




from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather




@overload
def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_					)				->		torch.Tensor:

    """simple docstring"""


    ...




@overload
def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_					)				->		Tuple[torch.Tensor, torch.Tensor]:

    """simple docstring"""


    ...




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_					)				->		Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase     						=  aatype == rc.restype_order['''G''']
    __UpperCamelCase     						=  rc.atom_order['''CA''']
    __UpperCamelCase     						=  rc.atom_order['''CB''']
    __UpperCamelCase     						=  torch.where(
        is_gly[..., None].expand(*((-1,) * len(is_gly.shape					))    ,   3					)    ,   all_atom_positions[..., ca_idx, :]    ,   all_atom_positions[..., cb_idx, :]    ,   )

    if all_atom_masks is not None:
        __UpperCamelCase     						=  torch.where(
            lowercase_    ,   all_atom_masks[..., ca_idx]    ,   all_atom_masks[..., cb_idx]    ,   )
        return pseudo_beta, pseudo_beta_mask
    else:
        return pseudo_beta




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		torch.Tensor:

    """simple docstring"""


    __UpperCamelCase     						=  batched_gather(
        lowercase_    ,   batch['''residx_atom37_to_atom14''']    ,   dim=-2    ,   no_batch_dims=len(atomaa.shape[:-2]					)    ,   )

    __UpperCamelCase     						=  atomaa_data * batch['''atom37_atom_exists'''][..., None]

    return atomaa_data




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		torch.Tensor:

    """simple docstring"""


    __UpperCamelCase     						=  template_feats['''template_aatype''']
    __UpperCamelCase     						=  template_feats['''template_torsion_angles_sin_cos''']
    __UpperCamelCase     						=  template_feats['''template_alt_torsion_angles_sin_cos''']
    __UpperCamelCase     						=  template_feats['''template_torsion_angles_mask''']
    __UpperCamelCase     						=  torch.cat(
        [
            nn.functional.one_hot(lowercase_    ,   22					),
            torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]    ,   14					),
            alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]    ,   14					),
            torsion_angles_mask,
        ]    ,   dim=-1    ,   )

    return template_angle_feat




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_ = False    ,   lowercase_ = 1E-20    ,   lowercase_ = 1E8    ,   )				->		torch.Tensor:

    """simple docstring"""


    __UpperCamelCase     						=  batch['''template_pseudo_beta_mask''']
    __UpperCamelCase     						=  template_mask[..., None] * template_mask[..., None, :]

    # Compute distogram (this seems to differ slightly from Alg. 5)
    __UpperCamelCase     						=  batch['''template_pseudo_beta''']
    __UpperCamelCase     						=  torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2    ,   dim=-1    ,   keepdim=lowercase_					)
    __UpperCamelCase     						=  torch.linspace(lowercase_    ,   lowercase_    ,   lowercase_    ,   device=tpb.device					) ** 2
    __UpperCamelCase     						=  torch.cat([lower[1:], lower.new_tensor([inf]					)]    ,   dim=-1					)
    __UpperCamelCase     						=  ((dgram > lower) * (dgram < upper)).type(dgram.dtype					)

    __UpperCamelCase     						=  [dgram, template_mask_ad[..., None]]

    __UpperCamelCase     						=  nn.functional.one_hot(
        batch['''template_aatype''']    ,   rc.restype_num + 2    ,   )

    __UpperCamelCase     						=  batch['''template_aatype'''].shape[-1]
    to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]    ,   lowercase_    ,   -1    ,   -1					)					)
    to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]    ,   -1    ,   lowercase_    ,   -1					)					)

    __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  [rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
    __UpperCamelCase     						=  Rigid.make_transform_from_reference(
        n_xyz=batch['''template_all_atom_positions'''][..., n, :]    ,   ca_xyz=batch['''template_all_atom_positions'''][..., ca, :]    ,   c_xyz=batch['''template_all_atom_positions'''][..., c, :]    ,   eps=lowercase_    ,   )
    __UpperCamelCase     						=  rigids.get_trans()[..., None, :, :]
    __UpperCamelCase     						=  rigids[..., None].invert_apply(lowercase_					)

    __UpperCamelCase     						=  torch.rsqrt(eps + torch.sum(rigid_vec**2    ,   dim=-1					)					)

    __UpperCamelCase     						=  batch['''template_all_atom_mask''']
    __UpperCamelCase     						=  t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
    __UpperCamelCase     						=  template_mask[..., None] * template_mask[..., None, :]

    __UpperCamelCase     						=  inv_distance_scalar * template_mask_ad
    __UpperCamelCase     						=  rigid_vec * inv_distance_scalar[..., None]

    if not use_unit_vector:
        __UpperCamelCase     						=  unit_vector * 0.0

    to_concat.extend(torch.unbind(unit_vector[..., None, :]    ,   dim=-1					)					)
    to_concat.append(template_mask_ad[..., None]					)

    __UpperCamelCase     						=  torch.cat(lowercase_    ,   dim=-1					)
    __UpperCamelCase     						=  act * template_mask_ad[..., None]

    return act




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		torch.Tensor:

    """simple docstring"""


    __UpperCamelCase     						=  nn.functional.one_hot(batch['''extra_msa''']    ,   23					)
    __UpperCamelCase     						=  [
        msa_ahot,
        batch['''extra_has_deletion'''].unsqueeze(-1					),
        batch['''extra_deletion_value'''].unsqueeze(-1					),
    ]
    return torch.cat(lowercase_    ,   dim=-1					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_    ,   )				->		Rigid:

    """simple docstring"""


    __UpperCamelCase     						=  rrgdf[aatype, ...]

    # [*, N, 8] transformations, i.e.
    #   One [*, N, 8, 3, 3] rotation matrix and
    #   One [*, N, 8, 3]    translation matrix
    __UpperCamelCase     						=  r.from_tensor_axa(lowercase_					)

    __UpperCamelCase     						=  alpha.new_zeros((*((1,) * len(alpha.shape[:-1]					)), 2)					)
    __UpperCamelCase     						=  1

    # [*, N, 8, 2]
    __UpperCamelCase     						=  torch.cat([bb_rot.expand(*alpha.shape[:-2]    ,   -1    ,   -1					), alpha]    ,   dim=-2					)

    # [*, N, 8, 3, 3]
    # Produces rotation matrices of the form:
    # [
    #   [1, 0  , 0  ],
    #   [0, a_2,-a_1],
    #   [0, a_1, a_2]
    # ]
    # This follows the original code rather than the supplement, which uses
    # different indices.

    __UpperCamelCase     						=  alpha.new_zeros(default_r.get_rots().get_rot_mats().shape					)
    __UpperCamelCase     						=  1
    __UpperCamelCase     						=  alpha[..., 1]
    __UpperCamelCase     						=  -alpha[..., 0]
    __UpperCamelCase     						=  alpha

    __UpperCamelCase     						=  default_r.compose(Rigid(Rotation(rot_mats=lowercase_					)    ,   lowercase_					)					)

    __UpperCamelCase     						=  all_frames[..., 5]
    __UpperCamelCase     						=  all_frames[..., 6]
    __UpperCamelCase     						=  all_frames[..., 7]

    __UpperCamelCase     						=  all_frames[..., 4]
    __UpperCamelCase     						=  chia_frame_to_bb.compose(lowercase_					)
    __UpperCamelCase     						=  chia_frame_to_bb.compose(lowercase_					)
    __UpperCamelCase     						=  chia_frame_to_bb.compose(lowercase_					)

    __UpperCamelCase     						=  Rigid.cat(
        [
            all_frames[..., :5],
            chia_frame_to_bb.unsqueeze(-1					),
            chia_frame_to_bb.unsqueeze(-1					),
            chia_frame_to_bb.unsqueeze(-1					),
        ]    ,   dim=-1    ,   )

    __UpperCamelCase     						=  r[..., None].compose(lowercase_					)

    return all_frames_to_global




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_    ,   )				->		torch.Tensor:

    """simple docstring"""


    __UpperCamelCase     						=  group_idx[aatype, ...]

    # [*, N, 14, 8]
    __UpperCamelCase     						=  nn.functional.one_hot(
        lowercase_    ,   num_classes=default_frames.shape[-3]    ,   )

    # [*, N, 14, 8]
    __UpperCamelCase     						=  r[..., None, :] * group_mask_one_hot

    # [*, N, 14]
    __UpperCamelCase     						=  t_atoms_to_global.map_tensor_fn(lambda lowercase_					: torch.sum(lowercase_    ,   dim=-1					)					)

    # [*, N, 14, 1]
    __UpperCamelCase     						=  atom_mask[aatype, ...].unsqueeze(-1					)

    # [*, N, 14, 3]
    __UpperCamelCase     						=  lit_positions[aatype, ...]
    __UpperCamelCase     						=  t_atoms_to_global.apply(lowercase_					)
    __UpperCamelCase     						=  pred_positions * atom_mask

    return pred_positions












import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
 import torch

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       Any       ,				snake_case    :       Union[str, Any]=14       ,				snake_case    :       Optional[Any]=7       ,				snake_case    :       Tuple=True       ,				snake_case    :       int=True       ,				snake_case    :       Dict=False       ,				snake_case    :       List[Any]=True       ,				snake_case    :       Optional[int]=99       ,				snake_case    :       Tuple=32       ,				snake_case    :       Any=4       ,				snake_case    :       List[Any]=4       ,				snake_case    :       int=[[["global", "local"], 2]]       ,				snake_case    :       Optional[Any]=37       ,				snake_case    :       Optional[Any]="gelu"       ,				snake_case    :       Dict=0.1       ,				snake_case    :       List[Any]=0.1       ,				snake_case    :       Union[str, Any]=512       ,				snake_case    :       int=7       ,				snake_case    :       Any=0.02       ,				):
          __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     						=  attention_types
          __UpperCamelCase     						=  intermediate_size
          __UpperCamelCase     						=  hidden_act
          __UpperCamelCase     						=  hidden_dropout_prob
          __UpperCamelCase     						=  attention_probs_dropout_prob
          __UpperCamelCase     						=  max_position_embeddings
          __UpperCamelCase     						=  window_size
          __UpperCamelCase     						=  initializer_range
          __UpperCamelCase     						=  None
          __UpperCamelCase     						=  vocab_size - 1
          __UpperCamelCase     						=  vocab_size - 1
          __UpperCamelCase     						=  vocab_size - 1







      def 					snake_case		(   self    :       Union[str, Any] ):
          __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     						=  GPTNeoConfig(
              vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				num_layers=self.num_hidden_layers       ,				num_heads=self.num_attention_heads       ,				max_position_embeddings=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       ,				window_size=self.window_size       ,				attention_types=self.attention_types       ,				)

          return (config, input_ids, input_mask)







      def 					snake_case		(   self    :       Tuple ):
          __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 					snake_case		(   self    :       List[Any]       ,				snake_case    :       int       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Optional[int]       ,				snake_case    :       Optional[int] ):
          __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 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       Dict       ,				snake_case    :       Dict       ,				snake_case    :       Optional[Any]       ,				snake_case    :       Tuple ):
          __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        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       Dict			=    (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      lowerCAmelCase__	:       Any			=    (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  FlaxGPTNeoModelTester(self )







      def 					snake_case		(   self    :       int ):
          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 					snake_case		(   self    :       List[str] ):
          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 					snake_case		(   self    :       Union[str, Any] ):
          __UpperCamelCase     						=  GPTaTokenizer.from_pretrained('''gpt2'''       ,				pad_token='''<|endoftext|>'''       ,				padding_side='''left''' )
          __UpperCamelCase     						=  tokenizer(['''Hello this is a long string''', '''Hey''']       ,				return_tensors='''np'''       ,				padding=snake_case       ,				truncation=snake_case )

          __UpperCamelCase     						=  FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''' )
          __UpperCamelCase     						=  False
          __UpperCamelCase     						=  model.config.eos_token_id

          __UpperCamelCase     						=  jax.jit(model.generate )

          __UpperCamelCase     						=  jit_generate(
              inputs['''input_ids''']       ,				attention_mask=inputs['''attention_mask''']       ,				pad_token_id=tokenizer.pad_token_id ).sequences

          __UpperCamelCase     						=  tokenizer.batch_decode(snake_case       ,				skip_special_tokens=snake_case )

          __UpperCamelCase     						=  [
              '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
              '''Hey, I\'m a little late to the party. I\'m going to''',
          ]

          self.assertListEqual(snake_case       ,				snake_case )







      @is_pt_flax_cross_test
      def 					snake_case		(   self    :       Dict ):
          __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 					snake_case		(   self    :       Optional[Any] ):
          __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 					snake_case		(   self    :       Tuple ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase     						=  model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''' )
              __UpperCamelCase     						=  model(np.ones((1, 1) ) )
              self.assertIsNotNone(snake_case )






