





import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


a_    :				Union[str, Any]					=			-1 if torch_device == 'cpu' else 0



@is_pipeline_test
class  SCREAMING_SNAKE_CASE_  ( unittest.TestCase			):


  """simple docstring"""





  _a								=							MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  _a								=							TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





  def 			__A      (  self	,  A	,  A	,  A						)		->   Union[str, Any]:





       '''simple docstring'''







       __magic_name__				=       SummarizationPipeline(model=A	,  tokenizer=A						)
       return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]





  def 			__A      (  self	,  A	,  A						)		->   str:





       '''simple docstring'''







       __magic_name__				=       summarizer.model

       __magic_name__				=       summarizer('''(CNN)The Palestinian Authority officially became'''						)
       self.assertEqual(A	,  [{'''summary_text''': ANY(A						)}]						)

       __magic_name__				=       summarizer(
           '''(CNN)The Palestinian Authority officially became '''	,  num_beams=2	,  min_length=2	,  max_length=5	,  )
       self.assertEqual(A	,  [{'''summary_text''': ANY(A						)}]						)

       # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
       __magic_name__				=       [
           '''SwitchTransformersConfig''',
           '''T5Config''',
           '''LongT5Config''',
           '''LEDConfig''',
           '''PegasusXConfig''',
           '''FSMTConfig''',
           '''M2M100Config''',
           '''ProphetNetConfig''',  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
       ]
       if model.config.__class__.__name__ not in model_can_handle_longer_seq:
            # Too long and exception is expected.
            # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
            # the embedding layer.
            if not (
                isinstance(A	,  A						)
                and get_gpu_count() > 0
                and len(summarizer.model.trainable_weights						) > 0
            ):
                 with self.assertRaises(A						):
                      __magic_name__				=       summarizer('''This ''' * 10_00						)
       __magic_name__				=       summarizer('''This ''' * 10_00	,  truncation=TruncationStrategy.ONLY_FIRST						)





  @require_torch
  def 			__A      (  self						)		->   str:





       '''simple docstring'''







       __magic_name__				=       pipeline(task='''summarization'''	,  model='''sshleifer/tiny-mbart'''	,  framework='''pt'''						)
       __magic_name__				=       summarizer('''This is a small test'''						)
       self.assertEqual(
           A	,  [
               {
                   '''summary_text''': '''เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'''
               }
           ]	,  )





  @require_tf
  def 			__A      (  self						)		->   Optional[Any]:





       '''simple docstring'''







       __magic_name__				=       pipeline(task='''summarization'''	,  model='''sshleifer/tiny-mbart'''	,  framework='''tf'''						)
       __magic_name__				=       summarizer('''This is a small test'''						)
       self.assertEqual(
           A	,  [
               {
                   '''summary_text''': '''เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'''
               }
           ]	,  )







  @require_torch
  @slow
  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       pipeline(task='''summarization'''	,  device=A						)
       __magic_name__				=       (
           ''' (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on'''
           ''' Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The'''
           ''' formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based.'''
           ''' The Palestinians signed the ICC\'s founding Rome Statute in January, when they also accepted its'''
           ''' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'''
           ''' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'''
           ''' situation in Palestinian territories, paving the way for possible war crimes investigations against'''
           ''' Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and'''
           ''' the United States, neither of which is an ICC member, opposed the Palestinians\' efforts to join the'''
           ''' body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday\'s ceremony, said it was a'''
           ''' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'''
           ''' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'''
           ''' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'''
           ''' Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the'''
           ''' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'''
           ''' acquires all the rights as well as responsibilities that come with being a State Party to the Statute.'''
           ''' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'''
           ''' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'''
           ''' immediately end their pressure, and countries that support universal acceptance of the court\'s treaty'''
           ''' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'''
           ''' group. \"What\'s objectionable is the attempts to undermine international justice, not Palestine\'s'''
           ''' decision to join a treaty to which over 100 countries around the world are members." In January, when'''
           ''' the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an'''
           ''' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'''
           ''' disagreed with the court\'s decision. \"As we have said repeatedly, we do not believe that Palestine is a'''
           ''' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'''
           ''' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'''
           ''' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'''
           ''' it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the'''
           ''' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'''
           ''' court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou'''
           ''' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'''
           ''' between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry'''
           ''' will include alleged war crimes committed since June. The International Criminal Court was set up in'''
           ''' 2002 to prosecute genocide, crimes against humanity and war crimes. CNN\'s Vasco Cotovio, Kareem Khadder'''
           ''' and Faith Karimi contributed to this report.'''
       )
       __magic_name__				=       (
           ''' The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives'''
           ''' the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States'''
           ''' opposed the Palestinians\' efforts to join the court . Rights group Human Rights Watch welcomes the move,'''
           ''' says governments seeking to penalize Palestine should end pressure .'''
       )
       __magic_name__				=       summarizer(A						)
       self.assertEqual(result[0]['''summary_text''']	,  A						)






# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Optional[int] ,     snake_case_		:						int ,     snake_case_		:						int=0						):
     # Format the message.
     if name is None:
          __magic_name__				=       None
     else:
          __magic_name__				=       '''.''' * max(0 ,     spaces - 2						) + '''# {:''' + str(50 - spaces						) + '''s}'''
          __magic_name__				=       fmt.format(snake_case_						)

     # Print and recurse (if needed).
     if isinstance(snake_case_ ,     snake_case_						):
          if msg is not None:
               print(snake_case_						)
          for k in val.keys():
               recursive_print(snake_case_ ,     val[k] ,     spaces + 2						)
     elif isinstance(snake_case_ ,     torch.Tensor						):
          print(snake_case_ ,     ''':''' ,     val.size()						)
     else:
          print(snake_case_ ,     ''':''' ,     snake_case_						)




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						int ,     snake_case_		:						Optional[int] ,     snake_case_		:						Any ,     snake_case_		:						Tuple ,     snake_case_		:						Tuple						):
     # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
     # for compatibility with later versions of NVIDIA Megatron-LM.
     # The inverse operation is performed inside Megatron-LM to read checkpoints:
     # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
     # If param is the weight tensor of the self-attention block, the returned tensor
     # will have to be transposed one more time to be read by HuggingFace BERT.
     __magic_name__				=       param.size()
     if checkpoint_version == 1.0:
          # version 1.0 stores [num_heads * hidden_size * num_splits, :]
          __magic_name__				=       (num_heads, hidden_size, num_splits) + input_shape[1:]
          __magic_name__				=       param.view(*snake_case_						)
          __magic_name__				=       param.transpose(0 ,     2						)
          __magic_name__				=       param.transpose(1 ,     2						).contiguous()
     elif checkpoint_version >= 2.0:
          # other versions store [num_heads * num_splits * hidden_size, :]
          __magic_name__				=       (num_heads, num_splits, hidden_size) + input_shape[1:]
          __magic_name__				=       param.view(*snake_case_						)
          __magic_name__				=       param.transpose(0 ,     1						).contiguous()
     __magic_name__				=       param.view(*snake_case_						)
     return param




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						int ,     snake_case_		:						Optional[int] ,     snake_case_		:						Union[str, Any]						):
     # The converted output model.
     __magic_name__				=       {}

     # old versions did not store training args
     __magic_name__				=       input_state_dict.get('''args''' ,     snake_case_						)
     if ds_args is not None:
          # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
          # from pprint import pprint
          # pprint(vars(ds_args))

          __magic_name__				=       ds_args.tokenizer_type
          __magic_name__				=       ds_args.padded_vocab_size
          __magic_name__				=       ds_args.max_position_embeddings
          __magic_name__				=       ds_args.hidden_size
          __magic_name__				=       ds_args.num_layers
          __magic_name__				=       ds_args.num_attention_heads
          __magic_name__				=       ds_args.ffn_hidden_size if '''ffn_hidden_size''' in ds_args else 4 * ds_args.hidden_size
          # pprint(config)

     # The number of heads.
     __magic_name__				=       config.num_attention_heads
     # The hidden_size per head.
     __magic_name__				=       config.hidden_size // heads
     # Megatron-LM checkpoint version
     if "checkpoint_version" in input_state_dict.keys():
          __magic_name__				=       input_state_dict['''checkpoint_version''']
     else:
          __magic_name__				=       0.0

     # The model.
     __magic_name__				=       input_state_dict['''model''']
     # The language model.
     __magic_name__				=       model['''language_model''']
     # The embeddings.
     __magic_name__				=       lm['''embedding''']

     # The word embeddings.
     __magic_name__				=       embeddings['''word_embeddings''']['''weight''']
     # Truncate the embedding table to vocab_size rows.
     __magic_name__				=       word_embeddings[: config.vocab_size, :]
     # Store the word embeddings.
     __magic_name__				=       word_embeddings

     # The position embeddings.
     __magic_name__				=       embeddings['''position_embeddings''']['''weight''']
     assert pos_embeddings.size(0						) == config.max_position_embeddings and pos_embeddings.size(1						) == config.hidden_size
     # Store the position embeddings.
     __magic_name__				=       pos_embeddings

     # The token-type embeddings.
     __magic_name__				=       embeddings['''tokentype_embeddings''']['''weight''']
     # Store the position embeddings.
     __magic_name__				=       tokentype_embeddings

     # The transformer.
     __magic_name__				=       lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

     # The regex to extract layer names.
     __magic_name__				=       re.compile(r'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'''						)

     # The simple map of names for "automated" rules.
     __magic_name__				=       {
         '''attention.dense''': '''.attention.output.dense.''',
         '''self_attention.dense''': '''.attention.output.dense.''',
         '''mlp.dense_h_to_4h''': '''.intermediate.dense.''',
         '''mlp.dense_4h_to_h''': '''.output.dense.''',
     }

     # Keep track of the attention/query/value tensor.
     __magic_name__				=       None

     # Extract the layers.
     for key, val in transformer.items():
          # Match the name.
          __magic_name__				=       layer_re.match(snake_case_						)

          # Stop if that's not a layer
          if m is None:
               break

          # The index of the layer.
          __magic_name__				=       int(m.group(1						)						)
          # The name of the operation.
          __magic_name__				=       m.group(2						)
          # Is it a weight or a bias?
          __magic_name__				=       m.group(3						)

          # The name of the layer.
          __magic_name__				=       f'bert.encoder.layer.{layer_idx}'

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith('''layernorm'''						):
               __magic_name__				=       '''attention.ln''' if op_name.startswith('''input'''						) else '''ln'''
               __magic_name__				=       val

          # Transpose the QKV matrix.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "weight":
               # Make sure the QKV pointer is nil.
               assert attention_qkv_weight is None, ""

               __magic_name__				=       fix_query_key_value_ordering(snake_case_ ,     snake_case_ ,     3 ,     snake_case_ ,     snake_case_						)
               # Store the tensor as we need the bias as well to interleave QKV and biases.
               __magic_name__				=       out_val

          # Transpose the bias.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "bias":
               # Make sure we read the weight tensor.
               assert attention_qkv_weight is not None, ""

               # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
               __magic_name__				=       attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
               __magic_name__				=       attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
               __magic_name__				=       attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

               __magic_name__				=       fix_query_key_value_ordering(snake_case_ ,     snake_case_ ,     3 ,     snake_case_ ,     snake_case_						)
               # Split the bias.
               __magic_name__				=       out_val[0 * config.hidden_size : 1 * config.hidden_size]
               __magic_name__				=       out_val[1 * config.hidden_size : 2 * config.hidden_size]
               __magic_name__				=       out_val[2 * config.hidden_size : 3 * config.hidden_size]

               # Store.
               __magic_name__				=       q
               __magic_name__				=       q_bias
               __magic_name__				=       k
               __magic_name__				=       k_bias
               __magic_name__				=       v
               __magic_name__				=       v_bias

               # Clear the stored tensor.
               __magic_name__				=       None

          # Copy weights and biases as is.
          elif weight_or_bias in ["weight", "bias"]:
               __magic_name__				=       megatron_to_transformers[op_name]
               __magic_name__				=       val

    # The final layernorm.
     __magic_name__				=       transformer['''final_layernorm.weight''']
     __magic_name__				=       transformer['''final_layernorm.bias''']

     # The pooler.
     __magic_name__				=       lm['''pooler''']

     # Store the matrix and the bias.
     __magic_name__				=       pooler['''dense.weight''']
     __magic_name__				=       pooler['''dense.bias''']

     # The LM head from Megatron (for RACE).
     __magic_name__				=       model['''lm_head''']

     # The transform matrix.
     __magic_name__				=       lm_head['''dense.weight''']
     __magic_name__				=       lm_head['''dense.bias''']

     # The transform LN.
     __magic_name__				=       lm_head['''layernorm.weight''']
     __magic_name__				=       lm_head['''layernorm.bias''']

     # For the decoder, we replicate the weights.
     __magic_name__				=       word_embeddings
     __magic_name__				=       lm_head['''bias''']

     # The classifier from Megatron (for MLNI).
     __magic_name__				=       model['''binary_head''']

     # Store the classifier.
     __magic_name__				=       binary_head['''weight''']
     __magic_name__				=       binary_head['''bias''']

     # It should be done!
     return output_state_dict




def   _SCREAMING_SNAKE_CASE      (	):
     # Create the argument parser.
     __magic_name__				=       argparse.ArgumentParser()
     parser.add_argument('''--print-checkpoint-structure''' ,     action='''store_true'''						)
     parser.add_argument('''path_to_checkpoint''' ,     type=snake_case_ ,     help='''Path to the ZIP file containing the checkpoint'''						)
     parser.add_argument(
         '''--config_file''' ,     default='''''' ,     type=snake_case_ ,     help='''An optional config json file describing the pre-trained model.''' ,     )
     __magic_name__				=       parser.parse_args()

     # Extract the basename.
     __magic_name__				=       os.path.dirname(args.path_to_checkpoint						)

     # Load the model.
     # the .zip is very optional, let's keep it for backward compatibility
     print(f'Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"'						)
     if args.path_to_checkpoint.endswith('''.zip'''						):
          with zipfile.ZipFile(args.path_to_checkpoint ,     '''r'''						) as checkpoint:
               with checkpoint.open('''release/mp_rank_00/model_optim_rng.pt'''						) as pytorch_dict:
                    __magic_name__				=       torch.load(snake_case_ ,     map_location='''cpu'''						)
     else:
          __magic_name__				=       torch.load(args.path_to_checkpoint ,     map_location='''cpu'''						)

     if args.config_file == "":
          # Default config of megatron-bert 345m
          __magic_name__				=       MegatronBertConfig()

          # different megatron-bert-*-345m models have different vocab sizes, so override the default
          # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
          __magic_name__				=       input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
     else:
          __magic_name__				=       MegatronBertConfig.from_json_file(args.config_file						)

     # Convert.
     print('''Converting'''						)
     __magic_name__				=       convert_megatron_checkpoint(snake_case_ ,     snake_case_ ,     snake_case_						)

     # Print the structure of converted state dict.
     if args.print_checkpoint_structure:
          recursive_print(snake_case_ ,     snake_case_						)

     # Store the config to file.
     print('''Saving config'''						)
     config.save_pretrained(snake_case_						)

     # Store the state_dict to file.
     __magic_name__				=       os.path.join(snake_case_ ,     '''pytorch_model.bin'''						)
     print(f'Saving checkpoint to "{output_checkpoint_file}"'						)
     torch.save(snake_case_ ,     snake_case_						)


####################################################################################################

if __name__ == "__main__":
    main()

####################################################################################################