     UpperCAmelCase__   ,     unittest.TestCase ):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							Optional[int]        =						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
    SCREAMING_SNAKE_CASE:							Union[str, Any]        =						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()




    def     _a						(						self   ):
       lowerCAmelCase_:       Any         =  FlaxGPTaModelTester(self   )




    def     _a						(						self   ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )




    def     _a						(						self   ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[Any]         =  self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward_with_attn_mask(
              lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )




    @slow
    def     _a						(						self   ):
       lowerCAmelCase_:       Any         =  GPTaTokenizer.from_pretrained("gpt2"					,	pad_token="</s>"					,	padding_side="left"   )
       lowerCAmelCase_:       Optional[int]         =  tokenizer(["Hello this is a long string", "Hey"]					,	return_tensors="np"					,	padding=lowerCamelCase__					,	truncation=lowerCamelCase__   )

       lowerCAmelCase_:       List[Any]         =  FlaxGPTaLMHeadModel.from_pretrained("gpt2"   )
       lowerCAmelCase_:       List[str]         =  False
       lowerCAmelCase_:       int         =  model.config.eos_token_id

       lowerCAmelCase_:       Optional[int]         =  jax.jit(model.generate   )

       lowerCAmelCase_:       Any         =  jit_generate(inputs["input_ids"]					,	attention_mask=inputs["attention_mask"]   ).sequences

       lowerCAmelCase_:       Any         =  tokenizer.batch_decode(lowerCamelCase__					,	skip_special_tokens=lowerCamelCase__   )

       lowerCAmelCase_:       Dict         =  [
           "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(lowerCamelCase__					,	lowerCamelCase__   )




    @is_pt_flax_cross_test
    def     _a						(						self   ):
       lowerCAmelCase_     ,  lowerCAmelCase_:       Union[str, Any]         =  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
             lowerCAmelCase_:       Any         =  self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )
             lowerCAmelCase_:       str         =  {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCAmelCase_:       Dict         =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCAmelCase_:       Union[str, Any]         =  getattr(lowerCamelCase__					,	lowerCamelCase__   )

             lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  pt_inputs["input_ids"].shape
             lowerCAmelCase_:       str         =  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)   )
             for batch_idx, start_index in enumerate(lowerCamelCase__   ):
                lowerCAmelCase_:       List[str]         =  0
                lowerCAmelCase_:       int         =  1
                lowerCAmelCase_:       List[Any]         =  0
                lowerCAmelCase_:       List[Any]         =  1
             lowerCAmelCase_:       Tuple         =  pt_model_class(lowerCamelCase__   ).eval()
             lowerCAmelCase_:       Tuple         =  model_class(lowerCamelCase__					,	dtype=jnp.floataa   )

             lowerCAmelCase_:       int         =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,	lowerCamelCase__   )
             lowerCAmelCase_:       Optional[int]         =  fx_state

             with torch.no_grad():
                lowerCAmelCase_:       Union[str, Any]         =  pt_model(**lowerCamelCase__   ).to_tuple()

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

             with tempfile.TemporaryDirectory() as tmpdirname:
                pt_model.save_pretrained(lowerCamelCase__   )
                lowerCAmelCase_:       Dict         =  model_class.from_pretrained(lowerCamelCase__					,	from_pt=lowerCamelCase__   )

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




    @is_pt_flax_cross_test
    def     _a						(						self   ):
       lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  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
             lowerCAmelCase_:       Any         =  self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )
             lowerCAmelCase_:       int         =  {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCAmelCase_:       List[str]         =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCAmelCase_:       Dict         =  getattr(lowerCamelCase__					,	lowerCamelCase__   )

             lowerCAmelCase_:       Dict         =  pt_model_class(lowerCamelCase__   ).eval()
             lowerCAmelCase_:       Optional[Any]         =  model_class(lowerCamelCase__					,	dtype=jnp.floataa   )

             lowerCAmelCase_:       Optional[Any]         =  load_flax_weights_in_pytorch_model(lowerCamelCase__					,	fx_model.params   )
             lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  pt_inputs["input_ids"].shape
             lowerCAmelCase_:       Optional[Any]         =  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)   )
             for batch_idx, start_index in enumerate(lowerCamelCase__   ):
                lowerCAmelCase_:       Dict         =  0
                lowerCAmelCase_:       List[Any]         =  1
                lowerCAmelCase_:       Dict         =  0
                lowerCAmelCase_:       Optional[int]         =  1

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

             with torch.no_grad():
                lowerCAmelCase_:       Any         =  pt_model(**lowerCamelCase__   ).to_tuple()

             lowerCAmelCase_:       Union[str, Any]         =  fx_model(**lowerCamelCase__   ).to_tuple()
             self.assertEqual(len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
             for fx_output, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                self.assert_almost_equals(fx_output[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )

             with tempfile.TemporaryDirectory() as tmpdirname:
                fx_model.save_pretrained(lowerCamelCase__   )
                lowerCAmelCase_:       str         =  pt_model_class.from_pretrained(lowerCamelCase__					,	from_flax=lowerCamelCase__   )

             with torch.no_grad():
                lowerCAmelCase_:       int         =  pt_model_loaded(**lowerCamelCase__   ).to_tuple()

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




    @slow
    def     _a						(						self   ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_:       Tuple         =  model_class_name.from_pretrained("gpt2"					,	from_pt=lowerCamelCase__   )
          lowerCAmelCase_:       int         =  model(np.ones((1, 1)   )   )
          self.assertIsNotNone(lowerCamelCase__   )