





import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)


def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[Any]		,       SCREAMING_SNAKE_CASE_  :					List[str]		,       SCREAMING_SNAKE_CASE_  :					List[Any]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      os.path.abspath(SCREAMING_SNAKE_CASE_    )
    logger.info(F'''Converting TensorFlow checkpoint from {tf_path}'''    )
    # Load weights from TF model
    _lowerCAmelCase				=      tf.train.list_variables(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      []
    _lowerCAmelCase				=      []
    _lowerCAmelCase				=      []
    for full_name, shape in init_vars:
        # logger.info(f"Loading TF weight {name} with shape {shape}")
        _lowerCAmelCase				=      full_name.split("/"    )
        if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
            logger.info(F'''Skipping non-model layer {full_name}'''    )
            continue
        if "optimizer" in full_name:
            logger.info(F'''Skipping optimization layer {full_name}'''    )
            continue
        if name[0] == "model":
            # ignore initial 'model'
            _lowerCAmelCase				=      name[1:]
        # figure out how many levels deep the name is
        _lowerCAmelCase				=      0
        for _name in name:
            if _name.startswith("layer_with_weights"    ):
                depth += 1
            else:
                break
        layer_depth.append(SCREAMING_SNAKE_CASE_    )
        # read data
        _lowerCAmelCase				=      tf.train.load_variable(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )
        names.append("/".join(SCREAMING_SNAKE_CASE_    )    )
        arrays.append(SCREAMING_SNAKE_CASE_    )
    logger.info(F'''Read a total of {len(SCREAMING_SNAKE_CASE_    ):,} layers'''    )

    # Sanity check
    if len(set(SCREAMING_SNAKE_CASE_    )    ) != 1:
        raise ValueError(F'''Found layer names with different depths (layer depth {list(set(SCREAMING_SNAKE_CASE_    )    )})'''    )
    _lowerCAmelCase				=      list(set(SCREAMING_SNAKE_CASE_    )    )[0]
    if layer_depth != 1:
        raise ValueError(
            "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
            " heads."    )

    # convert layers
    logger.info("Converting weights..."    )
    for full_name, array in zip(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      full_name.split("/"    )
        _lowerCAmelCase				=      model
        _lowerCAmelCase				=      []
        for i, m_name in enumerate(SCREAMING_SNAKE_CASE_    ):
            if m_name == ".ATTRIBUTES":
                # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                break
            if m_name.startswith("layer_with_weights"    ):
                _lowerCAmelCase				=      int(m_name.split("-"    )[-1]    )
                if layer_num <= 2:
                    # embedding layers
                    # layer_num 0: word_embeddings
                    # layer_num 1: position_embeddings
                    # layer_num 2: token_type_embeddings
                    continue
                elif layer_num == 3:
                    # embedding LayerNorm
                    trace.extend(["embeddings", "LayerNorm"]    )
                    _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "embeddings"    )
                    _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "LayerNorm"    )
                elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                    # encoder layers
                    trace.extend(["encoder", "layer", str(layer_num - 4    )]    )
                    _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "encoder"    )
                    _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "layer"    )
                    _lowerCAmelCase				=      pointer[layer_num - 4]
                elif layer_num == config.num_hidden_layers + 4:
                    # pooler layer
                    trace.extend(["pooler", "dense"]    )
                    _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "pooler"    )
                    _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "dense"    )
            elif m_name == "embeddings":
                trace.append("embeddings"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "embeddings"    )
                if layer_num == 0:
                    trace.append("word_embeddings"    )
                    _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "word_embeddings"    )
                elif layer_num == 1:
                    trace.append("position_embeddings"    )
                    _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "position_embeddings"    )
                elif layer_num == 2:
                    trace.append("token_type_embeddings"    )
                    _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "token_type_embeddings"    )
                else:
                    raise ValueError(F'''Unknown embedding layer with name {full_name}'''    )
                trace.append("weight"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "weight"    )
            elif m_name == "_attention_layer":
                # self-attention layer
                trace.extend(["attention", "self"]    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "attention"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "self"    )
            elif m_name == "_attention_layer_norm":
                # output attention norm
                trace.extend(["attention", "output", "LayerNorm"]    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "attention"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "output"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "LayerNorm"    )
            elif m_name == "_attention_output_dense":
                # output attention dense
                trace.extend(["attention", "output", "dense"]    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "attention"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "output"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "dense"    )
            elif m_name == "_output_dense":
                # output dense
                trace.extend(["output", "dense"]    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "output"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "dense"    )
            elif m_name == "_output_layer_norm":
                # output dense
                trace.extend(["output", "LayerNorm"]    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "output"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "LayerNorm"    )
            elif m_name == "_key_dense":
                # attention key
                trace.append("key"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "key"    )
            elif m_name == "_query_dense":
                # attention query
                trace.append("query"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "query"    )
            elif m_name == "_value_dense":
                # attention value
                trace.append("value"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "value"    )
            elif m_name == "_intermediate_dense":
                # attention intermediate dense
                trace.extend(["intermediate", "dense"]    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "intermediate"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "dense"    )
            elif m_name == "_output_layer_norm":
                # output layer norm
                trace.append("output"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "output"    )
            # weights & biases
            elif m_name in ["bias", "beta"]:
                trace.append("bias"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "bias"    )
            elif m_name in ["kernel", "gamma"]:
                trace.append("weight"    )
                _lowerCAmelCase				=      getattr(SCREAMING_SNAKE_CASE_		,       "weight"    )
            else:
                logger.warning(F'''Ignored {m_name}'''    )
        # for certain layers reshape is necessary
        _lowerCAmelCase				=      ".".join(SCREAMING_SNAKE_CASE_    )
        if re.match(R"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"		,       SCREAMING_SNAKE_CASE_    ) or re.match(
            R"(\S+)\.attention\.output\.dense\.weight"		,       SCREAMING_SNAKE_CASE_    ):
            _lowerCAmelCase				=      array.reshape(pointer.data.shape    )
        if "kernel" in full_name:
            _lowerCAmelCase				=      array.transpose()
        if pointer.shape == array.shape:
            _lowerCAmelCase				=      torch.from_numpy(SCREAMING_SNAKE_CASE_    )
        else:
            raise ValueError(
                F'''Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'''
                F''' {array.shape}'''    )
        logger.info(F'''Successfully set variable {full_name} to PyTorch layer {trace}'''    )
    return model


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					Union[str, Any]		,       SCREAMING_SNAKE_CASE_  :					List[Any]    ):



    '''simple docstring'''





    logger.info(F'''Loading model based on config from {config_path}...'''    )
    _lowerCAmelCase				=      BertConfig.from_json_file(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      BertModel(SCREAMING_SNAKE_CASE_    )

    # Load weights from checkpoint
    logger.info(F'''Loading weights from checkpoint {tf_checkpoint_path}...'''    )
    load_tfa_weights_in_bert(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )

    # Save pytorch-model
    logger.info(F'''Saving PyTorch model to {pytorch_dump_path}...'''    )
    torch.save(model.state_dict()		,       SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE	 =							argparse.ArgumentParser()
       parser.add_argument(
           "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x checkpoint path."
       )
       parser.add_argument(
           "--bert_config_file",
           type=str,
           required=True,
           help="The config json file corresponding to the BERT model. This specifies the model architecture.",
       )
       parser.add_argument(
           "--pytorch_dump_path",
           type=str,
           required=True,
           help="Path to the output PyTorch model (must include filename).",
       )
       _SCREAMING_SNAKE_CASE	 =							parser.parse_args()
       convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









'''simple docstring'''


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
_SCREAMING_SNAKE_CASE	 =							{"LayoutLMv2Config", "LayoutLMv3Config"}

@is_pipeline_test
class    lowerCAmelCase_		(		unittest.TestCase		):
 __lowerCamelCase						:							Optional[Any]         =					MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
 __lowerCamelCase						:							str         =					TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

 if model_mapping is not None:
  __lowerCamelCase						:							Optional[int]         =					{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
 if tf_model_mapping is not None:
  __lowerCamelCase						:							List[Any]         =					{
      config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
  }




 @require_torch
 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      pipeline(
         task="text-classification"			,     model="hf-internal-testing/tiny-random-distilbert"			,     framework="pt"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     _lowerCAmelCase				=      text_classifier("This is great !"			,     top_k=2		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]		)

     _lowerCAmelCase				=      text_classifier(["This is great !", "This is bad"]			,     top_k=2		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
         ]			,     )

     _lowerCAmelCase				=      text_classifier("This is great !"			,     top_k=1		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     # Legacy behavior
     _lowerCAmelCase				=      text_classifier("This is great !"			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     _lowerCAmelCase				=      text_classifier("This is great !"			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]		)

     _lowerCAmelCase				=      text_classifier(["This is great !", "Something else"]			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
         ]			,     )

     _lowerCAmelCase				=      text_classifier(["This is great !", "Something else"]			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             {"label": "LABEL_0", "score": 0.504},
             {"label": "LABEL_0", "score": 0.504},
         ]			,     )




 @require_torch
 def 							_snake_case   (	self		)    ->							Tuple:
     import torch

     _lowerCAmelCase				=      pipeline(
         task="text-classification"			,     model="hf-internal-testing/tiny-random-distilbert"			,     framework="pt"			,     device=torch.device("cpu"		)			,     )

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)




 @require_tf
 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      pipeline(
         task="text-classification"			,     model="hf-internal-testing/tiny-random-distilbert"			,     framework="tf"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)




 @slow
 @require_torch
 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      pipeline("text-classification"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("This is bad !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "NEGATIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("Birds are a type of animal"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 0.988}]		)




 @slow
 @require_tf
 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      pipeline("text-classification"			,     framework="tf"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("This is bad !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "NEGATIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("Birds are a type of animal"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 0.988}]		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      TextClassificationPipeline(model=_lowerCAmelCase			,     tokenizer=_lowerCAmelCase		)
     return text_classifier, ["HuggingFace is in", "This is another test"]





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      text_classifier.model
     # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
     _lowerCAmelCase				=      "HuggingFace is in"
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase		)

     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}]		)
     self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()		)

     _lowerCAmelCase				=      ["HuggingFace is in ", "Paris is in France"]
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}, {"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}]			,     )
     self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()		)
     self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()		)

     # Forcing to get all results with `top_k=None`
     # This is NOT the legacy format
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase			,     top_k=_lowerCAmelCase		)
     _lowerCAmelCase				=      len(model.config.idalabel.values()		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [[{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}] * N, [{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}] * N]			,     )

     _lowerCAmelCase				=      {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     {"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}			,     )
     self.assertTrue(outputs["label"] in model.config.idalabel.values()		)

     # This might be used a text pair, but tokenizer + pipe interaction
     # makes it hard to understand that it's not using the pair properly
     # https://github.com/huggingface/transformers/issues/17305
     # We disabled this usage instead as it was outputting wrong outputs.
     _lowerCAmelCase				=      [["HuggingFace is in ", "Paris is in France"]]
     with self.assertRaises(_lowerCAmelCase		):
         text_classifier(_lowerCAmelCase		)

     # This used to be valid for doing text pairs
     # We're keeping it working because of backward compatibility
     _lowerCAmelCase				=      text_classifier([[["HuggingFace is in ", "Paris is in France"]]]		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}]			,     )
     self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()		)


