






'''simple docstring'''

import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)



def 				snake_case       (  a_				:			Union[str, Any]						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: int            =				checkpoints.load_tax_checkpoint(a_						)
      UpperCamelCase_					: Optional[int]            =				flatten_dict(a_						)
      return flax_params



def 				snake_case       (  a_				:			Any						)							->					List[Any]:





      """simple docstring"""

      UpperCamelCase_					: int            =				{}

      UpperCamelCase_					: Dict            =				{
          """token_embedder""": """embeddings""",
          """encoder_norm""": """layernorm""",
          """kernel""": """weight""",
          """.out""": """.output""",
          """scale""": """weight""",
          """embedders_0.pos_embedding""": """row_embedder.weight""",
          """embedders_1.pos_embedding""": """column_embedder.weight""",
      }

      UpperCamelCase_					: Union[str, Any]            =				{
          """query""": """attention.query""",
          """key""": """attention.key""",
          """value""": """attention.value""",
          """output.dense""": """output""",
          """encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
          """pre_self_attention_layer_norm""": """self_attention.layer_norm""",
          """pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
          """mlp.""": """mlp.DenseReluDense.""",
          """pre_mlp_layer_norm""": """mlp.layer_norm""",
          """self_attention.o""": """self_attention.attention.o""",
          """decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
          """decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
          """decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
          """decoder.logits_dense.weight""": """decoder.lm_head.weight""",
      }

      for key in flax_dict.keys():
            if "target" in key:
                  # remove the first prefix from the key
                  UpperCamelCase_					: List[Any]            =				""".""".join(key[1:]						)

                  # rename the key
                  for old, new in CONVERSION_MAPPING.items():
                        UpperCamelCase_					: Optional[Any]            =				new_key.replace(a_     , a_						)

                  if "decoder" in new_key:
                        for old, new in DECODER_CONVERSION_MAPPING.items():
                              UpperCamelCase_					: Dict            =				new_key.replace(a_     , a_						)

                  if "layers" in new_key and "decoder" not in new_key:
                        # use regex to replace the layer number
                        UpperCamelCase_					: Optional[Any]            =				re.sub(r"""layers_(\d+)"""     , r"""layer.\1"""     , a_						)
                        UpperCamelCase_					: Optional[int]            =				new_key.replace("""encoder"""     , """encoder.encoder"""						)

                  elif "layers" in new_key and "decoder" in new_key:
                        # use regex to replace the layer number
                        UpperCamelCase_					: Union[str, Any]            =				re.sub(r"""layers_(\d+)"""     , r"""layer.\1"""     , a_						)

                  UpperCamelCase_					: Union[str, Any]            =				flax_dict[key]

      UpperCamelCase_					: Union[str, Any]            =				{}
      # convert converted_dict into torch format
      for key in converted_dict.keys():
            if ("embed_tokens" not in key) and ("embedder" not in key):
                  UpperCamelCase_					: str            =				torch.from_numpy(converted_dict[key].T						)
            else:
                  UpperCamelCase_					: List[str]            =				torch.from_numpy(converted_dict[key]						)

      return converted_torch_dict



def 				snake_case       (  a_				:			Optional[int]     , a_				:			List[Any]     , a_				:			List[str]=False     , a_				:			List[str]=False						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				get_flax_param(a_						)

      if not use_large:
            UpperCamelCase_					: Optional[Any]            =				PixaStructVisionConfig()
            UpperCamelCase_					: Dict            =				PixaStructTextConfig()
      else:
            UpperCamelCase_					: Tuple            =				PixaStructVisionConfig(
                hidden_size=1_536     , d_ff=3_968     , num_attention_heads=24     , num_hidden_layers=18						)
            UpperCamelCase_					: str            =				PixaStructTextConfig(hidden_size=1_536     , d_ff=3_968     , num_heads=24     , num_layers=18						)
      UpperCamelCase_					: Optional[int]            =				PixaStructConfig(
          vision_config=encoder_config.to_dict()     , text_config=decoder_config.to_dict()     , is_vqa=a_						)

      UpperCamelCase_					: Any            =				PixaStructForConditionalGeneration(a_						)

      UpperCamelCase_					: Tuple            =				rename_and_convert_flax_params(a_						)
      model.load_state_dict(a_						)

      UpperCamelCase_					: List[str]            =				AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""						)
      UpperCamelCase_					: Optional[Any]            =				PixaStructImageProcessor()
      UpperCamelCase_					: int            =				PixaStructProcessor(image_processor=a_     , tokenizer=a_						)

      if use_large:
            UpperCamelCase_					: Optional[int]            =				4_096

      UpperCamelCase_					: Union[str, Any]            =				True

      # mkdir if needed
      os.makedirs(a_     , exist_ok=a_						)

      model.save_pretrained(a_						)
      processor.save_pretrained(a_						)

      print("""Model saved in {}""".format(a_						)						)


if __name__ == "__main__":
      UpperCamelCase      					=argparse.ArgumentParser()
      parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument("--use_large", action="store_true", help="Use large model.")
      parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
      UpperCamelCase      					=parser.parse_args()

      convert_pixastruct_original_pytorch_checkpoint_to_hf(
          args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
      )














'''simple docstring'''

import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


UpperCamelCase      					=TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow("", "|", "|"),
    datarow=DataRow("", "|", "|"),
    padding=1,
    with_header_hide=None,
)


UpperCamelCase      					=[]
UpperCamelCase      					=[]

UpperCamelCase      					={"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}

UpperCamelCase      					=[
    {
        "type": "header",
        "text": {
            "type": "plain_text",
            "text": f"🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results",
            "emoji": True,
        },
    }
]

UpperCamelCase      					=0
for log in Path().glob("*.log"):
      UpperCamelCase      					=0
      with open(log, "r") as f:
            for line in f:
                  UpperCamelCase      					=json.loads(line)
                  if line.get("nodeid", "") != "":
                        UpperCamelCase      					=line["nodeid"]
                        if line.get("duration", None) is not None:
                              UpperCamelCase      					=f"{line['duration']:.4f}"
                              if line.get("outcome", "") == "failed":
                                    section_num_failed += 1
                                    failed.append([test, duration, log.name.split("_")[0]])
                                    total_num_failed += 1
      group_info.append([str(log), section_num_failed, failed])
      UpperCamelCase      					=[]
      log.unlink()

UpperCamelCase      					=""
UpperCamelCase      					=[]
if total_num_failed > 0:
      for name, num_failed, failed_tests in group_info:
            if num_failed > 0:
                  if num_failed == 1:
                        message += f"*{name[1:]}: {num_failed} failed test*\n"
                  else:
                        message += f"*{name[1:]}: {num_failed} failed tests*\n"
                  UpperCamelCase      					=[]
                  UpperCamelCase      					={}
                  for test in failed_tests:
                        UpperCamelCase      					=test[0].split("::")
                        UpperCamelCase      					=data[0].split("/")[-1]
                        if data[0] not in filesafailed:
                              UpperCamelCase      					=[data[1:]]
                        else:
                              filesafailed[data[0]] += [data[1:]]
                        failed_table.append(data)

                  UpperCamelCase      					=[test[0] for test in failed_table]
                  UpperCamelCase      					=list(set(files))
                  # Count number of instances in failed_tests
                  UpperCamelCase      					=[]
                  for file in individual_files:
                        table.append([file, len(filesafailed[file])])

                  UpperCamelCase      					=tabulate(
                      table,
                      headers=["Test Location", "Num Failed"],
                      tablefmt=hf_table_format,
                      stralign="right",
                  )
                  message += f"\n```\n{failed_table}\n```"
                  all_filesafailed.append(filesafailed)
      if len(message) > 3000:
            UpperCamelCase      					="Too many failed tests, please see the full report in the Action results."
            UpperCamelCase      					=len(err) + 10
            UpperCamelCase      					=message[: 3000 - offset] + f"\n...\n```\n{err}"
      print(f"### {message}")
else:
      UpperCamelCase      					="No failed tests! 🤗"
      print(f"## {message}")
      payload.append(no_error_payload)

if os.environ.get("TEST_TYPE", "") != "":
      from slack_sdk import WebClient

      UpperCamelCase      					=WebClient(token=os.environ["SLACK_API_TOKEN"])
      if message != "No failed tests! 🤗":
            UpperCamelCase      					={
                "type": "section",
                "text": {
                    "type": "mrkdwn",
                    "text": message,
                },
            }
            payload.append(md_report)
            UpperCamelCase      					={
                "type": "section",
                "text": {
                    "type": "mrkdwn",
                    "text": "*For more details:*",
                },
                "accessory": {
                    "type": "button",
                    "text": {
                        "type": "plain_text",
                        "text": "Check Action results",
                        "emoji": True,
                    },
                    "url": f"https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}",
                },
            }
            payload.append(action_button)
            UpperCamelCase      					={
                "type": "context",
                "elements": [
                    {
                        "type": "plain_text",
                        "text": f"Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}",
                    }
                ],
            }
            payload.append(date_report)
      UpperCamelCase      					=client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
      UpperCamelCase      					=response.data["ts"]
      for failed_file in all_filesafailed:
            for test_location, test_failures in failed_file.items():
                  # Keep only the first instance of the test name
                  UpperCamelCase      					=""
                  for i, row in enumerate(test_failures):
                        if row[0] != test_class:
                              UpperCamelCase      					=row[0]
                        else:
                              UpperCamelCase      					=""

                  UpperCamelCase      					={
                      "type": "section",
                      "text": {
                          "type": "mrkdwn",
                          "text": f"Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```",
                      },
                  }

                  client.chat_postMessage(
                      channel="#accelerate-ci-daily",
                      thread_ts=ts,
                      blocks=[payload],
                  )






