import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
    import torch

    from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer




@require_torch
class 						__snake_case						(			unittest.TestCase		):





      '''simple docstring'''






      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->Any:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased""")
          model.to(_UpperCamelCase)

          # encoder-decoder outputs
          _lowerCamelCase  :		Union[str, Any]			    =    torch.tensor([[17, 9_6208, 10_3471, 2]]).to(_UpperCamelCase)
          _lowerCamelCase  :		List[Any]			    =    torch.tensor(
              [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]).to(_UpperCamelCase)
          _lowerCamelCase  :		List[Any]			    =    model(
              input_ids=_UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	encoder_outputs=_UpperCamelCase     ,	decoder_input_ids=_UpperCamelCase)
          _lowerCamelCase  :		str			    =    output[0]
          _lowerCamelCase  :		str			    =    torch.Size((1, 14, 25_0012))
          self.assertEqual(output_predited_logis.shape     ,	_UpperCamelCase)
          _lowerCamelCase  :		Any			    =    torch.tensor(
              [[[-6.3_9_8_6, -8.2_3_9_1, 1_2.5_1_8_9], [-6.3_2_8_9, -8.0_8_6_4, 1_2.6_2_1_1], [-6.2_4_1_8, -8.0_4_4_5, 1_2.7_9_6_8]]]).to(_UpperCamelCase)
          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]     ,	_UpperCamelCase     ,	atol=1E-4))

          # encoder outputs
          _lowerCamelCase  :		int			    =    model.prophetnet.encoder(_UpperCamelCase)[0]
          _lowerCamelCase  :		Dict			    =    torch.tensor(
              [[[-1.4_2_6_0, -0.7_6_2_8, 0.8_4_5_3], [-1.4_7_1_9, -0.1_3_9_1, 0.7_8_0_7], [-1.7_6_7_8, 0.0_1_1_4, 0.4_6_4_6]]]).to(_UpperCamelCase)
          _lowerCamelCase  :		Union[str, Any]			    =    torch.Size((1, 4, 1024))
          self.assertEqual(encoder_outputs.shape     ,	_UpperCamelCase)
          self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]     ,	_UpperCamelCase     ,	atol=1E-4))

          # decoder outputs
          _lowerCamelCase  :		List[Any]			    =    model.prophetnet.decoder(
              _UpperCamelCase     ,	encoder_hidden_states=_UpperCamelCase     ,	)
          _lowerCamelCase  :		int			    =    decoder_outputs[1].view(1     ,	model.config.ngram     ,	14     ,	-1)
          _lowerCamelCase  :		Dict			    =    model.lm_head(_UpperCamelCase)
          _lowerCamelCase  :		str			    =    predicting_streams_logits[:, 0]
          self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]     ,	_UpperCamelCase     ,	atol=1E-4))



      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int])					->int:


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg""")
          model.to(_UpperCamelCase)

          _lowerCamelCase  :		str			    =    torch.tensor([[17, 9_6208, 10_3471, 2]]).to(_UpperCamelCase)
          _lowerCamelCase  :		Optional[int]			    =    torch.tensor(
              [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]).to(_UpperCamelCase)
          _lowerCamelCase  :		List[str]			    =    model(
              input_ids=_UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	encoder_outputs=_UpperCamelCase     ,	decoder_input_ids=_UpperCamelCase)
          _lowerCamelCase  :		Optional[Any]			    =    output[0]
          _lowerCamelCase  :		List[Any]			    =    torch.Size((1, 14, 25_0012))
          self.assertEqual(output_predited_logis.shape     ,	_UpperCamelCase)
          # compare the actual values for a slice.
          _lowerCamelCase  :		List[str]			    =    torch.tensor(
              [[[-9.2_2_5_3, -9.7_1_7_3, -6.3_5_2_9], [-7.6_7_0_1, -9.0_1_4_5, -1.9_3_8_2], [-8.0_1_9_5, -7.0_0_0_4, -0.1_5_2_3]]]).to(_UpperCamelCase)

          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]     ,	_UpperCamelCase     ,	atol=1E-4))




      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->Dict:


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg""")
          model.to(_UpperCamelCase)
          _lowerCamelCase  :		List[str]			    =    512

          _lowerCamelCase  :		Any			    =    XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg""")

          _lowerCamelCase  :		List[str]			    =    (
              """Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"""
              """ January 14, 2020, according to the official portal of the organization. From that day, users of this"""
              """ system will not be able to receive security updates, which could make their computers vulnerable to"""
              """ cyber attacks."""
          )
          _lowerCamelCase  :		Any			    =    (
              """орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"""
              """ после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"""
              """ этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"""
              """ к кибератакам."""
          )
          _lowerCamelCase  :		int			    =    (
              """根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"""
          )

          _lowerCamelCase  :		str			    =    tokenizer(
              [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]     ,	padding=_UpperCamelCase     ,	max_length=255     ,	return_tensors="""pt""").input_ids
          _lowerCamelCase  :		List[str]			    =    input_ids.to(_UpperCamelCase)

          _lowerCamelCase  :		List[str]			    =    model.generate(
              _UpperCamelCase     ,	num_beams=10     ,	length_penalty=1.0     ,	no_repeat_ngram_size=3     ,	early_stopping=_UpperCamelCase)
          _lowerCamelCase  :		Optional[Any]			    =    [tokenizer.decode(_UpperCamelCase     ,	skip_special_tokens=_UpperCamelCase) for g in summary_ids]
          _lowerCamelCase  :		Optional[int]			    =    """Microsoft to end Windows 7 free support after January 14, 2020"""
          _lowerCamelCase  :		Union[str, Any]			    =    """Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
          _lowerCamelCase  :		Union[str, Any]			    =    """微软打算终止对Windows 7操作系统的免费支持"""
          self.assertListEqual(
              [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]     ,	_UpperCamelCase     ,	)

          _lowerCamelCase  :		List[Any]			    =    model.generate(
              _UpperCamelCase     ,	num_beams=1     ,	length_penalty=1.0     ,	no_repeat_ngram_size=3     ,	early_stopping=_UpperCamelCase)
          _lowerCamelCase  :		Union[str, Any]			    =    [
              tokenizer.convert_ids_to_tokens(_UpperCamelCase     ,	skip_special_tokens=_UpperCamelCase) for g in summary_ids_beama
          ]
          _lowerCamelCase  :		Tuple			    =    """▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7""".split(""" """)
          _lowerCamelCase  :		Union[str, Any]			    =    """▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года""".split(
              """ """)
          _lowerCamelCase  :		str			    =    """微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持""".split(""" """)
          self.assertListEqual(
              [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]     ,	_UpperCamelCase     ,	)





import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Tuple			    =    TaConfig.from_pretrained(__A						)
    _lowerCamelCase  :		List[Any]			    =    FlaxTaForConditionalGeneration(config=__A						)
    _lowerCamelCase  :		Tuple			    =    checkpoints.load_tax_checkpoint(__A						)

    _lowerCamelCase  :		Union[str, Any]			    =    """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

    # Encoder
    for layer_index in range(config.num_layers						):
        _lowerCamelCase  :		Optional[int]			    =    F"""layers_{str(__A						)}"""

        # Self-Attention
        _lowerCamelCase  :		str			    =    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
        _lowerCamelCase  :		int			    =    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
        _lowerCamelCase  :		Optional[Any]			    =    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
        _lowerCamelCase  :		int			    =    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		str			    =    tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

        if split_mlp_wi:
            _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
            _lowerCamelCase  :		Union[str, Any]			    =    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
        else:
            _lowerCamelCase  :		str			    =    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

        _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		Optional[Any]			    =    tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

        # Assigning
        _lowerCamelCase  :		int			    =    tax_attention_key
        _lowerCamelCase  :		Tuple			    =    tax_attention_out
        _lowerCamelCase  :		List[str]			    =    tax_attention_query
        _lowerCamelCase  :		Tuple			    =    tax_attention_value

        _lowerCamelCase  :		Dict			    =    tax_attention_layer_norm

        if split_mlp_wi:
            _lowerCamelCase  :		Dict			    =    tax_mlp_wi_a
            _lowerCamelCase  :		Optional[int]			    =    tax_mlp_wi_a
        else:
            _lowerCamelCase  :		List[str]			    =    tax_mlp_wi

        _lowerCamelCase  :		Tuple			    =    tax_mlp_wo
        _lowerCamelCase  :		Optional[Any]			    =    tax_mlp_layer_norm

    # Only for layer 0:
    _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
    _lowerCamelCase  :		List[Any]			    =    tax_encoder_rel_embedding

    # Assigning
    _lowerCamelCase  :		Union[str, Any]			    =    tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
    _lowerCamelCase  :		int			    =    tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_decoder_layers						):
        _lowerCamelCase  :		Union[str, Any]			    =    F"""layers_{str(__A						)}"""

        # Self-Attention
        _lowerCamelCase  :		List[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
        _lowerCamelCase  :		Dict			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
        _lowerCamelCase  :		Any			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
        _lowerCamelCase  :		Dict			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		List[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
            """scale"""
        ]

        # Encoder-Decoder-Attention
        _lowerCamelCase  :		Union[str, Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
            """kernel"""
        ]
        _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
            """kernel"""
        ]
        _lowerCamelCase  :		Dict			    =    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
            """kernel"""
        ]
        _lowerCamelCase  :		Optional[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
            """kernel"""
        ]

        # Layer Normalization
        _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

        # MLP
        if split_mlp_wi:
            _lowerCamelCase  :		str			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
            _lowerCamelCase  :		List[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
        else:
            _lowerCamelCase  :		Optional[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

        _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

        # Assigning
        _lowerCamelCase  :		Optional[int]			    =    tax_attention_key
        _lowerCamelCase  :		Tuple			    =    tax_attention_out
        _lowerCamelCase  :		Optional[int]			    =    tax_attention_query
        _lowerCamelCase  :		Optional[int]			    =    tax_attention_value

        _lowerCamelCase  :		Optional[Any]			    =    tax_pre_attention_layer_norm

        _lowerCamelCase  :		str			    =    tax_enc_dec_attention_key
        _lowerCamelCase  :		str			    =    tax_enc_dec_attention_out
        _lowerCamelCase  :		Tuple			    =    tax_enc_dec_attention_query
        _lowerCamelCase  :		Dict			    =    tax_enc_dec_attention_value

        _lowerCamelCase  :		List[Any]			    =    tax_cross_layer_norm

        if split_mlp_wi:
            _lowerCamelCase  :		Optional[Any]			    =    tax_mlp_wi_a
            _lowerCamelCase  :		Tuple			    =    tax_mlp_wi_a
        else:
            _lowerCamelCase  :		Any			    =    tax_mlp_wi

        _lowerCamelCase  :		Optional[int]			    =    tax_mlp_wo

        _lowerCamelCase  :		List[str]			    =    txa_mlp_layer_norm

    # Decoder Normalization
    _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
    _lowerCamelCase  :		Union[str, Any]			    =    txa_decoder_norm

    # Only for layer 0:
    _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
    _lowerCamelCase  :		Tuple			    =    tax_decoder_rel_embedding

    # Token Embeddings
    _lowerCamelCase  :		List[str]			    =    tax_model["""target"""]["""token_embedder"""]["""embedding"""]
    _lowerCamelCase  :		List[str]			    =    txa_token_embeddings

    # LM Head (only in v1.1 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        _lowerCamelCase  :		List[Any]			    =    tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

    flax_model.save_pretrained(__A						)
    print("""T5X Model was sucessfully converted!"""						)


if __name__ == "__main__":
    lowerCAmelCase      :				Any     			=argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
    )
    parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
    parser.add_argument(
        "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
    )
    lowerCAmelCase      :				Any     			=parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




