
import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()






def 						__magic_name__			(   __a      :	str	,		__a      :	str	,		__a      :	str						):



     '''simple docstring'''




     def get_masked_lm_array(__a      :	str						):
          UpperCamelCase__				=    f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
          UpperCamelCase__				=    tf.train.load_variable(__a	,		__a						)

          if "kernel" in name:
               UpperCamelCase__				=    array.transpose()

          return torch.from_numpy(__a						)

     def get_encoder_array(__a      :	str						):
          UpperCamelCase__				=    f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
          UpperCamelCase__				=    tf.train.load_variable(__a	,		__a						)

          if "kernel" in name:
               UpperCamelCase__				=    array.transpose()

          return torch.from_numpy(__a						)

     def get_encoder_layer_array(__a      :	int	,		__a      :	str						):
          UpperCamelCase__				=    f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
          UpperCamelCase__				=    tf.train.load_variable(__a	,		__a						)

          if "kernel" in name:
               UpperCamelCase__				=    array.transpose()

          return torch.from_numpy(__a						)

     def get_encoder_attention_layer_array(__a      :	int	,		__a      :	str	,		__a      :	str						):
          UpperCamelCase__				=    f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
          UpperCamelCase__				=    tf.train.load_variable(__a	,		__a						)
          UpperCamelCase__				=    array.reshape(__a						)

          if "kernel" in name:
               UpperCamelCase__				=    array.transpose()

          return torch.from_numpy(__a						)

     print(f"Loading model based on config from {config_path}..."						)
     UpperCamelCase__				=    BertConfig.from_json_file(__a						)
     UpperCamelCase__				=    BertForMaskedLM(__a						)

     # Layers
     for layer_index in range(0	,		config.num_hidden_layers						):
          UpperCamelCase__				=    model.bert.encoder.layer[layer_index]

          # Self-attention
          UpperCamelCase__				=    layer.attention.self

          UpperCamelCase__				=    get_encoder_attention_layer_array(
              __a	,		"""_query_dense/kernel"""	,		self_attn.query.weight.data.shape						)
          UpperCamelCase__				=    get_encoder_attention_layer_array(
              __a	,		"""_query_dense/bias"""	,		self_attn.query.bias.data.shape						)
          UpperCamelCase__				=    get_encoder_attention_layer_array(
              __a	,		"""_key_dense/kernel"""	,		self_attn.key.weight.data.shape						)
          UpperCamelCase__				=    get_encoder_attention_layer_array(
              __a	,		"""_key_dense/bias"""	,		self_attn.key.bias.data.shape						)
          UpperCamelCase__				=    get_encoder_attention_layer_array(
              __a	,		"""_value_dense/kernel"""	,		self_attn.value.weight.data.shape						)
          UpperCamelCase__				=    get_encoder_attention_layer_array(
              __a	,		"""_value_dense/bias"""	,		self_attn.value.bias.data.shape						)

          # Self-attention Output
          UpperCamelCase__				=    layer.attention.output

          UpperCamelCase__				=    get_encoder_attention_layer_array(
              __a	,		"""_output_dense/kernel"""	,		self_output.dense.weight.data.shape						)
          UpperCamelCase__				=    get_encoder_attention_layer_array(
              __a	,		"""_output_dense/bias"""	,		self_output.dense.bias.data.shape						)

          UpperCamelCase__				=    get_encoder_layer_array(__a	,		"""_attention_layer_norm/gamma"""						)
          UpperCamelCase__				=    get_encoder_layer_array(__a	,		"""_attention_layer_norm/beta"""						)

          # Intermediate
          UpperCamelCase__				=    layer.intermediate

          UpperCamelCase__				=    get_encoder_layer_array(__a	,		"""_intermediate_dense/kernel"""						)
          UpperCamelCase__				=    get_encoder_layer_array(__a	,		"""_intermediate_dense/bias"""						)

          # Output
          UpperCamelCase__				=    layer.output

          UpperCamelCase__				=    get_encoder_layer_array(__a	,		"""_output_dense/kernel"""						)
          UpperCamelCase__				=    get_encoder_layer_array(__a	,		"""_output_dense/bias"""						)

          UpperCamelCase__				=    get_encoder_layer_array(__a	,		"""_output_layer_norm/gamma"""						)
          UpperCamelCase__				=    get_encoder_layer_array(__a	,		"""_output_layer_norm/beta"""						)

     # Embeddings
     UpperCamelCase__				=    get_encoder_array("""_position_embedding_layer/embeddings"""						)
     UpperCamelCase__				=    get_encoder_array("""_type_embedding_layer/embeddings"""						)
     UpperCamelCase__				=    get_encoder_array("""_embedding_norm_layer/gamma"""						)
     UpperCamelCase__				=    get_encoder_array("""_embedding_norm_layer/beta"""						)

     # LM Head
     UpperCamelCase__				=    model.cls.predictions.transform

     UpperCamelCase__				=    get_masked_lm_array("""dense/kernel"""						)
     UpperCamelCase__				=    get_masked_lm_array("""dense/bias"""						)

     UpperCamelCase__				=    get_masked_lm_array("""layer_norm/gamma"""						)
     UpperCamelCase__				=    get_masked_lm_array("""layer_norm/beta"""						)

     UpperCamelCase__				=    get_masked_lm_array("""embedding_table"""						)

     # Pooling
     UpperCamelCase__				=    BertPooler(config=__a						)
     UpperCamelCase__				=    get_encoder_array("""_pooler_layer/kernel"""						)
     UpperCamelCase__				=    get_encoder_array("""_pooler_layer/bias"""						)

     # Export final model
     model.save_pretrained(__a						)

     # Integration test - should load without any errors ;)
     UpperCamelCase__				=    BertForMaskedLM.from_pretrained(__a						)
     print(new_model.eval()						)

     print("""Model conversion was done sucessfully!"""						)


if __name__ == "__main__":
    lowerCamelCase_						     =					argparse.ArgumentParser()
    parser.add_argument(
        '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow Token Dropping 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.''',
    )
    lowerCamelCase_						     =					parser.parse_args()
    convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
    import torch

    from transformers import AutoModelForCausalLM




@require_torch
class      __A(    unittest.TestCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.decode(greedy_ids[0]						)

       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)
       # The greedy text should be printed to stdout, except for the final "\n" in the streamer
       UpperCamelCase__				=    cs.out[:-1]

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.decode(greedy_ids[0]						)

       UpperCamelCase__				=    TextIteratorStreamer(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
       UpperCamelCase__				=    Thread(target=model.generate	,      kwargs=SCREAMING_SNAKE_CASE_						)
       thread.start()
       UpperCamelCase__				=    """"""
       for new_text in streamer:
            streamer_text += new_text

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    greedy_ids[:, input_ids.shape[1] :]
       UpperCamelCase__				=    tokenizer.decode(new_greedy_ids[0]						)

       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_	,      skip_prompt=SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)
       # The greedy text should be printed to stdout, except for the final "\n" in the streamer
       UpperCamelCase__				=    cs.out[:-1]

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
       # with actual models -- the dummy models' tokenizers are not aligned with their models, and
       # `skip_special_tokens=True` has no effect on them
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""distilgpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""distilgpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    torch.ones((1, 5)	,      device=SCREAMING_SNAKE_CASE_						).long() * model.config.bos_token_id
       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_	,      skip_special_tokens=SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=1	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)

       # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
       # re-tokenized, must only contain one token
       UpperCamelCase__				=    cs.out[:-1]  # Remove the final "\n"
       UpperCamelCase__				=    tokenizer(SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						)
       self.assertEqual(streamer_text_tokenized.input_ids.shape	,      (1, 1)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    TextIteratorStreamer(SCREAMING_SNAKE_CASE_	,      timeout=0.001						)
       UpperCamelCase__				=    {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
       UpperCamelCase__				=    Thread(target=model.generate	,      kwargs=SCREAMING_SNAKE_CASE_						)
       thread.start()

       # The streamer will timeout after 0.001 seconds, so an exception will be raised
       with self.assertRaises(SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    """"""
            for new_text in streamer:
                 streamer_text += new_text





