



import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


SCREAMING_SNAKE_CASE_:Optional[Any]					=						logging.get_logger(__name__)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase=False		)	->  Union[str, Any]:



    """simple docstring"""






    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            """Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"""
            """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
            """ instructions."""		)
        raise

    if not is_sharded:
        A			:	List[str]										=       os.path.abspath(_lowerCAmelCase		)
        logger.info(f'''Loading PyTorch weights from {pt_path}'''		)

        A			:	Optional[int]										=       torch.load(_lowerCAmelCase				,					map_location="""cpu"""		)
        logger.info(f'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()		):,} parameters.'''		)

        A			:	int										=       convert_pytorch_state_dict_to_flax(_lowerCAmelCase				,					_lowerCAmelCase		)
    else:
        # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
        A			:	int										=       convert_pytorch_sharded_state_dict_to_flax(_lowerCAmelCase				,					_lowerCAmelCase		)
    return flax_state_dict




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					)	->  (Tuple[str], np.ndarray):



    """simple docstring"""






    def is_key_or_prefix_key_in_dict(_lowerCAmelCase		) -> bool:
        return len(set(_lowerCAmelCase		) & {key, (model_prefix,) + key}		) > 0

    # layer norm
    A			:	Union[str, Any]										=       pt_tuple_key[:-1] + ("""scale""",)
    if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_lowerCAmelCase		):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer mean
    A			:	Optional[Any]										=       pt_tuple_key[:-1] + ("""mean""",)
    if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase		):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer var
    A			:	int										=       pt_tuple_key[:-1] + ("""var""",)
    if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase		):
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    A			:	Optional[Any]										=       pt_tuple_key[:-1] + ("""embedding""",)
    if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_lowerCAmelCase		):
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    A			:	Optional[Any]										=       pt_tuple_key[:-1] + ("""kernel""",)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_lowerCAmelCase		):
        A			:	str										=       pt_tensor.transpose(2				,					3				,					1				,					0		)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    A			:	Any										=       pt_tuple_key[:-1] + ("""kernel""",)
    if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase		):
        A			:	List[str]										=       pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    A			:	Union[str, Any]										=       pt_tuple_key[:-1] + ("""weight""",)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    A			:	Dict										=       pt_tuple_key[:-1] + ("""bias""",)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    A			:	Optional[int]										=       None
    if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
        A			:	Optional[int]										=       pt_tuple_key[-2] + """_g"""
    elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
        A			:	Union[str, Any]										=       pt_tuple_key[-2] + """_v"""
    if name is not None:
        A			:	Any										=       pt_tuple_key[:-3] + (name,)
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






    A			:	Optional[Any]										=       {k: v.numpy() for k, v in pt_state_dict.items()}

    A			:	List[str]										=       flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers
    if "params" in flax_model.params:
        A			:	Union[str, Any]										=       flax_model.params["""params"""]
    else:
        A			:	Any										=       flax_model.params
    A			:	Optional[int]										=       flatten_dict(_lowerCAmelCase		)

    # add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
        A			:	Dict										=       flatten_dict(flax_model.params["""batch_stats"""]		)
        random_flax_state_dict.update(_lowerCAmelCase		)

    A			:	str										=       {}

    A			:	str										=       (model_prefix not in flax_model_params) and (
        model_prefix in {k.split("""."""		)[0] for k in pt_state_dict.keys()}
    )
    A			:	Dict										=       (model_prefix in flax_model_params) and (
        model_prefix not in {k.split("""."""		)[0] for k in pt_state_dict.keys()}
    )

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        A			:	int										=       tuple(pt_key.split("""."""		)		)

        # remove base model prefix if necessary
        A			:	Any										=       pt_tuple_key[0] == model_prefix
        if load_model_with_head_into_base_model and has_base_model_prefix:
            A			:	List[Any]										=       pt_tuple_key[1:]

        # Correctly rename weight parameters
        A					,							A			:	Tuple										=       rename_key_and_reshape_tensor(
            _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)

        # add model prefix if necessary
        A			:	Tuple										=       (model_prefix,) + flax_key in random_flax_state_dict
        if load_base_model_into_model_with_head and require_base_model_prefix:
            A			:	Union[str, Any]										=       (model_prefix,) + flax_key

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''		)

        # add batch stats if the model contains batchnorm layers
        if "batch_stats" in flax_model.params:
            if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                A			:	List[Any]										=       jnp.asarray(_lowerCAmelCase		)
                continue
            # remove num_batches_tracked key
            if "num_batches_tracked" in flax_key[-1]:
                flax_state_dict.pop(_lowerCAmelCase				,					_lowerCAmelCase		)
                continue

            # also add unexpected weight so that warning is thrown
            A			:	int										=       jnp.asarray(_lowerCAmelCase		)

        else:
            # also add unexpected weight so that warning is thrown
            A			:	List[Any]										=       jnp.asarray(_lowerCAmelCase		)

    return unflatten_dict(_lowerCAmelCase		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    import torch

    # Load the index
    A			:	List[Any]										=       {}
    for shard_file in shard_filenames:
        # load using msgpack utils
        A			:	str										=       torch.load(_lowerCAmelCase		)
        A			:	Any										=       {k: v.numpy() for k, v in pt_state_dict.items()}

        A			:	Dict										=       flax_model.base_model_prefix

        # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
        if "batch_stats" in flax_model.params:
            A			:	str										=       flax_model.params["""params"""]

            A			:	Any										=       flatten_dict(_lowerCAmelCase		)
            random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]		)		)
        else:
            A			:	Dict										=       flax_model.params
            A			:	Optional[int]										=       flatten_dict(_lowerCAmelCase		)

        A			:	str										=       (model_prefix not in flax_model_params) and (
            model_prefix in {k.split("""."""		)[0] for k in pt_state_dict.keys()}
        )
        A			:	Any										=       (model_prefix in flax_model_params) and (
            model_prefix not in {k.split("""."""		)[0] for k in pt_state_dict.keys()}
        )
        # Need to change some parameters name to match Flax names
        for pt_key, pt_tensor in pt_state_dict.items():
            A			:	List[Any]										=       tuple(pt_key.split("""."""		)		)

            # remove base model prefix if necessary
            A			:	Optional[Any]										=       pt_tuple_key[0] == model_prefix
            if load_model_with_head_into_base_model and has_base_model_prefix:
                A			:	List[Any]										=       pt_tuple_key[1:]

            # Correctly rename weight parameters
            A					,							A			:	Dict										=       rename_key_and_reshape_tensor(
                _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)
            # add model prefix if necessary
            A			:	Optional[int]										=       (model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                A			:	List[Any]										=       (model_prefix,) + flax_key

            if flax_key in random_flax_state_dict:
                if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                        f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''		)

            # add batch stats if the model contains batchnorm layers
            if "batch_stats" in flax_model.params:
                if "mean" in flax_key[-1]:
                    A			:	List[str]										=       jnp.asarray(_lowerCAmelCase		)
                    continue
                if "var" in flax_key[-1]:
                    A			:	int										=       jnp.asarray(_lowerCAmelCase		)
                    continue
                # remove num_batches_tracked key
                if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(_lowerCAmelCase				,					_lowerCAmelCase		)
                    continue

                # also add unexpected weight so that warning is thrown
                A			:	Union[str, Any]										=       jnp.asarray(_lowerCAmelCase		)

            else:
                # also add unexpected weight so that warning is thrown
                A			:	List[str]										=       jnp.asarray(_lowerCAmelCase		)
    return unflatten_dict(_lowerCAmelCase		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Optional[int]:



    """simple docstring"""






    A			:	Optional[Any]										=       os.path.abspath(_lowerCAmelCase		)
    logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''		)

    # import correct flax class
    A			:	Tuple										=       getattr(_lowerCAmelCase				,					"""Flax""" + model.__class__.__name__		)

    # load flax weight dict
    with open(_lowerCAmelCase				,					"""rb"""		) as state_f:
        try:
            A			:	Dict										=       from_bytes(_lowerCAmelCase				,					state_f.read()		)
        except UnpicklingError:
            raise EnvironmentError(f'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. '''		)

    return load_flax_weights_in_pytorch_model(_lowerCAmelCase				,					_lowerCAmelCase		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  str:



    """simple docstring"""






    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            """Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"""
            """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
            """ instructions."""		)
        raise

    # check if we have bf16 weights
    A			:	Optional[Any]										=       flatten_dict(jax.tree_util.tree_map(lambda _lowerCAmelCase		: x.dtype == jnp.bfloataa				,					_lowerCAmelCase		)		).values()
    if any(_lowerCAmelCase		):
        # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
        # and bf16 is not fully supported in PT yet.
        logger.warning(
            """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
            """before loading those in PyTorch model."""		)
        A			:	Optional[int]										=       jax.tree_util.tree_map(
            lambda _lowerCAmelCase		: params.astype(np.floataa		) if params.dtype == jnp.bfloataa else params				,					_lowerCAmelCase		)

    A			:	int										=       flatten_dict(_lowerCAmelCase		)
    A			:	Tuple										=       pt_model.state_dict()

    A			:	Union[str, Any]										=       (pt_model.base_model_prefix in flax_state) and (
        pt_model.base_model_prefix not in {k.split("""."""		)[0] for k in pt_model_dict.keys()}
    )
    A			:	str										=       (pt_model.base_model_prefix not in flax_state) and (
        pt_model.base_model_prefix in {k.split("""."""		)[0] for k in pt_model_dict.keys()}
    )

    # keep track of unexpected & missing keys
    A			:	Any										=       []
    A			:	List[Any]										=       set(pt_model_dict.keys()		)

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        A			:	str										=       flax_key_tuple[0] == pt_model.base_model_prefix
        A			:	Union[str, Any]										=       """.""".join((pt_model.base_model_prefix,) + flax_key_tuple		) in pt_model_dict

        # adapt flax_key to prepare for loading from/to base model only
        if load_model_with_head_into_base_model and has_base_model_prefix:
            A			:	str										=       flax_key_tuple[1:]
        elif load_base_model_into_model_with_head and require_base_model_prefix:
            A			:	List[Any]										=       (pt_model.base_model_prefix,) + flax_key_tuple

        # rename flax weights to PyTorch format
        if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_lowerCAmelCase		) not in pt_model_dict:
            # conv layer
            A			:	Union[str, Any]										=       flax_key_tuple[:-1] + ("""weight""",)
            A			:	List[Any]										=       jnp.transpose(_lowerCAmelCase				,					(3, 2, 0, 1)		)
        elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase		) not in pt_model_dict:
            # linear layer
            A			:	Optional[int]										=       flax_key_tuple[:-1] + ("""weight""",)
            A			:	Tuple										=       flax_tensor.T
        elif flax_key_tuple[-1] in ["scale", "embedding"]:
            A			:	List[str]										=       flax_key_tuple[:-1] + ("""weight""",)

        # adding batch stats from flax batch norm to pt
        elif "mean" in flax_key_tuple[-1]:
            A			:	Optional[Any]										=       flax_key_tuple[:-1] + ("""running_mean""",)
        elif "var" in flax_key_tuple[-1]:
            A			:	Tuple										=       flax_key_tuple[:-1] + ("""running_var""",)

        if "batch_stats" in flax_state:
            A			:	List[str]										=       """.""".join(flax_key_tuple[1:]		)  # Remove the params/batch_stats header
        else:
            A			:	Union[str, Any]										=       """.""".join(_lowerCAmelCase		)

        # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
        A			:	List[str]										=       {}
        # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
        for key in pt_model_dict:
            A			:	Optional[int]										=       key.split("""."""		)
            A			:	List[Any]										=       None
            if key_components[-3::2] == ["parametrizations", "original0"]:
                A			:	Optional[int]										=       key_components[-2] + """_g"""
            elif key_components[-3::2] == ["parametrizations", "original1"]:
                A			:	Any										=       key_components[-2] + """_v"""
            if name is not None:
                A			:	int										=       key_components[:-3] + [name]
                A			:	str										=       """.""".join(_lowerCAmelCase		)
                A			:	Dict										=       key

        if flax_key in special_pt_names:
            A			:	List[Any]										=       special_pt_names[flax_key]

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
                    f'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''		)
            else:
                # add weight to pytorch dict
                A			:	Union[str, Any]										=       np.asarray(_lowerCAmelCase		) if not isinstance(_lowerCAmelCase				,					np.ndarray		) else flax_tensor
                A			:	Tuple										=       torch.from_numpy(_lowerCAmelCase		)
                # remove from missing keys
                missing_keys.remove(_lowerCAmelCase		)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(_lowerCAmelCase		)

    pt_model.load_state_dict(_lowerCAmelCase		)

    # re-transform missing_keys to list
    A			:	Tuple										=       list(_lowerCAmelCase		)

    if len(_lowerCAmelCase		) > 0:
        logger.warning(
            """Some weights of the Flax model were not used when initializing the PyTorch model"""
            f''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
            f''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
            """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
            f''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
            """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
            """ FlaxBertForSequenceClassification model)."""		)
    else:
        logger.warning(f'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'''		)
    if len(_lowerCAmelCase		) > 0:
        logger.warning(
            f'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
            f''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
            """ use it for predictions and inference."""		)
    else:
        logger.warning(
            f'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
            """If your task is similar to the task the model of the checkpoint was trained on, """
            f'''you can already use {pt_model.__class__.__name__} for predictions without further training.'''		)

    return pt_model







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 SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__=14,		lowerCamelCase__=7,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=False,		lowerCamelCase__=True,		lowerCamelCase__=99,		lowerCamelCase__=32,		lowerCamelCase__=4,		lowerCamelCase__=4,		lowerCamelCase__=4,		lowerCamelCase__=37,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=0.02,		):
        A			:	List[str]										=       parent
        A			:	Any										=       batch_size
        A			:	Dict										=       seq_length
        A			:	Tuple										=       is_training
        A			:	Any										=       use_input_mask
        A			:	Any										=       use_token_type_ids
        A			:	Any										=       use_labels
        A			:	Optional[int]										=       vocab_size
        A			:	Dict										=       hidden_size
        A			:	Dict										=       rotary_dim
        A			:	Dict										=       num_hidden_layers
        A			:	Tuple										=       num_attention_heads
        A			:	Tuple										=       intermediate_size
        A			:	Union[str, Any]										=       hidden_act
        A			:	Dict										=       hidden_dropout_prob
        A			:	List[str]										=       attention_probs_dropout_prob
        A			:	Optional[int]										=       max_position_embeddings
        A			:	str										=       initializer_range
        A			:	Any										=       None
        A			:	Any										=       vocab_size - 1
        A			:	int										=       vocab_size - 1
        A			:	int										=       vocab_size - 1






    def       _lowerCAmelCase (	self		):
        A			:	str										=       ids_tensor([self.batch_size, self.seq_length],		self.vocab_size		)

        A			:	Optional[int]										=       None
        if self.use_input_mask:
            A			:	Any										=       random_attention_mask([self.batch_size, self.seq_length]		)

        A			:	int										=       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=lowerCamelCase__,		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       _lowerCAmelCase (	self		):
        A			:	List[str]										=       self.prepare_config_and_inputs()
        A					,							A					,							A			:	List[str]										=       config_and_inputs
        A			:	Optional[int]										=       {"""input_ids""": input_ids, """attention_mask""": attention_mask}
        return config, inputs_dict






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Optional[int]										=       20
        A			:	Tuple										=       model_class_name(lowerCamelCase__		)

        A			:	Dict										=       model.init_cache(input_ids.shape[0],		lowerCamelCase__		)
        A			:	int										=       jnp.ones((input_ids.shape[0], max_decoder_length),		dtype="""i4"""		)

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

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

        A			:	Any										=       model(lowerCamelCase__		)

        A			:	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       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Any										=       20
        A			:	Any										=       model_class_name(lowerCamelCase__		)

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

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

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

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

        A			:	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 SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						unittest.TestCase   ):




    '''simple docstring'''






    __lowerCamelCase      :			Any =				(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    __lowerCamelCase      :			Optional[int] =				(FlaxGPTJForCausalLM,) if is_flax_available() else ()






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       FlaxGPTJModelTester(self		)






    def       _lowerCAmelCase (	self		):
        for model_class_name in self.all_model_classes:
            A					,							A					,							A			:	Optional[Any]										=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        for model_class_name in self.all_model_classes:
            A					,							A					,							A			:	str										=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward_with_attn_mask(
                lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)






    @tooslow
    def       _lowerCAmelCase (	self		):
        A			:	int										=       GPTaTokenizer.from_pretrained("""gpt2""",		pad_token="""<|endoftext|>""",		padding_side="""left"""		)
        A			:	Optional[int]										=       tokenizer(["""Hello this is a long string""", """Hey"""],		return_tensors="""np""",		padding=lowerCamelCase__,		truncation=lowerCamelCase__		)

        A			:	Dict										=       FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""		)
        A			:	str										=       False
        A			:	Optional[Any]										=       model.config.eos_token_id

        A			:	Union[str, Any]										=       jax.jit(model.generate		)

        A			:	str										=       jit_generate(
            inputs["""input_ids"""],		attention_mask=inputs["""attention_mask"""],		pad_token_id=tokenizer.pad_token_id		).sequences

        A			:	Optional[Any]										=       tokenizer.batch_decode(lowerCamelCase__,		skip_special_tokens=lowerCamelCase__		)

        A			:	Tuple										=       [
            """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(lowerCamelCase__,		lowerCamelCase__		)






    @is_pt_flax_cross_test
    def       _lowerCAmelCase (	self		):
        A					,							A			:	Optional[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
                A			:	Any										=       self._prepare_for_class(lowerCamelCase__,		lowerCamelCase__		)
                A			:	Dict										=       {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

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

                A			:	int										=       convert_pytorch_state_dict_to_flax(pt_model.state_dict(),		lowerCamelCase__		)
                A			:	Dict										=       fx_state

                with torch.no_grad():
                    A			:	Optional[int]										=       pt_model(**lowerCamelCase__		).to_tuple()

                A			:	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__		)
                    A			:	Union[str, Any]										=       model_class.from_pretrained(lowerCamelCase__,		from_pt=lowerCamelCase__		)

                A			:	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       _lowerCAmelCase (	self		):
        A					,							A			:	Dict										=       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			:	int										=       self._prepare_for_class(lowerCamelCase__,		lowerCamelCase__		)
                A			:	List[str]										=       {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

                A			:	int										=       pt_model_class(lowerCamelCase__		).eval()
                A			:	int										=       model_class(lowerCamelCase__,		dtype=jnp.floataa		)

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

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

                with torch.no_grad():
                    A			:	List[str]										=       pt_model(**lowerCamelCase__		).to_tuple()

                A			:	Optional[int]										=       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__		)
                    A			:	str										=       pt_model_class.from_pretrained(lowerCamelCase__,		from_flax=lowerCamelCase__		)

                with torch.no_grad():
                    A			:	str										=       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		)






    @tooslow
    def       _lowerCAmelCase (	self		):
        for model_class_name in self.all_model_classes:
            A			:	Union[str, Any]										=       model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""		)
            A			:	List[str]										=       model(np.ones((1, 1)		)		)
            self.assertIsNotNone(lowerCamelCase__		)


