

import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


_lowerCamelCase     :		Union[str, Any]								=       Mapping[str, np.ndarray]
_lowerCamelCase     :		Dict								=       Mapping[str, Any]  # Is a nested dict.
_lowerCamelCase     :		List[str]								=       0.01


@dataclasses.dataclass(frozen=_lowercase					)
class     UpperCamelCase_   :

    '''simple docstring'''






    UpperCAmelCase__							  =				42  # [num_res, num_atom_type, 3]

    # Amino-acid type for each residue represented as an integer between 0 and
    # 20, where 20 is 'X'.
    UpperCAmelCase__							  =				42  # [num_res]

    # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
    # is present and 0.0 if not. This should be used for loss masking.
    UpperCAmelCase__							  =				42  # [num_res, num_atom_type]

    # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
    UpperCAmelCase__							  =				42  # [num_res]

    # B-factors, or temperature factors, of each residue (in sq. angstroms units),
    # representing the displacement of the residue from its ground truth mean
    # value.
    UpperCAmelCase__							  =				42  # [num_res, num_atom_type]

    # Chain indices for multi-chain predictions
    UpperCAmelCase__							  =				None

    # Optional remark about the protein. Included as a comment in output PDB
    # files
    UpperCAmelCase__							  =				None

    # Templates used to generate this protein (prediction-only)
    UpperCAmelCase__							  =				None

    # Chain corresponding to each parent
    UpperCAmelCase__							  =				None







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Protein:



       """simple docstring"""

       A__							=   R'''(\[[A-Z]+\]\n)'''
       A__							=   [tag.strip() for tag in re.split(a__			,    a__						) if len(a__						) > 0]
       A__							=   zip(tags[0::2]			,    [l.split('''\n'''						) for l in tags[1::2]]						)

       A__							=   ['''N''', '''CA''', '''C''']
       A__							=   None
       A__							=   None
       A__							=   None
       for g in groups:
              if "[PRIMARY]" == g[0]:
                     A__							=   g[1][0].strip()
                     for i in range(len(a__						)						):
                            if seq[i] not in residue_constants.restypes:
                                   A__							=   '''X'''  # FIXME: strings are immutable
                     A__							=   np.array(
                         [residue_constants.restype_order.get(a__			,    residue_constants.restype_num						) for res_symbol in seq]						)
              elif "[TERTIARY]" == g[0]:
                     A__							=   []
                     for axis in range(3						):
                            tertiary.append(list(map(a__			,    g[1][axis].split()						)						)						)
                     A__							=   np.array(a__						)
                     A__							=   np.zeros((len(tertiary[0]						) // 3, residue_constants.atom_type_num, 3)						).astype(np.floataa						)
                     for i, atom in enumerate(a__						):
                            A__							=   np.transpose(tertiary_np[:, i::3]						)
                     atom_positions *= PICO_TO_ANGSTROM
              elif "[MASK]" == g[0]:
                     A__							=   np.array(list(map({'''-''': 0, '''+''': 1}.get			,    g[1][0].strip()						)						)						)
                     A__							=   np.zeros(
                         (
                             len(a__						),
                             residue_constants.atom_type_num,
                         )						).astype(np.floataa						)
                     for i, atom in enumerate(a__						):
                            A__							=   1
                     atom_mask *= mask[..., None]

       assert aatype is not None

       return Protein(
           atom_positions=a__			,    atom_mask=a__			,    aatype=a__			,    residue_index=np.arange(len(a__						)						)			,    b_factors=a__			,    )







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_ = 0						)       ->	List[str]:



       """simple docstring"""

       A__							=   []

       A__							=   prot.remark
       if remark is not None:
              pdb_headers.append(f"""REMARK {remark}"""						)

       A__							=   prot.parents
       A__							=   prot.parents_chain_index
       if parents is not None and parents_chain_index is not None:
              A__							=   [p for i, p in zip(a__			,    a__						) if i == chain_id]

       if parents is None or len(a__						) == 0:
              A__							=   ['''N/A''']

       pdb_headers.append(f"""PARENT {" ".join(a__						)}"""						)

       return pdb_headers







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	str:



       """simple docstring"""

       A__							=   []
       A__							=   pdb_str.split('''\n'''						)

       A__							=   prot.remark
       if remark is not None:
              out_pdb_lines.append(f"""REMARK {remark}"""						)

       A__							=   42
       if prot.parents is not None and len(prot.parents						) > 0:
              A__							=   []
              if prot.parents_chain_index is not None:
                     A__							=   {}
                     for p, i in zip(prot.parents			,    prot.parents_chain_index						):
                            parent_dict.setdefault(str(a__						)			,    []						)
                            parent_dict[str(a__						)].append(a__						)

                     A__							=   max([int(a__						) for chain_idx in parent_dict]						)
                     for i in range(max_idx + 1						):
                            A__							=   parent_dict.get(str(a__						)			,    ['''N/A''']						)
                            parents_per_chain.append(a__						)
              else:
                     parents_per_chain.append(list(prot.parents						)						)
       else:
              A__							=   [['''N/A''']]

       def make_parent_line(lowercase_						) -> str:
              return f"""PARENT {" ".join(a__						)}"""

       out_pdb_lines.append(make_parent_line(parents_per_chain[0]						)						)

       A__							=   0
       for i, l in enumerate(a__						):
              if "PARENT" not in l and "REMARK" not in l:
                     out_pdb_lines.append(a__						)
              if "TER" in l and "END" not in lines[i + 1]:
                     chain_counter += 1
                     if not chain_counter >= len(a__						):
                            A__							=   parents_per_chain[chain_counter]
                     else:
                            A__							=   ['''N/A''']

                     out_pdb_lines.append(make_parent_line(a__						)						)

       return "\n".join(a__						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       A__							=   residue_constants.restypes + ['''X''']

       def res_atoa(lowercase_						) -> str:
              return residue_constants.restype_atoa.get(restypes[r]			,    '''UNK'''						)

       A__							=   residue_constants.atom_types

       A__							=   []

       A__							=   prot.atom_mask
       A__							=   prot.aatype
       A__							=   prot.atom_positions
       A__							=   prot.residue_index.astype(np.intaa						)
       A__							=   prot.b_factors
       A__							=   prot.chain_index

       if np.any(aatype > residue_constants.restype_num						):
              raise ValueError('''Invalid aatypes.'''						)

       A__							=   get_pdb_headers(a__						)
       if len(a__						) > 0:
              pdb_lines.extend(a__						)

       A__							=   aatype.shape[0]
       A__							=   1
       A__							=   0
       A__							=   string.ascii_uppercase
       A__							=   None
       # Add all atom sites.
       for i in range(a__						):
              A__							=   res_atoa(aatype[i]						)
              for atom_name, pos, mask, b_factor in zip(a__			,    atom_positions[i]			,    atom_mask[i]			,    b_factors[i]						):
                     if mask < 0.5:
                            continue

                     A__							=   '''ATOM'''
                     A__							=   atom_name if len(a__						) == 4 else f""" {atom_name}"""
                     A__							=   ''''''
                     A__							=   ''''''
                     A__							=   1.00
                     A__							=   atom_name[0]  # Protein supports only C, N, O, S, this works.
                     A__							=   ''''''

                     A__							=   '''A'''
                     if chain_index is not None:
                            A__							=   chain_tags[chain_index[i]]

                     # PDB is a columnar format, every space matters here!
                     A__							=   (
                         f"""{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"""
                         f"""{res_name_a:>3} {chain_tag:>1}"""
                         f"""{residue_index[i]:>4}{insertion_code:>1}   """
                         f"""{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"""
                         f"""{occupancy:>6.2f}{b_factor:>6.2f}          """
                         f"""{element:>2}{charge:>2}"""
                     )
                     pdb_lines.append(a__						)
                     atom_index += 1

              A__							=   i == n - 1
              if chain_index is not None:
                     if i != n - 1 and chain_index[i + 1] != prev_chain_index:
                            A__							=   True
                            A__							=   chain_index[i + 1]

              if should_terminate:
                     # Close the chain.
                     A__							=   '''TER'''
                     A__							=   (
                         f"""{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]						):>3} {chain_tag:>1}{residue_index[i]:>4}"""
                     )
                     pdb_lines.append(a__						)
                     atom_index += 1

                     if i != n - 1:
                            # "prev" is a misnomer here. This happens at the beginning of
                            # each new chain.
                            pdb_lines.extend(get_pdb_headers(a__			,    a__						)						)

       pdb_lines.append('''END'''						)
       pdb_lines.append(''''''						)
       return "\n".join(a__						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	np.ndarray:



       """simple docstring"""

       return residue_constants.STANDARD_ATOM_MASK[prot.aatype]







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_ = None			,    lowercase_ = None			,    lowercase_ = None			,    lowercase_ = None			,    lowercase_ = None			,    )       ->	Protein:



       """simple docstring"""

       return Protein(
           aatype=features['''aatype''']			,    atom_positions=result['''final_atom_positions''']			,    atom_mask=result['''final_atom_mask''']			,    residue_index=features['''residue_index'''] + 1			,    b_factors=b_factors if b_factors is not None else np.zeros_like(result['''final_atom_mask''']						)			,    chain_index=a__			,    remark=a__			,    parents=a__			,    parents_chain_index=a__			,    )








import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
  import torch


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     str ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Any=14 ,			UpperCAmelCase__				:     Any=7 ,			UpperCAmelCase__				:     Dict=True ,			UpperCAmelCase__				:     int=True ,			UpperCAmelCase__				:     Tuple=False ,			UpperCAmelCase__				:     Optional[int]=True ,			UpperCAmelCase__				:     Tuple=99 ,			UpperCAmelCase__				:     Optional[Any]=32 ,			UpperCAmelCase__				:     Tuple=4 ,			UpperCAmelCase__				:     Any=4 ,			UpperCAmelCase__				:     int=4 ,			UpperCAmelCase__				:     Optional[Any]=37 ,			UpperCAmelCase__				:     Union[str, Any]="gelu" ,			UpperCAmelCase__				:     Optional[int]=0.1 ,			UpperCAmelCase__				:     int=0.1 ,			UpperCAmelCase__				:     Optional[Any]=512 ,			UpperCAmelCase__				:     Union[str, Any]=0.02 ,			)						->Any:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->List[str]:





           '''simple docstring'''

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

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

           A__							=   GPTJConfig(
               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=UpperCAmelCase__ ,			bos_token_id=self.bos_token_id ,			eos_token_id=self.eos_token_id ,			pad_token_id=self.pad_token_id ,			rotary_dim=self.rotary_dim ,			)

           return (config, input_ids, input_mask)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Optional[int]:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     List[Any])						->Optional[int]:





           '''simple docstring'''

           A__							=   20
           A__							=   model_class_name(UpperCAmelCase__)

           A__							=   model.init_cache(input_ids.shape[0] ,			UpperCAmelCase__)
           A__							=   jnp.ones((input_ids.shape[0], max_decoder_length) ,			dtype='''i4''')

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

           A__							=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,			dtype='''i4''')
           A__							=   model(
               input_ids[:, -1:] ,			attention_mask=UpperCAmelCase__ ,			past_key_values=outputs_cache.past_key_values ,			position_ids=UpperCAmelCase__ ,			)

           A__							=   model(UpperCAmelCase__)

           A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     int)						->Any:





           '''simple docstring'''

           A__							=   20
           A__							=   model_class_name(UpperCAmelCase__)

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

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

           A__							=   model(
               input_ids[:, :-1] ,			attention_mask=UpperCAmelCase__ ,			past_key_values=UpperCAmelCase__ ,			position_ids=UpperCAmelCase__ ,			)
           A__							=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,			dtype='''i4''')
           A__							=   model(
               input_ids[:, -1:] ,			past_key_values=outputs_cache.past_key_values ,			attention_mask=UpperCAmelCase__ ,			position_ids=UpperCAmelCase__ ,			)

           A__							=   model(UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__)

           A__							=   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     UpperCamelCase_   (			UpperCAmelCase__		, UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    UpperCAmelCase__							  =				(FlaxGPTJForCausalLM,) if is_flax_available() else ()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->List[Any]:





           '''simple docstring'''

           A__							=   FlaxGPTJModelTester(self)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Any:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__			,							A__			,							A__							=   self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Any:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__			,							A__			,							A__							=   self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward_with_attn_mask(
                      UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    @tooslow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Dict:





           '''simple docstring'''

           A__							=   GPTaTokenizer.from_pretrained('''gpt2''' ,			pad_token='''<|endoftext|>''' ,			padding_side='''left''')
           A__							=   tokenizer(['''Hello this is a long string''', '''Hey'''] ,			return_tensors='''np''' ,			padding=UpperCAmelCase__ ,			truncation=UpperCAmelCase__)

           A__							=   FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''')
           A__							=   False
           A__							=   model.config.eos_token_id

           A__							=   jax.jit(model.generate)

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

           A__							=   tokenizer.batch_decode(UpperCAmelCase__ ,			skip_special_tokens=UpperCAmelCase__)

           A__							=   [
               '''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(UpperCAmelCase__ ,			UpperCAmelCase__)


    @is_pt_flax_cross_test
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->int:





           '''simple docstring'''

           A__			,							A__							=   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
                         A__							=   self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__)
                         A__							=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

                         A__							=   convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,			UpperCAmelCase__)
                         A__							=   fx_state

                         with torch.no_grad():
                                A__							=   pt_model(**UpperCAmelCase__).to_tuple()

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

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

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


    @is_pt_flax_cross_test
    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Optional[int]:





           '''simple docstring'''

           A__			,							A__							=   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
                         A__							=   self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__)
                         A__							=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

                         A__							=   pt_model_class(UpperCAmelCase__).eval()
                         A__							=   model_class(UpperCAmelCase__ ,			dtype=jnp.floataa)

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

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

                         with torch.no_grad():
                                A__							=   pt_model(**UpperCAmelCase__).to_tuple()

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

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

                         with torch.no_grad():
                                A__							=   pt_model_loaded(**UpperCAmelCase__).to_tuple()

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





    @tooslow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Optional[Any]:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__							=   model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''')
                  A__							=   model(np.ones((1, 1)))
                  self.assertIsNotNone(UpperCAmelCase__)


