

"""simple docstring"""






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


_lowerCAmelCase						=		logging.get_logger(__name__)

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=False   ):

    '''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:
        _lowerCAmelCase       :   Optional[Any]  =    os.path.abspath(_lowerCamelCase   )
        logger.info(f"""Loading PyTorch weights from {pt_path}"""   )

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

        _lowerCAmelCase       :   Optional[Any]  =    convert_pytorch_state_dict_to_flax(_lowerCamelCase ,   _lowerCamelCase   )
    else:
        # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
        _lowerCAmelCase       :   List[str]  =    convert_pytorch_sharded_state_dict_to_flax(_lowerCamelCase ,   _lowerCamelCase   )
    return flax_state_dict

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   ):

    '''simple docstring'''



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

    # layer norm
    _lowerCAmelCase       :   int  =    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
    _lowerCAmelCase       :   int  =    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
    _lowerCAmelCase       :   Dict  =    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
    _lowerCAmelCase       :   List[str]  =    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
    _lowerCAmelCase       :   List[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   ):
        _lowerCAmelCase       :   Optional[Any]  =    pt_tensor.transpose(2 ,   3 ,   1 ,   0   )
        return renamed_pt_tuple_key, pt_tensor

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

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

    # old PyTorch layer norm bias
    _lowerCAmelCase       :   int  =    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
    _lowerCAmelCase       :   Optional[int]  =    None
    if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
        _lowerCAmelCase       :   Union[str, Any]  =    pt_tuple_key[-2] + '_g'
    elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
        _lowerCAmelCase       :   List[str]  =    pt_tuple_key[-2] + '_v'
    if name is not None:
        _lowerCAmelCase       :   Any  =    pt_tuple_key[:-3] + (name,)
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    {k: v.numpy() for k, v in pt_state_dict.items()}

    _lowerCAmelCase       :   Optional[int]  =    flax_model.base_model_prefix

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

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

    _lowerCAmelCase       :   List[str]  =    {}

    _lowerCAmelCase       :   Any  =    (model_prefix not in flax_model_params) and (
        model_prefix in {k.split('.'   )[0] for k in pt_state_dict.keys()}
    )
    _lowerCAmelCase       :   Optional[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():
        _lowerCAmelCase       :   Any  =    tuple(pt_key.split('.'   )   )

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

        # Correctly rename weight parameters
        _lowerCAmelCase       :   Optional[Any]  =    rename_key_and_reshape_tensor(
            _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )

        # add model prefix if necessary
        _lowerCAmelCase       :   List[str]  =    (model_prefix,) + flax_key in random_flax_state_dict
        if load_base_model_into_model_with_head and require_base_model_prefix:
            _lowerCAmelCase       :   List[str]  =    (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]:
                _lowerCAmelCase       :   Tuple  =    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
            _lowerCAmelCase       :   str  =    jnp.asarray(_lowerCamelCase   )

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

    return unflatten_dict(_lowerCamelCase   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    import torch

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

        _lowerCAmelCase       :   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:
            _lowerCAmelCase       :   Optional[Any]  =    flax_model.params['params']

            _lowerCAmelCase       :   Union[str, Any]  =    flatten_dict(_lowerCamelCase   )
            random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']   )   )
        else:
            _lowerCAmelCase       :   Optional[Any]  =    flax_model.params
            _lowerCAmelCase       :   str  =    flatten_dict(_lowerCamelCase   )

        _lowerCAmelCase       :   str  =    (model_prefix not in flax_model_params) and (
            model_prefix in {k.split('.'   )[0] for k in pt_state_dict.keys()}
        )
        _lowerCAmelCase       :   Tuple  =    (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():
            _lowerCAmelCase       :   Tuple  =    tuple(pt_key.split('.'   )   )

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

            # Correctly rename weight parameters
            _lowerCAmelCase       :   int  =    rename_key_and_reshape_tensor(
                _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )
            # add model prefix if necessary
            _lowerCAmelCase       :   Dict  =    (model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                _lowerCAmelCase       :   List[str]  =    (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]:
                    _lowerCAmelCase       :   str  =    jnp.asarray(_lowerCamelCase   )
                    continue
                if "var" in flax_key[-1]:
                    _lowerCAmelCase       :   Optional[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
                _lowerCAmelCase       :   Union[str, Any]  =    jnp.asarray(_lowerCamelCase   )

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

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[str]  =    os.path.abspath(_lowerCamelCase   )
    logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""   )

    # import correct flax class
    _lowerCAmelCase       :   Union[str, Any]  =    getattr(_lowerCamelCase ,   'Flax' + model.__class__.__name__   )

    # load flax weight dict
    with open(_lowerCamelCase ,   'rb'   ) as state_f:
        try:
            _lowerCAmelCase       :   Any  =    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 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''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
    _lowerCAmelCase       :   Tuple  =    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.'   )
        _lowerCAmelCase       :   List[Any]  =    jax.tree_util.tree_map(
            lambda _lowerCamelCase   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params ,   _lowerCamelCase   )

    _lowerCAmelCase       :   Optional[int]  =    flatten_dict(_lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    pt_model.state_dict()

    _lowerCAmelCase       :   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()}
    )
    _lowerCAmelCase       :   List[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
    _lowerCAmelCase       :   Tuple  =    []
    _lowerCAmelCase       :   List[str]  =    set(pt_model_dict.keys()   )

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        _lowerCAmelCase       :   List[str]  =    flax_key_tuple[0] == pt_model.base_model_prefix
        _lowerCAmelCase       :   List[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:
            _lowerCAmelCase       :   Tuple  =    flax_key_tuple[1:]
        elif load_base_model_into_model_with_head and require_base_model_prefix:
            _lowerCAmelCase       :   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
            _lowerCAmelCase       :   Tuple  =    flax_key_tuple[:-1] + ('weight',)
            _lowerCAmelCase       :   Tuple  =    jnp.transpose(_lowerCamelCase ,   (3, 2, 0, 1)   )
        elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCamelCase   ) not in pt_model_dict:
            # linear layer
            _lowerCAmelCase       :   List[Any]  =    flax_key_tuple[:-1] + ('weight',)
            _lowerCAmelCase       :   str  =    flax_tensor.T
        elif flax_key_tuple[-1] in ["scale", "embedding"]:
            _lowerCAmelCase       :   int  =    flax_key_tuple[:-1] + ('weight',)

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

        if "batch_stats" in flax_state:
            _lowerCAmelCase       :   Any  =    '.'.join(flax_key_tuple[1:]   )  # Remove the params/batch_stats header
        else:
            _lowerCAmelCase       :   Tuple  =    '.'.join(_lowerCamelCase   )

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

        if flax_key in special_pt_names:
            _lowerCAmelCase       :   Tuple  =    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
                _lowerCAmelCase       :   Union[str, Any]  =    np.asarray(_lowerCamelCase   ) if not isinstance(_lowerCamelCase ,   np.ndarray   ) else flax_tensor
                _lowerCAmelCase       :   Optional[int]  =    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
    _lowerCAmelCase       :   Dict  =    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








"""simple docstring"""






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFResNetForImageClassification, TFResNetModel
 from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor







class 	__UpperCamelCase    :

  def __init__(		self    ,_A    ,_A=3    ,_A=32    ,_A=3    ,_A=10    ,_A=[10, 20, 30, 40]    ,_A=[1, 1, 2, 1]    ,_A=True    ,_A=True    ,_A="relu"    ,_A=3    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    parent
      _lowerCAmelCase       :   int  =    batch_size
      _lowerCAmelCase       :   int  =    image_size
      _lowerCAmelCase       :   List[str]  =    num_channels
      _lowerCAmelCase       :   Optional[int]  =    embeddings_size
      _lowerCAmelCase       :   Optional[int]  =    hidden_sizes
      _lowerCAmelCase       :   str  =    depths
      _lowerCAmelCase       :   str  =    is_training
      _lowerCAmelCase       :   int  =    use_labels
      _lowerCAmelCase       :   Optional[int]  =    hidden_act
      _lowerCAmelCase       :   Optional[int]  =    num_labels
      _lowerCAmelCase       :   Dict  =    scope
      _lowerCAmelCase       :   Union[str, Any]  =    len(_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

      _lowerCAmelCase       :   Optional[Any]  =    None
      if self.use_labels:
          _lowerCAmelCase       :   List[Any]  =    ids_tensor([self.batch_size]    ,self.num_labels      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.get_config()

      return config, pixel_values, labels

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return ResNetConfig(
          num_channels=self.num_channels    ,embeddings_size=self.embeddings_size    ,hidden_sizes=self.hidden_sizes    ,depths=self.depths    ,hidden_act=self.hidden_act    ,num_labels=self.num_labels    ,image_size=self.image_size    ,)

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    TFResNetModel(config=_A      )
      _lowerCAmelCase       :   List[str]  =    model(_A      )
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape    ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,)

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self.num_labels
      _lowerCAmelCase       :   Dict  =    TFResNetForImageClassification(_A      )
      _lowerCAmelCase       :   int  =    model(_A    ,labels=_A      )
      self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)      )







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.prepare_config_and_inputs()
      _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    config_and_inputs
      _lowerCAmelCase       :   Any  =    {'pixel_values': pixel_values}
      return config, inputs_dict







@require_tf
class 	__UpperCamelCase    (    a__							,							a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
  _UpperCAmelCase          =		(
      {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
      if is_tf_available()
      else {}
  )

  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    TFResNetModelTester(self      )
      _lowerCAmelCase       :   List[str]  =    ConfigTester(self    ,config_class=_A    ,has_text_modality=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return

  @unittest.skip(reason='ResNet does not use inputs_embeds'      )
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      pass

  @unittest.skip(reason='ResNet does not support input and output embeddings'      )
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      pass

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          _lowerCAmelCase       :   int  =    model_class(_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    inspect.signature(model.call      )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          _lowerCAmelCase       :   Any  =    [*signature.parameters.keys()]

          _lowerCAmelCase       :   str  =    ['pixel_values']
          self.assertListEqual(arg_names[:1]    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      def check_hidden_states_output(_A    ,_A    ,_A      ):
          _lowerCAmelCase       :   int  =    model_class(_A      )
          _lowerCAmelCase       :   int  =    model(**self._prepare_for_class(_A    ,_A      )      )

          _lowerCAmelCase       :   Dict  =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          _lowerCAmelCase       :   int  =    self.model_tester.num_stages
          self.assertEqual(len(_A      )    ,expected_num_stages + 1      )

          # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:]      )    ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,)

      _lowerCAmelCase,					_lowerCAmelCase       :   List[str]  =    self.model_tester.prepare_config_and_inputs_for_common()
      _lowerCAmelCase       :   Any  =    ['basic', 'bottleneck']
      for model_class in self.all_model_classes:
          for layer_type in layers_type:
              _lowerCAmelCase       :   Optional[int]  =    layer_type
              _lowerCAmelCase       :   Tuple  =    True
              check_hidden_states_output(_A    ,_A    ,_A      )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              _lowerCAmelCase       :   Any  =    True

              check_hidden_states_output(_A    ,_A    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_A      )







  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowerCAmelCase       :   Optional[Any]  =    TFResNetModel.from_pretrained(_A      )
          self.assertIsNotNone(_A      )





def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
    return image







@require_tf
@require_vision
class 	__UpperCamelCase    (    unittest.TestCase						):

  @cached_property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return (
          AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
          if is_vision_available()
          else None
      )







  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )

      _lowerCAmelCase       :   Tuple  =    self.default_image_processor
      _lowerCAmelCase       :   Optional[Any]  =    prepare_img()
      _lowerCAmelCase       :   int  =    image_processor(images=_A    ,return_tensors='tf'      )

      # forward pass
      _lowerCAmelCase       :   int  =    model(**_A      )

      # verify the logits
      _lowerCAmelCase       :   Optional[Any]  =    tf.TensorShape((1, 1000)      )
      self.assertEqual(outputs.logits.shape    ,_A      )

      _lowerCAmelCase       :   Any  =    tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]      )

      self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()    ,_A    ,atol=1E-4      )      )





